



"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_a      :			Tuple					=							logging.getLogger(__name__)






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_=-1     ):
      _snake_case     :						Dict						=   label_idx
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_     ):

      if isinstance(snake_case_						,    snake_case_     ):
        _snake_case     :						List[Any]						=   mode.value
      _snake_case     :						str						=   os.path.join(snake_case_						,    F'{mode}.txt'     )
      _snake_case     :						int						=   1
      _snake_case     :						List[Any]						=   []
      with open(snake_case_						,    encoding="utf-8"     ) as f:
        _snake_case     :						Optional[int]						=   []
        _snake_case     :						Tuple						=   []
        for line in f:
          if line.startswith("-DOCSTART-"     ) or line == "" or line == "\n":
            if words:
              examples.append(InputExample(guid=F'{mode}-{guid_index}'						,    words=snake_case_						,    labels=snake_case_     )     )
              guid_index += 1
              _snake_case     :						Optional[int]						=   []
              _snake_case     :						Any						=   []
          else:
            _snake_case     :						Dict						=   line.split(" "     )
            words.append(splits[0]     )
            if len(snake_case_     ) > 1:
              labels.append(splits[self.label_idx].replace("\n"						,    ""     )     )
            else:
              # Examples could have no label for mode = "test"
              labels.append("O"     )
        if words:
          examples.append(InputExample(guid=F'{mode}-{guid_index}'						,    words=snake_case_						,    labels=snake_case_     )     )
      return examples
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						List[str]						=   0
      for line in test_input_reader:
        if line.startswith("-DOCSTART-"     ) or line == "" or line == "\n":
          writer.write(snake_case_     )
          if not preds_list[example_id]:
            example_id += 1
        elif preds_list[example_id]:
          _snake_case     :						str						=   line.split()[0] + " " + preds_list[example_id].pop(0     ) + "\n"
          writer.write(snake_case_     )
        else:
          logger.warning("Maximum sequence length exceeded: No prediction for \'%s\'."						,    line.split()[0]     )


    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      if path:
        with open(snake_case_						,    "r"     ) as f:
          _snake_case     :						Optional[Any]						=   f.read().splitlines()
        if "O" not in labels:
          _snake_case     :						Any						=   ["O"] + labels
        return labels
      else:
        return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self     ):
      super().__init__(label_idx=-2     )


    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      if path:
        with open(snake_case_						,    "r"     ) as f:
          _snake_case     :						Optional[Any]						=   f.read().splitlines()
        if "O" not in labels:
          _snake_case     :						List[Any]						=   ["O"] + labels
        return labels
      else:
        return [
            "O",
            "B-ADVP",
            "B-INTJ",
            "B-LST",
            "B-PRT",
            "B-NP",
            "B-SBAR",
            "B-VP",
            "B-ADJP",
            "B-CONJP",
            "B-PP",
            "I-ADVP",
            "I-INTJ",
            "I-LST",
            "I-PRT",
            "I-NP",
            "I-SBAR",
            "I-VP",
            "I-ADJP",
            "I-CONJP",
            "I-PP",
        ]






class      _UpperCAmelCase				(					_snake_case):
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_     ):

      if isinstance(snake_case_						,    snake_case_     ):
        _snake_case     :						Union[str, Any]						=   mode.value
      _snake_case     :						Tuple						=   os.path.join(snake_case_						,    F'{mode}.txt'     )
      _snake_case     :						Any						=   1
      _snake_case     :						List[str]						=   []

      with open(snake_case_						,    encoding="utf-8"     ) as f:
        for sentence in parse_incr(snake_case_     ):
          _snake_case     :						Tuple						=   []
          _snake_case     :						int						=   []
          for token in sentence:
            words.append(token["form"]     )
            labels.append(token["upos"]     )
          assert len(snake_case_     ) == len(snake_case_     )
          if words:
            examples.append(InputExample(guid=F'{mode}-{guid_index}'						,    words=snake_case_						,    labels=snake_case_     )     )
            guid_index += 1
      return examples
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Optional[int]						=   0
      for sentence in parse_incr(snake_case_     ):
        _snake_case     :						List[str]						=   preds_list[example_id]
        _snake_case     :						int						=   ""
        for token in sentence:
          out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0     )}) '
        out += "\n"
        writer.write(snake_case_     )
        example_id += 1


    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      if path:
        with open(snake_case_						,    "r"     ) as f:
          return f.read().splitlines()
      else:
        return [
            "ADJ",
            "ADP",
            "ADV",
            "AUX",
            "CCONJ",
            "DET",
            "INTJ",
            "NOUN",
            "NUM",
            "PART",
            "PRON",
            "PROPN",
            "PUNCT",
            "SCONJ",
            "SYM",
            "VERB",
            "X",
        ]



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertModel,
    )






@require_tf
class      _UpperCAmelCase				(					_snake_case		,					_snake_case		,					unittest.TestCase):
    __lowercase							: Dict  =					(
        (
            TFMobileBertModel,
            TFMobileBertForMaskedLM,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __lowercase							: Optional[Any]  =					(
        {
            """feature-extraction""": TFMobileBertModel,
            """fill-mask""": TFMobileBertForMaskedLM,
            """question-answering""": TFMobileBertForQuestionAnswering,
            """text-classification""": TFMobileBertForSequenceClassification,
            """token-classification""": TFMobileBertForTokenClassification,
            """zero-shot""": TFMobileBertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    __lowercase							: Union[str, Any]  =					False
    __lowercase							: Optional[int]  =					False
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_=False     ):
      _snake_case     :						Union[str, Any]						=   super()._prepare_for_class(snake_case_						,    snake_case_						,    return_labels=snake_case_     )

      if return_labels:
        if model_class in get_values(snake_case_     ):
          _snake_case     :						Any						=   tf.zeros(self.model_tester.batch_size						,    dtype=tf.intaa     )

      return inputs_dict






    class      _UpperCAmelCase				(					_snake_case):
        def __init__(    self						,    snake_case_						,    snake_case_=13						,    snake_case_=7						,    snake_case_=True						,    snake_case_=True						,    snake_case_=True						,    snake_case_=True						,    snake_case_=99						,    snake_case_=32						,    snake_case_=32						,    snake_case_=2						,    snake_case_=4						,    snake_case_=37						,    snake_case_="gelu"						,    snake_case_=0.1						,    snake_case_=0.1						,    snake_case_=5_12						,    snake_case_=16						,    snake_case_=2						,    snake_case_=0.02						,    snake_case_=3						,    snake_case_=4						,    snake_case_=None						,    ):
          _snake_case     :						Optional[Any]						=   parent
          _snake_case     :						List[Any]						=   batch_size
          _snake_case     :						Optional[int]						=   seq_length
          _snake_case     :						Dict						=   is_training
          _snake_case     :						Union[str, Any]						=   use_input_mask
          _snake_case     :						List[Any]						=   use_token_type_ids
          _snake_case     :						int						=   use_labels
          _snake_case     :						Dict						=   vocab_size
          _snake_case     :						Tuple						=   hidden_size
          _snake_case     :						Optional[int]						=   num_hidden_layers
          _snake_case     :						List[str]						=   num_attention_heads
          _snake_case     :						Optional[Any]						=   intermediate_size
          _snake_case     :						Dict						=   hidden_act
          _snake_case     :						Tuple						=   hidden_dropout_prob
          _snake_case     :						Union[str, Any]						=   attention_probs_dropout_prob
          _snake_case     :						str						=   max_position_embeddings
          _snake_case     :						str						=   type_vocab_size
          _snake_case     :						Any						=   type_sequence_label_size
          _snake_case     :						Optional[int]						=   initializer_range
          _snake_case     :						List[Any]						=   num_labels
          _snake_case     :						Optional[int]						=   num_choices
          _snake_case     :						Optional[int]						=   scope
          _snake_case     :						Any						=   embedding_size
        def 					lowerCamelCase__	(    self     ):
          _snake_case     :						Optional[int]						=   ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size     )

          _snake_case     :						Optional[Any]						=   None
          if self.use_input_mask:
            _snake_case     :						Optional[Any]						=   random_attention_mask([self.batch_size, self.seq_length]     )

          _snake_case     :						List[str]						=   None
          if self.use_token_type_ids:
            _snake_case     :						Any						=   ids_tensor([self.batch_size, self.seq_length]						,    self.type_vocab_size     )

          _snake_case     :						Dict						=   None
          _snake_case     :						Tuple						=   None
          _snake_case     :						str						=   None
          if self.use_labels:
            _snake_case     :						Union[str, Any]						=   ids_tensor([self.batch_size]						,    self.type_sequence_label_size     )
            _snake_case     :						Any						=   ids_tensor([self.batch_size, self.seq_length]						,    self.num_labels     )
            _snake_case     :						Optional[int]						=   ids_tensor([self.batch_size]						,    self.num_choices     )

          _snake_case     :						Tuple						=   MobileBertConfig(
              vocab_size=self.vocab_size						,    hidden_size=self.hidden_size						,    num_hidden_layers=self.num_hidden_layers						,    num_attention_heads=self.num_attention_heads						,    intermediate_size=self.intermediate_size						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    max_position_embeddings=self.max_position_embeddings						,    type_vocab_size=self.type_vocab_size						,    initializer_range=self.initializer_range						,    embedding_size=self.embedding_size						,    )

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						Dict						=   TFMobileBertModel(config=snake_case_     )
          _snake_case     :						Optional[Any]						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case     :						Optional[int]						=   model(snake_case_     )

          _snake_case     :						Union[str, Any]						=   [input_ids, input_mask]
          _snake_case     :						Optional[Any]						=   model(snake_case_     )

          _snake_case     :						Dict						=   model(snake_case_     )

          self.parent.assertEqual(
              result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)     )
          self.parent.assertEqual(result.pooler_output.shape						,    (self.batch_size, self.hidden_size)     )
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						List[Any]						=   TFMobileBertForMaskedLM(config=snake_case_     )
          _snake_case     :						Tuple						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case     :						List[str]						=   model(snake_case_     )
          self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)     )
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						Union[str, Any]						=   TFMobileBertForNextSentencePrediction(config=snake_case_     )
          _snake_case     :						Dict						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case     :						Tuple						=   model(snake_case_     )
          self.parent.assertEqual(result.logits.shape						,    (self.batch_size, 2)     )
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						str						=   TFMobileBertForPreTraining(config=snake_case_     )
          _snake_case     :						Union[str, Any]						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case     :						List[Any]						=   model(snake_case_     )
          self.parent.assertEqual(
              result.prediction_logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)     )
          self.parent.assertEqual(result.seq_relationship_logits.shape						,    (self.batch_size, 2)     )
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						str						=   self.num_labels
          _snake_case     :						str						=   TFMobileBertForSequenceClassification(config=snake_case_     )
          _snake_case     :						Union[str, Any]						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case     :						Optional[int]						=   model(snake_case_     )
          self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)     )
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						Any						=   self.num_choices
          _snake_case     :						Tuple						=   TFMobileBertForMultipleChoice(config=snake_case_     )
          _snake_case     :						List[Any]						=   tf.tile(tf.expand_dims(snake_case_						,    1     )						,    (1, self.num_choices, 1)     )
          _snake_case     :						List[str]						=   tf.tile(tf.expand_dims(snake_case_						,    1     )						,    (1, self.num_choices, 1)     )
          _snake_case     :						Tuple						=   tf.tile(tf.expand_dims(snake_case_						,    1     )						,    (1, self.num_choices, 1)     )
          _snake_case     :						int						=   {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          _snake_case     :						Optional[Any]						=   model(snake_case_     )
          self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_choices)     )
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						Union[str, Any]						=   self.num_labels
          _snake_case     :						Optional[int]						=   TFMobileBertForTokenClassification(config=snake_case_     )
          _snake_case     :						List[Any]						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case     :						List[Any]						=   model(snake_case_     )
          self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.num_labels)     )
        def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
          _snake_case     :						int						=   TFMobileBertForQuestionAnswering(config=snake_case_     )
          _snake_case     :						str						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          _snake_case     :						Union[str, Any]						=   model(snake_case_     )
          self.parent.assertEqual(result.start_logits.shape						,    (self.batch_size, self.seq_length)     )
          self.parent.assertEqual(result.end_logits.shape						,    (self.batch_size, self.seq_length)     )


        def 					lowerCamelCase__	(    self     ):
          _snake_case     :						Optional[Any]						=   self.prepare_config_and_inputs()
          (
              (
              _snake_case
          )			,					(
              _snake_case
          )			,					(
              _snake_case
          )			,					(
              _snake_case
          )			,					(
              _snake_case
          )			,					(
              _snake_case
          )			,					(
              _snake_case
          )			,					
          )     :						Tuple						=   config_and_inputs
          _snake_case     :						Tuple						=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   TFMobileBertModelTest.TFMobileBertModelTester(self     )
      _snake_case     :						Optional[Any]						=   ConfigTester(self						,    config_class=snake_case_						,    hidden_size=37     )
    def 					lowerCamelCase__	(    self     ):
      self.config_tester.run_common_tests()
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_model(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[Any]						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_     )


    @slow
    def 					lowerCamelCase__	(    self     ):
      # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["google/mobilebert-uncased"]:
        _snake_case     :						str						=   TFMobileBertModel.from_pretrained(snake_case_     )
        self.assertIsNotNone(snake_case_     )






@require_tf
class      _UpperCAmelCase				(					unittest.TestCase):
    @slow
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased"     )
      _snake_case     :						str						=   tf.constant([[0, 1, 2, 3, 4, 5]]     )
      _snake_case     :						Union[str, Any]						=   model(snake_case_     )[0]

      _snake_case     :						int						=   [1, 6, 3_05_22]
      self.assertEqual(output.shape						,    snake_case_     )

      _snake_case     :						Optional[Any]						=   tf.constant(
          [
              [
                  [-4.5919547, -9.248295, -9.645256],
                  [-6.7306175, -6.440284, -6.6052837],
                  [-7.2743506, -6.7847915, -6.024673],
              ]
          ]     )
      tf.debugging.assert_near(output[:, :3, :3]						,    snake_case_						,    atol=1E-4     )

