
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   UpperCAmelCase		(						unittest.TestCase ):



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=13    ,						snake_case__		:					Dict=7    ,						snake_case__		:					List[str]=True    ,						snake_case__		:					List[Any]=True    ,						snake_case__		:					List[str]=True    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					int=99    ,						snake_case__		:					List[str]=32    ,						snake_case__		:					Optional[Any]=5    ,						snake_case__		:					List[Any]=4    ,						snake_case__		:					Dict=37    ,						snake_case__		:					Optional[int]="gelu"    ,						snake_case__		:					List[str]=0.1    ,						snake_case__		:					List[Any]=0.1    ,						snake_case__		:					Union[str, Any]=5_12    ,						snake_case__		:					Any=16    ,						snake_case__		:					str=2    ,						snake_case__		:					Optional[Any]=0.02    ,						snake_case__		:					Dict=4    ,						)		->	Dict:


          '''simple docstring'''


          snake_case       :   Any							=      parent
          snake_case       :   List[Any]							=      batch_size
          snake_case       :   Optional[int]							=      seq_length
          snake_case       :   Dict							=      is_training
          snake_case       :   Union[str, Any]							=      use_attention_mask
          snake_case       :   Union[str, Any]							=      use_token_type_ids
          snake_case       :   Optional[int]							=      use_labels
          snake_case       :   Tuple							=      vocab_size
          snake_case       :   List[str]							=      hidden_size
          snake_case       :   int							=      num_hidden_layers
          snake_case       :   List[Any]							=      num_attention_heads
          snake_case       :   Optional[Any]							=      intermediate_size
          snake_case       :   List[Any]							=      hidden_act
          snake_case       :   List[str]							=      hidden_dropout_prob
          snake_case       :   Union[str, Any]							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   Optional[Any]							=      type_vocab_size
          snake_case       :   List[str]							=      type_sequence_label_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   Optional[int]							=      num_choices



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   List[str]							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size )

          snake_case       :   Optional[int]							=      None
          if self.use_attention_mask:
                 snake_case       :   Dict							=      random_attention_mask([self.batch_size, self.seq_length] )

          snake_case       :   Union[str, Any]							=      None
          if self.use_token_type_ids:
                 snake_case       :   str							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.type_vocab_size )

          snake_case       :   List[str]							=      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=snake_case__    ,						initializer_range=self.initializer_range    ,						)

          return config, input_ids, token_type_ids, attention_mask



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.prepare_config_and_inputs()
          snake_case				,					snake_case				,					snake_case				,					snake_case       :   Union[str, Any]							=      config_and_inputs
          snake_case       :   Optional[int]							=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	int:


          '''simple docstring'''


          snake_case       :   str							=      self.prepare_config_and_inputs()
          snake_case				,					snake_case				,					snake_case				,					snake_case       :   Dict							=      config_and_inputs

          snake_case       :   Any							=      True
          snake_case       :   List[Any]							=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
          snake_case       :   Union[str, Any]							=      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   UpperCAmelCase		(						A_       ,unittest.TestCase ):
   A__					:		List[str]						     =     True

   A__					:		Union[str, Any]						     =     (
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Any:


          '''simple docstring'''


          snake_case       :   Tuple							=      FlaxRobertaModelTester(self )



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


          '''simple docstring'''


          for model_class_name in self.all_model_classes:
                 snake_case       :   List[str]							=      model_class_name.from_pretrained("roberta-base"    ,						from_pt=snake_case__ )
                 snake_case       :   Dict							=      model(np.ones((1, 1) ) )
                 self.assertIsNotNone(snake_case__ )







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

					lowercase							=						(
					    ('''layer.''', '''layer_'''),
					    ('''word_embeddings.weight''', '''word_embeddings'''),
					    ('''position_embeddings.weight''', '''position_embeddings'''),
					    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
					    ('''.''', '''/'''),
					    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
					    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
					    ('''weight''', '''kernel'''),
					)

					if not os.path.isdir(lowerCAmelCase__		):
										os.makedirs(lowerCAmelCase__		)

					lowercase							=						model.state_dict()

					def to_tf_var_name(lowerCAmelCase__		):
										for patt, repl in iter(lowerCAmelCase__		):
															lowercase							=						name.replace(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return f'bert/{name}'

					def create_tf_var(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
										lowercase							=						tf.dtypes.as_dtype(tensor.dtype		)
										lowercase							=						tf.get_variable(dtype=lowerCAmelCase__ ,			shape=tensor.shape ,			name=lowerCAmelCase__ ,			initializer=tf.zeros_initializer()		)
										session.run(tf.variables_initializer([tf_var]		)		)
										session.run(lowerCAmelCase__		)
										return tf_var

					tf.reset_default_graph()
					with tf.Session() as session:
										for var_name in state_dict:
															lowercase							=						to_tf_var_name(lowerCAmelCase__		)
															lowercase							=						state_dict[var_name].numpy()
															if any(x in var_name for x in tensors_to_transpose		):
																				lowercase							=						torch_tensor.T
															lowercase							=						create_tf_var(tensor=lowerCAmelCase__ ,			name=lowerCAmelCase__ ,			session=lowerCAmelCase__		)
															tf.keras.backend.set_value(lowerCAmelCase__ ,			lowerCAmelCase__		)
															lowercase							=						session.run(lowerCAmelCase__		)
															print(f'Successfully created {tf_name}: {np.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)}'		)

										lowercase							=						tf.train.Saver(tf.trainable_variables()		)
										saver.save(lowerCAmelCase__ ,			os.path.join(lowerCAmelCase__ ,			model_name.replace('''-''' ,			'''_'''		) + '''.ckpt'''		)		)



def        UpperCamelCase    (							lowerCAmelCase__=None		):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''model name e.g. bert-base-uncased'''		)
					parser.add_argument(
					    '''--cache_dir''' ,			type=lowerCAmelCase__ ,			default=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory containing pytorch model'''		)
					parser.add_argument('''--pytorch_model_path''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''/path/to/<pytorch-model-name>.bin'''		)
					parser.add_argument('''--tf_cache_dir''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory in which to save tensorflow model'''		)
					lowercase							=						parser.parse_args(lowerCAmelCase__		)

					lowercase							=						BertModel.from_pretrained(
					    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path		) ,			cache_dir=args.cache_dir ,			)

					convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name		)


if __name__ == "__main__":
							main()




