

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    str 	=			('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

	__lowerCamelCase    :    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__      )

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

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

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

		__lowerCamelCase    :    str 	=			tf.train.Saver(tf.trainable_variables()      )
		saver.save(lowerCamelCase__     ,			os.path.join(lowerCamelCase__     ,			model_name.replace('-'     ,			'_'      ) + '.ckpt'      )      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=None      )  ->					Optional[Any]:
	__lowerCamelCase    :    Optional[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'      )
	__lowerCamelCase    :    Dict 	=			parser.parse_args(lowerCamelCase__      )

	__lowerCamelCase    :    Union[str, Any] 	=			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()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Mask2FormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Mask2FormerForUniversalSegmentation""",
					    """Mask2FormerModel""",
					    """Mask2FormerPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)




