import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         MobileBertForMaskedLM,
         MobileBertForMultipleChoice,
         MobileBertForNextSentencePrediction,
         MobileBertForPreTraining,
         MobileBertForQuestionAnswering,
         MobileBertForSequenceClassification,
         MobileBertForTokenClassification,
         MobileBertModel,
     )



class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=7   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=99   ,				SCREAMING_SNAKE_CASE_=64   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=5   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=37   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      seq_length
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_input_mask
       UpperCamelCase__										=      use_token_type_ids
       UpperCamelCase__										=      use_labels
       UpperCamelCase__										=      vocab_size
       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      embedding_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      max_position_embeddings
       UpperCamelCase__										=      type_vocab_size
       UpperCamelCase__										=      type_sequence_label_size
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      num_labels
       UpperCamelCase__										=      num_choices
       UpperCamelCase__										=      scope






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size	)

       UpperCamelCase__										=      None
       if self.use_input_mask:
            UpperCamelCase__										=      random_attention_mask([self.batch_size, self.seq_length]	)

       UpperCamelCase__										=      None
       if self.use_token_type_ids:
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.seq_length]   ,				self.type_vocab_size	)

       UpperCamelCase__										=      None
       UpperCamelCase__										=      None
       UpperCamelCase__										=      None
       if self.use_labels:
            UpperCamelCase__										=      ids_tensor([self.batch_size]   ,				self.type_sequence_label_size	)
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels	)
            UpperCamelCase__										=      ids_tensor([self.batch_size]   ,				self.num_choices	)

       UpperCamelCase__										=      self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       return 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   ,				embedding_size=self.embedding_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   ,				is_decoder=SCREAMING_SNAKE_CASE_   ,				initializer_range=self.initializer_range   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      MobileBertModel(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      MobileBertForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      MobileBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(
           SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_   ,				)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, 2)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      MobileBertForPreTraining(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(
           SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_   ,				next_sentence_label=SCREAMING_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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      MobileBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(
           SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				start_positions=SCREAMING_SNAKE_CASE_   ,				end_positions=SCREAMING_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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.num_labels
       UpperCamelCase__										=      MobileBertForSequenceClassification(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.num_labels
       UpperCamelCase__										=      MobileBertForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.num_choices
       UpperCamelCase__										=      MobileBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      input_ids.unsqueeze(1	).expand(-1   ,				self.num_choices   ,				-1	).contiguous()
       UpperCamelCase__										=      token_type_ids.unsqueeze(1	).expand(-1   ,				self.num_choices   ,				-1	).contiguous()
       UpperCamelCase__										=      input_mask.unsqueeze(1	).expand(-1   ,				self.num_choices   ,				-1	).contiguous()
       UpperCamelCase__										=      model(
           SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				labels=SCREAMING_SNAKE_CASE_   ,				)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       
       )										=      config_and_inputs
       UpperCamelCase__										=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict



@require_torch
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	int						=(
      (
          MobileBertModel,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE_	:	int						=(
      {
          "feature-extraction": MobileBertModel,
          "fill-mask": MobileBertForMaskedLM,
          "question-answering": MobileBertForQuestionAnswering,
          "text-classification": MobileBertForSequenceClassification,
          "token-classification": MobileBertForTokenClassification,
          "zero-shot": MobileBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE_	:	List[str]						=True






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      super()._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				return_labels=SCREAMING_SNAKE_CASE_	)

       if return_labels:
            if model_class in get_values(SCREAMING_SNAKE_CASE_	):
                 UpperCamelCase__										=      torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length)   ,				dtype=torch.long   ,				device=SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      torch.zeros(
                     self.model_tester.batch_size   ,				dtype=torch.long   ,				device=SCREAMING_SNAKE_CASE_	)
       return inputs_dict






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      MobileBertModelTester(self	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_   ,				hidden_size=37	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_model(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_masked_lm(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_pretraining(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_question_answering(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_token_classification(*SCREAMING_SNAKE_CASE_	)







def  __UpperCamelCase    (						A							):
     return torch.tensor(
         A ,							dtype=torch.long ,							device=A ,							)


__magic_name__								=1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class     _A   (    unittest.TestCase							):






  @slow
  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      MobileBertModel.from_pretrained('''google/mobilebert-uncased'''	).to(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]	)
       with torch.no_grad():
            UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)[0]
       UpperCamelCase__										=      torch.Size((1, 9, 512)	)
       self.assertEqual(output.shape   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      torch.tensor(
           [
               [
                   [-2.4736526E07, 8.2691656E04, 1.6521838E05],
                   [-5.7541704E-01, 3.9056022E00, 4.4011507E00],
                   [2.6047359E00, 1.5677652E00, -1.7324188E-01],
               ]
           ]   ,				device=SCREAMING_SNAKE_CASE_   ,				)

       # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
       # ~1 difference, it's therefore not a good idea to measure using addition.
       # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
       # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
       UpperCamelCase__										=      torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE	)
       UpperCamelCase__										=      torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE	)

       self.assertTrue(lower_bound and upper_bound	)




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=""
  SCREAMING_SNAKE_CASE_	:	str						=(
      None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
  )
  SCREAMING_SNAKE_CASE_	:	str						=None  # compression type in fsspec. ex: "gzip"
  SCREAMING_SNAKE_CASE_	:	str						=None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = ""   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       super().__init__(self   ,				**SCREAMING_SNAKE_CASE_	)
       # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
       UpperCamelCase__										=      fsspec.open(
           SCREAMING_SNAKE_CASE_   ,				mode='''rb'''   ,				protocol=SCREAMING_SNAKE_CASE_   ,				compression=self.compression   ,				client_kwargs={
               '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
               '''trust_env''': True,  # Enable reading proxy env variables.
               **(target_options or {}).pop('''client_kwargs'''   ,				{}	),  # To avoid issues if it was already passed.
           }   ,				**(target_options or {})   ,				)
       UpperCamelCase__										=      os.path.basename(self.file.path.split('''::'''	)[0]	)
       UpperCamelCase__										=      (
           self.compressed_name[: self.compressed_name.rindex('''.'''	)]
           if '''.''' in self.compressed_name
           else self.compressed_name
       )
       UpperCamelCase__										=      None






  @classmethod
  def 		_a					(cls   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       return super()._strip_protocol(SCREAMING_SNAKE_CASE_	).lstrip('''/'''	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       if self.dir_cache is None:
            UpperCamelCase__										=      {**self.file.fs.info(self.file.path	), '''name''': self.uncompressed_name}
            UpperCamelCase__										=      {f['''name''']: f}






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       return self.file.open().read()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "rb"   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self._strip_protocol(SCREAMING_SNAKE_CASE_	)
       if mode != "rb":
            raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"	)
       return self.file.open()



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						="bz2"
  SCREAMING_SNAKE_CASE_	:	List[str]						="bz2"
  SCREAMING_SNAKE_CASE_	:	List[Any]						=".bz2"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						="gzip"
  SCREAMING_SNAKE_CASE_	:	Dict						="gzip"
  SCREAMING_SNAKE_CASE_	:	List[Any]						=".gz"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[Any]						="lz4"
  SCREAMING_SNAKE_CASE_	:	str						="lz4"
  SCREAMING_SNAKE_CASE_	:	Any						=".lz4"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="xz"
  SCREAMING_SNAKE_CASE_	:	List[str]						="xz"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=".xz"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						="zstd"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						="zstd"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=".zst"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "rb"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = DEFAULT_BLOCK_SIZE   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





       super().__init__(
           fo=SCREAMING_SNAKE_CASE_   ,				mode=SCREAMING_SNAKE_CASE_   ,				target_protocol=SCREAMING_SNAKE_CASE_   ,				target_options=SCREAMING_SNAKE_CASE_   ,				block_size=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)
       # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
       #
       # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
       #     out.close = close
       # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
       #
       # see https://github.com/intake/filesystem_spec/issues/725
       UpperCamelCase__										=      self.file.__enter__



       class     _A   :






         def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





              '''simple docstring'''





              UpperCamelCase__										=      file_






         def __enter__(self	)		->      Union[str, Any]:





              '''simple docstring'''





              self._file.__enter__()
              return self






         def __exit__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





              '''simple docstring'''





              self._file.__exit__(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






         def __iter__(self	)		->      Dict:





              '''simple docstring'''





              return iter(self._file	)






         def 		_a					(self	)		->      Dict:





              '''simple docstring'''





              return next(self._file	)






         def __getattr__(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





              '''simple docstring'''





              return getattr(self._file   ,				SCREAMING_SNAKE_CASE_	)

       def fixed_enter(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	):
            return WrappedFile(_enter(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)	)

       UpperCamelCase__										=      fixed_enter



