





import pytest


snake_case 				=       """__dummy_dataset1__"""

snake_case 				=       """
import json
import os

import datasets


REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),
                \"ner_tags\": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            \"O\",
                            \"B-PER\",
                            \"I-PER\",
                            \"B-ORG\",
                            \"I-ORG\",
                            \"B-LOC\",
                            \"I-LOC\",
                        ]
                    )
                ),
                \"langs\": datasets.Sequence(datasets.Value(\"string\")),
                \"spans\": datasets.Sequence(datasets.Value(\"string\")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, \"r\", encoding=\"utf-8\") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
"""

@pytest.fixture
def 							SCREAMING_SNAKE_CASE__       (  )      ->						List[str]:
 return DATASET_LOADING_SCRIPT_NAME

@pytest.fixture
def 							SCREAMING_SNAKE_CASE__       (  )      ->						Tuple:
 return DATASET_LOADING_SCRIPT_CODE







@pytest.fixture
def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :Union[str, Any]			,							snake_case__  :Dict     )      ->						Optional[Any]:
 _lowercase							= dataset_loading_script_name
 _lowercase							= tmp_path / 'datasets' / script_name
 script_dir.mkdir(parents=snake_case__     )
 _lowercase							= script_dir / F"""{script_name}.py"""
 with open(snake_case__			,							'w'     ) as f:
  f.write(snake_case__     )
 return str(snake_case__     )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case 				=       {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""BertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

      snake_case 				=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)