
"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """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(snake_case__					):
              os.makedirs(snake_case__					)

       UpperCamelCase       			=		model.state_dict()

       def to_tf_var_name(UpperCAmelCase_					):
              for patt, repl in iter(snake_case__					):
                     UpperCamelCase       			=		name.replace(snake_case__		,   snake_case__					)
              return F"bert/{name}"

       def create_tf_var(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		tf.dtypes.as_dtype(tensor.dtype					)
              UpperCamelCase       			=		tf.get_variable(dtype=snake_case__		,   shape=tensor.shape		,   name=snake_case__		,   initializer=tf.zeros_initializer()					)
              session.run(tf.variables_initializer([tf_var]					)					)
              session.run(snake_case__					)
              return tf_var

       tf.reset_default_graph()
       with tf.Session() as session:
              for var_name in state_dict:
                     UpperCamelCase       			=		to_tf_var_name(snake_case__					)
                     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=snake_case__		,   name=snake_case__		,   session=snake_case__					)
                     tf.keras.backend.set_value(snake_case__		,   snake_case__					)
                     UpperCamelCase       			=		session.run(snake_case__					)
                     print(F"Successfully created {tf_name}: {np.allclose(snake_case__		,   snake_case__					)}"					)

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



def 						lowerCamelCase__      (				UpperCAmelCase_=None					)->				Optional[int]:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser()
       parser.add_argument("--model_name"		,   type=snake_case__		,   required=snake_case__		,   help="model name e.g. bert-base-uncased"					)
       parser.add_argument(
           "--cache_dir"		,   type=snake_case__		,   default=snake_case__		,   required=snake_case__		,   help="Directory containing pytorch model"					)
       parser.add_argument("--pytorch_model_path"		,   type=snake_case__		,   required=snake_case__		,   help="/path/to/<pytorch-model-name>.bin"					)
       parser.add_argument("--tf_cache_dir"		,   type=snake_case__		,   required=snake_case__		,   help="Directory in which to save tensorflow model"					)
       UpperCamelCase       			=		parser.parse_args(snake_case__					)

       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=snake_case__		,   ckpt_dir=args.tf_cache_dir		,   model_name=args.model_name					)


if __name__ == "__main__":
      main()













"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@register_to_config
	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = False  ,  ):
		super().__init__()

		A_		:    Tuple						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    Any						     = False

		A_		:    Tuple						     = nn.Dropout(p=lowerCAmelCase_				)

		A_		:    List[str]						     = TaConfig(
		    vocab_size=lowerCAmelCase_  ,  d_model=lowerCAmelCase_  ,  num_heads=lowerCAmelCase_  ,  d_kv=lowerCAmelCase_  ,  d_ff=lowerCAmelCase_  ,  dropout_rate=lowerCAmelCase_  ,  feed_forward_proj=lowerCAmelCase_  ,  is_decoder=lowerCAmelCase_  ,  is_encoder_decoder=lowerCAmelCase_  ,  )

		A_		:    Optional[Any]						     = nn.ModuleList()
		for lyr_num in range(lowerCAmelCase_				):
			A_		:    Tuple						     = TaBlock(lowerCAmelCase_				)
			self.encoders.append(lowerCAmelCase_				)

		A_		:    Any						     = TaLayerNorm(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = nn.Dropout(p=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.token_embedder(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = encoder_input_tokens.shape[1]
		A_		:    Any						     = torch.arange(lowerCAmelCase_  ,  device=encoder_input_tokens.device				)
		x += self.position_encoding(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.dropout_pre(lowerCAmelCase_				)

		# inverted the attention mask
		A_		:    int						     = encoder_input_tokens.size()
		A_		:    Optional[Any]						     = self.get_extended_attention_mask(lowerCAmelCase_  ,  lowerCAmelCase_				)

		for lyr in self.encoders:
			A_		:    int						     = lyr(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]
		A_		:    List[str]						     = self.layer_norm(lowerCAmelCase_				)

		return self.dropout_post(lowerCAmelCase_				), encoder_inputs_mask

