



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def        a						(      _UpperCAmelCase							:					BertModel ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

		__UpperCAmelCase    :     str													=	(
		    ('''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(_UpperCAmelCase						):
				os.makedirs(_UpperCAmelCase						)

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

		def to_tf_var_name(_UpperCAmelCase							:					str						):
				for patt, repl in iter(_UpperCAmelCase						):
						__UpperCAmelCase    :     Optional[Any]													=	name.replace(_UpperCAmelCase ,   _UpperCAmelCase						)
				return f'bert/{name}'

		def create_tf_var(_UpperCAmelCase							:					np.ndarray ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					tf.Session						):
				__UpperCAmelCase    :     List[str]													=	tf.dtypes.as_dtype(tensor.dtype						)
				__UpperCAmelCase    :     Optional[Any]													=	tf.get_variable(dtype=_UpperCAmelCase ,   shape=tensor.shape ,   name=_UpperCAmelCase ,   initializer=tf.zeros_initializer()						)
				session.run(tf.variables_initializer([tf_var]						)						)
				session.run(_UpperCAmelCase						)
				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(_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	state_dict[var_name].numpy()
						if any(x in var_name for x in tensors_to_transpose						):
								__UpperCAmelCase    :     Union[str, Any]													=	torch_tensor.T
						__UpperCAmelCase    :     Optional[Any]													=	create_tf_var(tensor=_UpperCAmelCase ,   name=_UpperCAmelCase ,   session=_UpperCAmelCase						)
						tf.keras.backend.set_value(_UpperCAmelCase ,   _UpperCAmelCase						)
						__UpperCAmelCase    :     Tuple													=	session.run(_UpperCAmelCase						)
						print(f'Successfully created {tf_name}: {np.allclose(_UpperCAmelCase ,   _UpperCAmelCase						)}'						)

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

def        a						(      _UpperCAmelCase							:					int=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	argparse.ArgumentParser()
		parser.add_argument('''--model_name''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''model name e.g. bert-base-uncased'''						)
		parser.add_argument(
		    '''--cache_dir''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''Directory containing pytorch model'''						)
		parser.add_argument('''--pytorch_model_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''/path/to/<pytorch-model-name>.bin'''						)
		parser.add_argument('''--tf_cache_dir''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''Directory in which to save tensorflow model'''						)
		__UpperCAmelCase    :     Optional[Any]													=	parser.parse_args(_UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	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=_UpperCAmelCase ,   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()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
				from .pipeline_kandinsky import KandinskyPipeline
				from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
				from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
				from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
				from .text_encoder import MultilingualCLIP





