path: root/scripts/create_squad_features.py

    #!/usr/bin/env python
    # coding: utf-8

    # auther = 'liuzhiyong'
    # date = 20201204


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import datetime
import threading
import time
from flask import Flask, abort, request, jsonify
from concurrent.futures import ThreadPoolExecutor

import collections
import math
import os
import random
import modeling
import optimization
import tokenization
import six
import tensorflow as tf
import sys
import requests

from global_setting import *

version_2_with_negative = True

def get_squad_feature_result(title,text,tokenizer,question, url):

    def make_json(title, text, question):
        res = {}
        res['data'] = []
        data_inside = {}

        data_inside['title'] = title
        data_inside['paragraphs'] = []
        paragraphs_inside = {}
        paragraphs_inside['context'] = text
        paragraphs_inside['qas'] = []
        for ques in question:
            qas_inside = {}
            qas_inside['answers'] = []

            answer_inside = {}

            qas_inside['id'] = ques
            qas_inside['question'] = ques
            qas_inside['answers'].append(answer_inside.copy())
            paragraphs_inside['qas'].append(qas_inside.copy())
        data_inside['paragraphs'].append(paragraphs_inside.copy())

        res['data'].append(data_inside.copy())
        return json.dumps(res)


    def _compute_softmax(scores):
        """Compute softmax probability over raw logits."""
        if not scores:
            return []

        max_score = None
        for score in scores:
            if max_score is None or score > max_score:
                max_score = score

        exp_scores = []
        total_sum = 0.0
        for score in scores:
            x = math.exp(score - max_score)
            exp_scores.append(x)
            total_sum += x

        probs = []
        for score in exp_scores:
            probs.append(score / total_sum)
        return probs

    def get_final_text(pred_text, orig_text, do_lower_case):
  
        def _strip_spaces(text):
            ns_chars = []
            ns_to_s_map = collections.OrderedDict()
            for (i, c) in enumerate(text):
                if c == " ":
                    continue
                ns_to_s_map[len(ns_chars)] = i
                ns_chars.append(c)
            ns_text = "".join(ns_chars)
            return (ns_text, ns_to_s_map)

        # We first tokenize `orig_text`, strip whitespace from the result
        # and `pred_text`, and check if they are the same length. If they are
        # NOT the same length, the heuristic has failed. If they are the same
        # length, we assume the characters are one-to-one aligned.
        tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)

        tok_text = " ".join(tokenizer.tokenize(orig_text))

        start_position = tok_text.find(pred_text)
        if start_position == -1:
            if 0:
                tf.logging.info(
                    "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
            return orig_text
        end_position = start_position + len(pred_text) - 1

        (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
        (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)

        if len(orig_ns_text) != len(tok_ns_text):
            if 0:
                tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
                                orig_ns_text, tok_ns_text)
            return orig_text

        # We then project the characters in `pred_text` back to `orig_text` using
        # the character-to-character alignment.
        tok_s_to_ns_map = {}
        for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
            tok_s_to_ns_map[tok_index] = i

        orig_start_position = None
        if start_position in tok_s_to_ns_map:
            ns_start_position = tok_s_to_ns_map[start_position]
            if ns_start_position in orig_ns_to_s_map:
                orig_start_position = orig_ns_to_s_map[ns_start_position]

        if orig_start_position is None:
            if 0:
                tf.logging.info("Couldn't map start position")
            return orig_text

        orig_end_position = None
        if end_position in tok_s_to_ns_map:
            ns_end_position = tok_s_to_ns_map[end_position]
            if ns_end_position in orig_ns_to_s_map:
                orig_end_position = orig_ns_to_s_map[ns_end_position]

        if orig_end_position is None:
            if 0:
                tf.logging.info("Couldn't map end position")
            return orig_text

        output_text = orig_text[orig_start_position:(orig_end_position + 1)]
        return output_text

    def _get_best_indexes(logits, n_best_size):
      
        index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)

        best_indexes = []
        for i in range(len(index_and_score)):
            if i >= n_best_size:
                break
            best_indexes.append(index_and_score[i][0])
        return best_indexes

    RawResult = collections.namedtuple("RawResult", ["unique_id", "start_logits", "end_logits"])

    def write_predictions(all_examples, all_features, all_results, n_best_size,
                        max_answer_length, do_lower_case):
        """Write final predictions to the json file and log-odds of null if needed."""

        example_index_to_features = collections.defaultdict(list)
        for feature in all_features:
            example_index_to_features[feature.example_index].append(feature)

        unique_id_to_result = {}
        for result in all_results:
            unique_id_to_result[result.unique_id] = result

        _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
            "PrelimPrediction",
            ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])

        all_predictions = collections.OrderedDict()
        all_nbest_json = collections.OrderedDict()
        scores_diff_json = collections.OrderedDict()

        for (example_index, example) in enumerate(all_examples):
            features = example_index_to_features[example_index]

            prelim_predictions = []
            # keep track of the minimum score of null start+end of position 0
            score_null = 1000000  # large and positive
            min_null_feature_index = 0  # the paragraph slice with min mull score
            null_start_logit = 0  # the start logit at the slice with min null score
            null_end_logit = 0  # the end logit at the slice with min null score
            for (feature_index, feature) in enumerate(features):
                result = unique_id_to_result[feature.unique_id]
                start_indexes = _get_best_indexes(result.start_logits, n_best_size)
                end_indexes = _get_best_indexes(result.end_logits, n_best_size)
                # if we could have irrelevant answers, get the min score of irrelevant
                if version_2_with_negative:
                    feature_null_score = result.start_logits[0] + result.end_logits[0]
                    if feature_null_score < score_null:
                        score_null = feature_null_score
                        min_null_feature_index = feature_index
                        null_start_logit = result.start_logits[0]
                        null_end_logit = result.end_logits[0]

                for start_index in start_indexes:
                    for end_index in end_indexes:
                        # We could hypothetically create invalid predictions, e.g., predict
                        # that the start of the span is in the question. We throw out all
                        # invalid predictions.
                        if start_index >= len(feature.tokens):
                            continue
                        if end_index >= len(feature.tokens):
                            continue
                        if start_index not in feature.token_to_orig_map:
                            continue
                        if end_index not in feature.token_to_orig_map:
                            continue
                        if not feature.token_is_max_context.get(start_index, False):
                            continue
                        if end_index < start_index:
                            continue
                        length = end_index - start_index + 1
                        if length > max_answer_length:
                            continue
                        prelim_predictions.append(
                            _PrelimPrediction(
                                feature_index=feature_index,
                                start_index=start_index,
                                end_index=end_index,
                                start_logit=result.start_logits[start_index],
                                end_logit=result.end_logits[end_index]))

            if version_2_with_negative:
                prelim_predictions.append(
                _PrelimPrediction(
                    feature_index=min_null_feature_index,
                    start_index=0,
                    end_index=0,
                    start_logit=null_start_logit,
                    end_logit=null_end_logit))
            prelim_predictions = sorted(
                    prelim_predictions,
                    key=lambda x: (x.start_logit + x.end_logit),
                    reverse=True)

            _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
                    "NbestPrediction", ["text", "start_logit", "end_logit"])

            seen_predictions = {}
            nbest = []
            for pred in prelim_predictions:
                if len(nbest) >= n_best_size:
                    break
                feature = features[pred.feature_index]
                if pred.start_index > 0:  # this is a non-null prediction
                    tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
                    orig_doc_start = feature.token_to_orig_map[pred.start_index]
                    orig_doc_end = feature.token_to_orig_map[pred.end_index]
                    orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
                    tok_text = " ".join(tok_tokens)

                    # De-tokenize WordPieces that have been split off.
                    tok_text = tok_text.replace(" ##", "")
                    tok_text = tok_text.replace("##", "")

                    # Clean whitespace
                    tok_text = tok_text.strip()
                    tok_text = " ".join(tok_text.split())
                    orig_text = " ".join(orig_tokens)

                    final_text = get_final_text(tok_text, orig_text, do_lower_case)
                    if final_text in seen_predictions:
                        continue

                    seen_predictions[final_text] = True
                else:
                    final_text = ""
                    seen_predictions[final_text] = True

                nbest.append(
                        _NbestPrediction(
                                text=final_text,
                                start_logit=pred.start_logit,
                                end_logit=pred.end_logit))

            # if we didn't inlude the empty option in the n-best, inlcude it
            if version_2_with_negative:
                if "" not in seen_predictions:
                    nbest.append(
                        _NbestPrediction(
                            text="", start_logit=null_start_logit,
                            end_logit=null_end_logit))

            # In very rare edge cases we could have no valid predictions. So we
            # just create a nonce prediction in this case to avoid failure.
            if not nbest:
                nbest.append(
                        _NbestPrediction(text="", start_logit=0.0, end_logit=0.0))

            assert len(nbest) >= 1

            total_scores = []
            best_non_null_entry = None
            for entry in nbest:
                total_scores.append(entry.start_logit + entry.end_logit)
                if not best_non_null_entry:
                    if entry.text:
                        best_non_null_entry = entry

            probs = _compute_softmax(total_scores)

            nbest_json = []
            for (i, entry) in enumerate(nbest):
                output = collections.OrderedDict()
                output["text"] = entry.text
                output["probability"] = probs[i]
                output["start_logit"] = entry.start_logit
                output["end_logit"] = entry.end_logit
                nbest_json.append(output)

            assert len(nbest_json) >= 1

            if not version_2_with_negative:
                all_predictions[example.qas_id] = nbest_json[0]["text"]
            else:
                # predict "" iff the null score - the score of best non-null > threshold
                score_diff = score_null - best_non_null_entry.start_logit - (
                    best_non_null_entry.end_logit)
                scores_diff_json[example.qas_id] = score_diff
                if score_diff > 0:
                    all_predictions[example.qas_id] = ""
                else:
                    all_predictions[example.qas_id] = best_non_null_entry.text

            all_nbest_json[example.qas_id] = nbest_json
        return all_predictions


    def create_int_feature(values):

        feature = tf.train.Feature(
            int64_list=tf.train.Int64List(value=list(values)))
        return feature


    class InputFeatures(object):
        """A single set of features of data."""

        def __init__(self,
                    unique_id,
                    example_index,
                    doc_span_index,
                    tokens,
                    token_to_orig_map,
                    token_is_max_context,
                    input_ids,
                    input_mask,
                    segment_ids,
                    start_position=None,
                    end_position=None,
                    is_impossible=None):
            self.unique_id = unique_id
            self.example_index = example_index
            self.doc_span_index = doc_span_index
            self.tokens = tokens
            self.token_to_orig_map = token_to_orig_map
            self.token_is_max_context = token_is_max_context
            self.input_ids = input_ids
            self.input_mask = input_mask
            self.segment_ids = segment_ids
            self.start_position = start_position
            self.end_position = end_position
            self.is_impossible = is_impossible

    def _check_is_max_context(doc_spans, cur_span_index, position):
        """Check if this is the 'max context' doc span for the token."""

        # Because of the sliding window approach taken to scoring documents, a single
        # token can appear in multiple documents. E.g.
        #  Doc: the man went to the store and bought a gallon of milk
        #  Span A: the man went to the
        #  Span B: to the store and bought
        #  Span C: and bought a gallon of
        #  ...
        #
        # Now the word 'bought' will have two scores from spans B and C. We only
        # want to consider the score with "maximum context", which we define as
        # the *minimum* of its left and right context (the *sum* of left and
        # right context will always be the same, of course).
        #
        # In the example the maximum context for 'bought' would be span C since
        # it has 1 left context and 3 right context, while span B has 4 left context
        # and 0 right context.
        best_score = None
        best_span_index = None
        for (span_index, doc_span) in enumerate(doc_spans):
            end = doc_span.start + doc_span.length - 1
            if position < doc_span.start:
                continue
            if position > end:
                continue
            num_left_context = position - doc_span.start
            num_right_context = end - position
            score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
            if best_score is None or score > best_score:
                best_score = score
                best_span_index = span_index

        return cur_span_index == best_span_index

    def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                    doc_stride, max_query_length, is_training):
        """Loads a data file into a list of `InputBatch`s."""

        unique_id = 1000000000
        result = []

        for (example_index, example) in enumerate(examples):
            query_tokens = tokenizer.tokenize(example.question_text)

        if len(query_tokens) > max_query_length:
            query_tokens = query_tokens[0:max_query_length]

        tok_to_orig_index = []
        orig_to_tok_index = []
        all_doc_tokens = []
        for (i, token) in enumerate(example.doc_tokens):
            orig_to_tok_index.append(len(all_doc_tokens))
            sub_tokens = tokenizer.tokenize(token)
            for sub_token in sub_tokens:
                tok_to_orig_index.append(i)
                all_doc_tokens.append(sub_token)

            tok_start_position = None
            tok_end_position = None
            if is_training and example.is_impossible:
                tok_start_position = -1
                tok_end_position = -1
            if is_training and not example.is_impossible:
                tok_start_position = orig_to_tok_index[example.start_position]
                if example.end_position < len(example.doc_tokens) - 1:
                    tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
                else:
                    tok_end_position = len(all_doc_tokens) - 1
                (tok_start_position, tok_end_position) = _improve_answer_span(
                    all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
                    example.orig_answer_text)

            # The -3 accounts for [CLS], [SEP] and [SEP]
            max_tokens_for_doc = max_seq_length - len(query_tokens) - 3

            # We can have documents that are longer than the maximum sequence length.
            # To deal with this we do a sliding window approach, where we take chunks
            # of the up to our max length with a stride of `doc_stride`.
            _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
                "DocSpan", ["start", "length"])
            doc_spans = []
            start_offset = 0
            while start_offset < len(all_doc_tokens):
                length = len(all_doc_tokens) - start_offset
                if length > max_tokens_for_doc:
                    length = max_tokens_for_doc
                doc_spans.append(_DocSpan(start=start_offset, length=length))
                if start_offset + length == len(all_doc_tokens):
                    break
                start_offset += min(length, doc_stride)

            for (doc_span_index, doc_span) in enumerate(doc_spans):
                tokens = []
                token_to_orig_map = {}
                token_is_max_context = {}
                segment_ids = []
                tokens.append("[CLS]")
                segment_ids.append(0)
                for token in query_tokens:
                    tokens.append(token)
                    segment_ids.append(0)
                tokens.append("[SEP]")
                segment_ids.append(0)

                for i in range(doc_span.length):
                    split_token_index = doc_span.start + i
                    token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]

                    is_max_context = _check_is_max_context(doc_spans, doc_span_index,
                                                        split_token_index)
                    token_is_max_context[len(tokens)] = is_max_context
                    tokens.append(all_doc_tokens[split_token_index])
                    segment_ids.append(1)
                tokens.append("[SEP]")
                segment_ids.append(1)

                input_ids = tokenizer.convert_tokens_to_ids(tokens)

                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                input_mask = [1] * len(input_ids)

                # Zero-pad up to the sequence length.
                while len(input_ids) < max_seq_length:
                    input_ids.append(0)
                    input_mask.append(0)
                    segment_ids.append(0)

                assert len(input_ids) == max_seq_length
                assert len(input_mask) == max_seq_length
                assert len(segment_ids) == max_seq_length

                start_position = None
                end_position = None
                if is_training and not example.is_impossible:
                    # For training, if our document chunk does not contain an annotation
                    # we throw it out, since there is nothing to predict.
                    doc_start = doc_span.start
                    doc_end = doc_span.start + doc_span.length - 1
                    out_of_span = False
                    if not (tok_start_position >= doc_start and
                            tok_end_position <= doc_end):
                        out_of_span = True
                    if out_of_span:
                        start_position = 0
                        end_position = 0
                    else:
                        doc_offset = len(query_tokens) + 2
                        start_position = tok_start_position - doc_start + doc_offset
                        end_position = tok_end_position - doc_start + doc_offset

                if is_training and example.is_impossible:
                    start_position = 0
                    end_position = 0

                if example_index < 20:
                    tf.logging.info("*** Example ***")
                    tf.logging.info("unique_id: %s" % (unique_id))
                    tf.logging.info("example_index: %s" % (example_index))
                    tf.logging.info("doc_span_index: %s" % (doc_span_index))
                    tf.logging.info("tokens: %s" % " ".join(
                        [tokenization.printable_text(x) for x in tokens]))
                    tf.logging.info("token_to_orig_map: %s" % " ".join(
                        ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
                    tf.logging.info("token_is_max_context: %s" % " ".join([
                        "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
                    ]))
                    tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                    tf.logging.info(
                        "input_mask: %s" % " ".join([str(x) for x in input_mask]))
                    tf.logging.info(
                        "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                    if is_training and example.is_impossible:
                        tf.logging.info("impossible example")
                    if is_training and not example.is_impossible:
                        answer_text = " ".join(tokens[start_position:(end_position + 1)])
                        tf.logging.info("start_position: %d" % (start_position))
                        tf.logging.info("end_position: %d" % (end_position))
                        tf.logging.info(
                            "answer: %s" % (tokenization.printable_text(answer_text)))

                feature = InputFeatures(
                    unique_id=unique_id,
                    example_index=example_index,
                    doc_span_index=doc_span_index,
                    tokens=tokens,
                    token_to_orig_map=token_to_orig_map,
                    token_is_max_context=token_is_max_context,
                    input_ids=input_ids,
                    input_mask=input_mask,
                    segment_ids=segment_ids,
                    start_position=start_position,
                    end_position=end_position,
                    is_impossible=example.is_impossible)

                # Run callback
                
                result.append(feature)
                unique_id += 1
        return result

    class SquadExample(object):


        def __init__(self,
                    qas_id,
                    question_text,
                    doc_tokens,
                    orig_answer_text=None,
                    start_position=None,
                    end_position=None,
                    is_impossible=False):
            self.qas_id = qas_id
            self.question_text = question_text
            self.doc_tokens = doc_tokens
            self.orig_answer_text = orig_answer_text
            self.start_position = start_position
            self.end_position = end_position
            self.is_impossible = is_impossible

        def __str__(self):
            return self.__repr__()

        def __repr__(self):
            s = ""
            s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
            s += ", question_text: %s" % (
                tokenization.printable_text(self.question_text))
            s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
            if self.start_position:
                s += ", start_position: %d" % (self.start_position)
            if self.start_position:
                s += ", end_position: %d" % (self.end_position)
            if self.start_position:
                s += ", is_impossible: %r" % (self.is_impossible)
            return s



    def read_squad_examples(input_file, is_training):
        """Read a SQuAD json file into a list of SquadExample."""

        input_data = json.loads(input_file)["data"]

        def is_whitespace(c):
            if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
                return True
            return False

        examples = []
        for entry in input_data:
            for paragraph in entry["paragraphs"]:
                paragraph_text = paragraph["context"]
                doc_tokens = []
                char_to_word_offset = []
                prev_is_whitespace = True
                for c in paragraph_text:
                    if is_whitespace(c):
                        prev_is_whitespace = True
                    else:
                        if prev_is_whitespace:
                            doc_tokens.append(c)
                        else:
                            doc_tokens[-1] += c
                        prev_is_whitespace = False
                    char_to_word_offset.append(len(doc_tokens) - 1)

                for qa in paragraph["qas"]:
                    qas_id = qa["id"]
                    question_text = qa["question"]
                    start_position = None
                    end_position = None
                    orig_answer_text = None
                    is_impossible = False
                    if is_training:

        
                        if (len(qa["answers"]) != 1) and (not is_impossible):
                            raise ValueError(
                                "For training, each question should have exactly 1 answer.")
                        if not is_impossible:
                            answer = qa["answers"][0]
                            orig_answer_text = answer["text"]
                            answer_offset = answer["answer_start"]
                            answer_length = len(orig_answer_text)
                            start_position = char_to_word_offset[answer_offset]
                            end_position = char_to_word_offset[answer_offset + answer_length -
                                                1]
                            # Only add answers where the text can be exactly recovered from the
                            # document. If this CAN'T happen it's likely due to weird Unicode
                            # stuff so we will just skip the example.
                            #
                            # Note that this means for training mode, every example is NOT
                            # guaranteed to be preserved.
                            actual_text = " ".join(
                                doc_tokens[start_position:(end_position + 1)])
                            cleaned_answer_text = " ".join(
                                tokenization.whitespace_tokenize(orig_answer_text))
                            if actual_text.find(cleaned_answer_text) == -1:
                                tf.logging.warning("Could not find answer: '%s' vs. '%s'",
                                                    actual_text, cleaned_answer_text)
                                continue
                        else:
                            start_position = -1
                            end_position = -1
                            orig_answer_text = ""

                    example = SquadExample(
                        qas_id=qas_id,
                        question_text=question_text,
                        doc_tokens=doc_tokens,
                        orig_answer_text=orig_answer_text,
                        start_position=start_position,
                        end_position=end_position,
                        is_impossible=is_impossible)
                    examples.append(example)

        return examples


    def get_result(title,text,question,url):
     
        data = make_json(title,text,question)
      

        examples = read_squad_examples(data,False)


        predict_files = convert_examples_to_features(
                examples=examples,
                tokenizer=tokenizer,
                max_seq_length=512,
                doc_stride=128,
                max_query_length=100,
                is_training=False,
        )
       
        headers = {"content-type": "application/json"}
        all_results = []
        for predict_file in predict_files: 
            features = {}
            features["unique_ids"] = predict_file.unique_id
            features["input_mask"] = predict_file.input_mask
            features["segment_ids"] = predict_file.segment_ids
            features["input_ids"] = predict_file.input_ids
            data_list = []
            data_list.append(features)
        
            data = json.dumps({"instances": data_list})
    
            json_response = requests.post(url, data=data, headers=headers)

       
            x = json.loads(json_response.text)
          
            all_results.append(
                RawResult(
                    unique_id=predict_file.unique_id,
                    start_logits=x['predictions'][0]['start_logits'],
                    end_logits=x['predictions'][0]['end_logits']))
       
        result = write_predictions(examples, predict_files, all_results,20, 64,True)
        return result

    return get_result(title, text, question, url)