

"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )
class        _lowercase					(     unittest.TestCase     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=13 ,						UpperCamelCase_=7 ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						UpperCamelCase_=99 ,						UpperCamelCase_=32 ,						UpperCamelCase_=5 ,						UpperCamelCase_=4 ,						UpperCamelCase_=37 ,						UpperCamelCase_="gelu" ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=512 ,						UpperCamelCase_=16 ,						UpperCamelCase_=2 ,						UpperCamelCase_=0.0_2 ,						UpperCamelCase_=4 ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      seq_length
      __magic_name__     =      is_training
      __magic_name__     =      use_attention_mask
      __magic_name__     =      use_token_type_ids
      __magic_name__     =      use_labels
      __magic_name__     =      vocab_size
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      attention_probs_dropout_prob
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      type_vocab_size
      __magic_name__     =      type_sequence_label_size
      __magic_name__     =      initializer_range
      __magic_name__     =      num_choices







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size       )

      __magic_name__     =      None
      if self.use_attention_mask:
       __magic_name__     =      random_attention_mask([self.batch_size, self.seq_length]       )

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

      __magic_name__     =      RobertaConfig(
          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 ,						is_decoder=UpperCamelCase_ ,						initializer_range=self.initializer_range ,						)

      return config, input_ids, token_type_ids, attention_mask







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.prepare_config_and_inputs()
      __magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__     =      config_and_inputs
      __magic_name__     =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.prepare_config_and_inputs()
      __magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__     =      config_and_inputs

      __magic_name__     =      True
      __magic_name__     =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      __magic_name__     =      ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2       )

      return (
          config,
          input_ids,
          token_type_ids,
          encoder_hidden_states,
          encoder_attention_mask,
      )


@require_flax
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       True

     _lowerCamelCase     =       (
         (
             FlaxRobertaModel,
             FlaxRobertaForCausalLM,
             FlaxRobertaForMaskedLM,
             FlaxRobertaForSequenceClassification,
             FlaxRobertaForTokenClassification,
             FlaxRobertaForMultipleChoice,
             FlaxRobertaForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      FlaxRobertaModelTester(self       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      for model_class_name in self.all_model_classes:
       __magic_name__     =      model_class_name.from_pretrained('''roberta-base''' ,						from_pt=UpperCamelCase_       )
       __magic_name__     =      model(np.ones((1, 1)       )       )
       self.assertIsNotNone(UpperCamelCase_       )




"""simple docstring"""






from pathlib import Path

import fire





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Optional[int]:
 __magic_name__     =      Path(__UpperCamelCase						)
 __magic_name__     =      Path(__UpperCamelCase						)
 dest_dir.mkdir(exist_ok=__UpperCamelCase						)
 for path in src_dir.iterdir():
  __magic_name__     =      [x.rstrip() for x in list(path.open().readlines()						)][:n]
  __magic_name__     =      dest_dir.joinpath(path.name						)
  print(__UpperCamelCase						)
  dest_path.open('''w'''						).write('''\n'''.join(__UpperCamelCase						)						)


if __name__ == "__main__":
 fire.Fire(minify)

