
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

		UpperCamelCase          =					(
		    ('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          =					model.state_dict()

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

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

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


def 				A	( lowercase=None			)       ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase          =					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          =					parser.parse_args(lowercase			)

		UpperCamelCase          =					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()


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel
