



'''simple docstring'''




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							UpperCAmelCase  :	List[str]							  =	("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

							UpperCAmelCase  :	List[Any]							  =	(
							    ("""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(_lowercase	):
														os.makedirs(_lowercase	)

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

							def to_tf_var_name(_lowercase	):
														for patt, repl in iter(_lowercase	):
																					UpperCAmelCase  :	Tuple							  =	name.replace(_lowercase			,       _lowercase	)
														return F'''bert/{name}'''

							def create_tf_var(_lowercase			,       _lowercase			,       _lowercase	):
														UpperCAmelCase  :	List[str]							  =	tf.dtypes.as_dtype(tensor.dtype	)
														UpperCAmelCase  :	Optional[int]							  =	tf.get_variable(dtype=_lowercase			,       shape=tensor.shape			,       name=_lowercase			,       initializer=tf.zeros_initializer()	)
														session.run(tf.variables_initializer([tf_var]	)	)
														session.run(_lowercase	)
														return tf_var

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

														UpperCAmelCase  :	Dict							  =	tf.train.Saver(tf.trainable_variables()	)
														saver.save(_lowercase			,       os.path.join(_lowercase			,       model_name.replace("""-"""			,       """_"""	) + """.ckpt"""	)	)



def    __lowerCamelCase						(      _lowercase=None	)   ->    int:
							UpperCAmelCase  :	List[str]							  =	argparse.ArgumentParser()
							parser.add_argument("""--model_name"""			,       type=_lowercase			,       required=_lowercase			,       help="""model name e.g. bert-base-uncased"""	)
							parser.add_argument(
							    """--cache_dir"""			,       type=_lowercase			,       default=_lowercase			,       required=_lowercase			,       help="""Directory containing pytorch model"""	)
							parser.add_argument("""--pytorch_model_path"""			,       type=_lowercase			,       required=_lowercase			,       help="""/path/to/<pytorch-model-name>.bin"""	)
							parser.add_argument("""--tf_cache_dir"""			,       type=_lowercase			,       required=_lowercase			,       help="""Directory in which to save tensorflow model"""	)
							UpperCAmelCase  :	Optional[Any]							  =	parser.parse_args(_lowercase	)

							UpperCAmelCase  :	int							  =	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=_lowercase			,       ckpt_dir=args.tf_cache_dir			,       model_name=args.model_name	)


if __name__ == "__main__":
							main()






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

