




"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__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(UpperCamelCase				):
						os.makedirs(UpperCamelCase				)

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

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

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

						__UpperCAmelCase						:			Optional[int]												=       tf.train.Saver(tf.trainable_variables()				)
						saver.save(UpperCamelCase					,							os.path.join(UpperCamelCase					,							model_name.replace("-"					,							"_"				) + ".ckpt"				)				)

def        _UpperCamelCase     (      UpperCamelCase=None				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			List[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						:			List[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()





'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






