




'''simple docstring'''



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 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]=1_3  ,				__lowerCamelCase :     List[str]=7  ,				__lowerCamelCase :     Dict=True  ,				__lowerCamelCase :     Optional[int]=True  ,				__lowerCamelCase :     Any=True  ,				__lowerCamelCase :     Union[str, Any]=True  ,				__lowerCamelCase :     Tuple=9_9  ,				__lowerCamelCase :     Optional[Any]=6_4  ,				__lowerCamelCase :     List[Any]=3_2  ,				__lowerCamelCase :     Any=5  ,				__lowerCamelCase :     Optional[int]=4  ,				__lowerCamelCase :     Union[str, Any]=3_7  ,				__lowerCamelCase :     int="gelu"  ,				__lowerCamelCase :     Dict=0.1  ,				__lowerCamelCase :     List[Any]=0.1  ,				__lowerCamelCase :     Optional[Any]=5_1_2  ,				__lowerCamelCase :     Tuple=1_6  ,				__lowerCamelCase :     Tuple=2  ,				__lowerCamelCase :     int=0.0_2  ,				__lowerCamelCase :     Optional[Any]=3  ,				__lowerCamelCase :     str=4  ,				__lowerCamelCase :     Tuple=None  ,				):

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



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size   )

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

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

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

      _SCREAMING_SNAKE_CASE	=				self.get_config()

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



   def 							lowerCAmelCase_      (   self :     str   ):

      """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=__lowerCamelCase  ,				initializer_range=self.initializer_range  ,				)



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MobileBertModel(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				token_type_ids=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )

      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 :     Optional[int]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MobileBertForMaskedLM(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.vocab_size)   )



   def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MobileBertForNextSentencePrediction(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(
          __lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				labels=__lowerCamelCase  ,				)
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, 2)   )



   def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MobileBertForPreTraining(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(
          __lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				labels=__lowerCamelCase  ,				next_sentence_label=__lowerCamelCase  ,				)
      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 :     Optional[int]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MobileBertForQuestionAnswering(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(
          __lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				start_positions=__lowerCamelCase  ,				end_positions=__lowerCamelCase  ,				)
      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 :     Union[str, Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.num_labels
      _SCREAMING_SNAKE_CASE	=				MobileBertForSequenceClassification(__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_labels)   )



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.num_labels
      _SCREAMING_SNAKE_CASE	=				MobileBertForTokenClassification(config=__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				labels=__lowerCamelCase   )
      self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.seq_length, self.num_labels)   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]   ):

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



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.prepare_config_and_inputs()
      (
          (
          _SCREAMING_SNAKE_CASE
      ),	(
          _SCREAMING_SNAKE_CASE
      ),	(
          _SCREAMING_SNAKE_CASE
      ),	(
          _SCREAMING_SNAKE_CASE
      ),	(
          _SCREAMING_SNAKE_CASE
      ),	(
          _SCREAMING_SNAKE_CASE
      ),	(
          _SCREAMING_SNAKE_CASE
      ),	
      )	=				config_and_inputs
      _SCREAMING_SNAKE_CASE	=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_torch
class 		lowercase_   (			A					,  A					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						(
       (
           MobileBertModel,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase_      =						(
       {
           '''feature-extraction''': MobileBertModel,
           '''fill-mask''': MobileBertForMaskedLM,
           '''question-answering''': MobileBertForQuestionAnswering,
           '''text-classification''': MobileBertForSequenceClassification,
           '''token-classification''': MobileBertForTokenClassification,
           '''zero-shot''': MobileBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_      =						True



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     str=False   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super()._prepare_for_class(__lowerCamelCase  ,				__lowerCamelCase  ,				return_labels=__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MobileBertModelTester(self   )
      _SCREAMING_SNAKE_CASE	=				ConfigTester(self  ,				config_class=__lowerCamelCase  ,				hidden_size=3_7   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_model(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCamelCase   )


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Optional[Any]:
   return torch.tensor(
       __A	,					dtype=torch.long	,					device=__A	,					)


lowerCamelCase_			=						1e-3







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


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				MobileBertModel.from_pretrained("google/mobilebert-uncased"   ).to(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				_long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   )
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase   )[0]
      _SCREAMING_SNAKE_CASE	=				torch.Size((1, 9, 5_1_2)   )
      self.assertEqual(output.shape  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.tensor(
          [
              [
                  [-2.4_736_526e07, 8.2_691_656e04, 1.6_521_838e05],
                  [-5.7_541_704e-01, 3.9_056_022e00, 4.4_011_507e00],
                  [2.6_047_359e00, 1.5_677_652e00, -1.7_324_188e-01],
              ]
          ]  ,				device=__lowerCamelCase  ,				)

      # 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
      _SCREAMING_SNAKE_CASE	=				torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
      _SCREAMING_SNAKE_CASE	=				torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

      self.assertTrue(lower_bound and upper_bound   )







'''simple docstring'''



import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCamelCase_			=						'<<<<<<< This should probably be modified because it mentions: '

lowerCamelCase_			=						'=======\n>>>>>>>\n'

lowerCamelCase_			=						[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCamelCase_			=						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Namespace  )			->			List[Any]:
   return ConvertCommand(args.tfds_path	,					args.datasets_directory  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     ArgumentParser   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				parser.add_parser(
          "convert"  ,				help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."  ,				)
      train_parser.add_argument(
          "--tfds_path"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."  ,				)
      train_parser.add_argument(
          "--datasets_directory"  ,				type=__lowerCamelCase  ,				required=__lowerCamelCase  ,				help="Path to the HuggingFace Datasets folder."   )
      train_parser.set_defaults(func=__lowerCamelCase   )



   def __init__(   self :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str  ,				*__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_logger("datasets-cli/converting"   )

      _SCREAMING_SNAKE_CASE	=				tfds_path
      _SCREAMING_SNAKE_CASE	=				datasets_directory



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._tfds_path   )
      elif os.path.isfile(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.path.dirname(self._tfds_path   )
      else:
         raise ValueError("--tfds_path is neither a directory nor a file. Please check path."   )

      _SCREAMING_SNAKE_CASE	=				os.path.abspath(self._datasets_directory   )

      self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""   )

      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				{}

      if os.path.isdir(self._tfds_path   ):
         _SCREAMING_SNAKE_CASE	=				os.listdir(__lowerCamelCase   )
      else:
         _SCREAMING_SNAKE_CASE	=				[os.path.basename(self._tfds_path   )]

      for f_name in file_names:
         self._logger.info(F"""Looking at file {f_name}"""   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )

         if not os.path.isfile(__lowerCamelCase   ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
            self._logger.info("Skipping file"   )
            continue

         with open(__lowerCamelCase  ,				encoding="utf-8"   ) as f:
            _SCREAMING_SNAKE_CASE	=				f.readlines()

         _SCREAMING_SNAKE_CASE	=				[]
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				False
         _SCREAMING_SNAKE_CASE	=				[]
         for line in lines:
            _SCREAMING_SNAKE_CASE	=				line

            # Convert imports
            if "import tensorflow.compat.v2 as tf" in out_line:
               continue
            elif "@tfds.core" in out_line:
               continue
            elif "builder=self" in out_line:
               continue
            elif "import tensorflow_datasets.public_api as tfds" in out_line:
               _SCREAMING_SNAKE_CASE	=				"import datasets\n"
            elif "import tensorflow" in out_line:
               # order is important here
               _SCREAMING_SNAKE_CASE	=				""
               continue
            elif "from absl import logging" in out_line:
               _SCREAMING_SNAKE_CASE	=				"from datasets import logging\n"
            elif "getLogger" in out_line:
               _SCREAMING_SNAKE_CASE	=				out_line.replace("getLogger"  ,				"get_logger"   )
            elif any(expression in out_line for expression in TO_HIGHLIGHT   ):
               _SCREAMING_SNAKE_CASE	=				True
               _SCREAMING_SNAKE_CASE	=				list(filter(lambda __lowerCamelCase   : e in out_line  ,				__lowerCamelCase   )   )
               out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowerCamelCase   ) + "\n"   )
               out_lines.append(__lowerCamelCase   )
               out_lines.append(__lowerCamelCase   )
               continue
            else:
               for pattern, replacement in TO_CONVERT:
                  _SCREAMING_SNAKE_CASE	=				re.sub(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )

                # Take care of saving utilities (to later move them together with main script)
            if "tensorflow_datasets" in out_line:
               _SCREAMING_SNAKE_CASE	=				re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)"  ,				__lowerCamelCase   )
               tfds_imports.extend(imp.strip() for imp in match.group(1   ).split(","   )   )
               _SCREAMING_SNAKE_CASE	=				"from . import " + match.group(1   )

            # Check we have not forget anything
            if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
               raise ValueError(F"""Error converting {out_line.strip()}"""   )

            if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
               _SCREAMING_SNAKE_CASE	=				True
            out_lines.append(__lowerCamelCase   )

         if is_builder or "wmt" in f_name:
            # We create a new directory for each dataset
            _SCREAMING_SNAKE_CASE	=				f_name.replace(".py"  ,				""   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__lowerCamelCase  ,				__lowerCamelCase   )
            os.makedirs(__lowerCamelCase  ,				exist_ok=__lowerCamelCase   )
            self._logger.info(F"""Adding directory {output_dir}"""   )
            imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}   )
         else:
            # Utilities will be moved at the end
            utils_files.append(__lowerCamelCase   )

         if needs_manual_update:
            with_manual_update.append(__lowerCamelCase   )

         with open(__lowerCamelCase  ,				"w"  ,				encoding="utf-8"   ) as f:
            f.writelines(__lowerCamelCase   )
         self._logger.info(F"""Converted in {output_file}"""   )

      for utils_file in utils_files:
         try:
            _SCREAMING_SNAKE_CASE	=				os.path.basename(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				imports_to_builder_map[f_name.replace(".py"  ,				""   )]
            self._logger.info(F"""Moving {dest_folder} to {utils_file}"""   )
            shutil.copy(__lowerCamelCase  ,				__lowerCamelCase   )
         except KeyError:
            self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually."""   )

      if with_manual_update:
         for file_path in with_manual_update:
            self._logger.warning(
                F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""   )

