



'''simple docstring'''






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def 				snake_case_  (      _lowerCAmelCase						:		BertModel     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		str						)   ->     Union[str, Any]:
							UpperCAmelCase				: int											=	('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

							UpperCAmelCase				: Optional[int]											=	(
							    ('''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						)

							UpperCAmelCase				: Optional[int]											=	model.state_dict()

							def to_tf_var_name(_lowerCAmelCase						:		str						):
														for patt, repl in iter(_lowerCAmelCase						):
																					UpperCAmelCase				: List[str]											=	name.replace(_lowerCAmelCase     ,   _lowerCAmelCase						)
														return f"""bert/{name}"""

							def create_tf_var(_lowerCAmelCase						:		np.ndarray     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		tf.Session						):
														UpperCAmelCase				: Dict											=	tf.dtypes.as_dtype(tensor.dtype						)
														UpperCAmelCase				: Any											=	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:
																					UpperCAmelCase				: List[str]											=	to_tf_var_name(_lowerCAmelCase						)
																					UpperCAmelCase				: Dict											=	state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose						):
																												UpperCAmelCase				: Optional[int]											=	torch_tensor.T
																					UpperCAmelCase				: Union[str, Any]											=	create_tf_var(tensor=_lowerCAmelCase     ,   name=_lowerCAmelCase     ,   session=_lowerCAmelCase						)
																					tf.keras.backend.set_value(_lowerCAmelCase     ,   _lowerCAmelCase						)
																					UpperCAmelCase				: Union[str, Any]											=	session.run(_lowerCAmelCase						)
																					print(f"""Successfully created {tf_name}: {np.allclose(_lowerCAmelCase     ,   _lowerCAmelCase						)}"""						)

														UpperCAmelCase				: List[str]											=	tf.train.Saver(tf.trainable_variables()						)
														saver.save(_lowerCAmelCase     ,   os.path.join(_lowerCAmelCase     ,   model_name.replace('''-'''     ,   '''_'''						) + '''.ckpt'''						)						)


def 				snake_case_  (      _lowerCAmelCase						:		List[str]=None						)   ->     List[Any]:
							UpperCAmelCase				: Union[str, Any]											=	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'''						)
							UpperCAmelCase				: str											=	parser.parse_args(_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	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()










'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





