



'''simple docstring'''



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.electra.modeling_flax_electra import (
     FlaxElectraForCausalLM,
     FlaxElectraForMaskedLM,
     FlaxElectraForMultipleChoice,
     FlaxElectraForPreTraining,
     FlaxElectraForQuestionAnswering,
     FlaxElectraForSequenceClassification,
     FlaxElectraForTokenClassification,
     FlaxElectraModel,
 )
class        UpperCAmelCase__  (    unittest.TestCase):



 def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=9_9		,	lowercase=2_4		,	lowercase=3_2		,	lowercase=5		,	lowercase=4		,	lowercase=3_7		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=5_1_2		,	lowercase=1_6		,	lowercase=2		,	lowercase=0.02		,	lowercase=4		,	)  ->							Optional[int]:
  __UpperCamelCase				      =  parent
  __UpperCamelCase				      =  batch_size
  __UpperCamelCase				      =  seq_length
  __UpperCamelCase				      =  is_training
  __UpperCamelCase				      =  use_attention_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_choices



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)

  __UpperCamelCase				      =  None
  if self.use_attention_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				      =  ElectraConfig(
      vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	embedding_size=self.embedding_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		,	)

  return config, input_ids, token_type_ids, attention_mask




 def    __lowerCamelCase				(  self			)  ->							int:
  __UpperCamelCase				      =  self.prepare_config_and_inputs()
  __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  config_and_inputs
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
  return config, inputs_dict


@require_flax
class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				True

 __SCREAMING_SNAKE_CASE					      =				(
     (
         FlaxElectraModel,
         FlaxElectraForCausalLM,
         FlaxElectraForMaskedLM,
         FlaxElectraForPreTraining,
         FlaxElectraForTokenClassification,
         FlaxElectraForQuestionAnswering,
         FlaxElectraForMultipleChoice,
         FlaxElectraForSequenceClassification,
     )
     if is_flax_available()
     else ()
 )



 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase				      =  FlaxElectraModelTester(self			)




 @slow
 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  for model_class_name in self.all_model_classes:
   if model_class_name == FlaxElectraForMaskedLM:
    __UpperCamelCase				      =  model_class_name.from_pretrained("""google/electra-small-generator"""			)
   else:
    __UpperCamelCase				      =  model_class_name.from_pretrained("""google/electra-small-discriminator"""			)
   __UpperCamelCase				      =  model(np.ones((1, 1)			)			)
   self.assertIsNotNone(lowercase			)










'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def _lowercase   (  __A							,__A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
 print(f"Building PyTorch model from configuration: {config}"       )
 __UpperCamelCase				      =  TaForConditionalGeneration(__A       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(__A							,__A							,__A       )

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"       )
 model.save_pretrained(__A       )


if __name__ == "__main__":
 a__						:			int										=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 a__						:			Optional[Any]										=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





