

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

      __lowerCAmelCase					:						Any					=					(
          ('''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__						)

      __lowerCAmelCase					:						Any					=					model.state_dict()

      def to_tf_var_name(lowercase__						):
            for patt, repl in iter(lowercase__						):
                  __lowerCAmelCase					:						Union[str, Any]					=					name.replace(lowercase__   ,	lowercase__						)
            return f"""bert/{name}"""

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

            __lowerCAmelCase					:						Any					=					tf.train.Saver(tf.trainable_variables()						)
            saver.save(lowercase__   ,	os.path.join(lowercase__   ,	model_name.replace('''-'''   ,	'''_'''						) + '''.ckpt'''						)						)







def 		_lowercase (     lowercase__=None						):
      __lowerCAmelCase					:						List[str]					=					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'''						)
      __lowerCAmelCase					:						Dict					=					parser.parse_args(lowercase__						)

      __lowerCAmelCase					:						Optional[int]					=					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()





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
            return True
      __lowerCAmelCase					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							padding=self.padding      ,							max_length=self.max_length      ,							pad_to_multiple_of=self.pad_to_multiple_of      ,							return_tensors='''pt''' if labels is None else None      ,							)

       if labels is None:
             return batch

       __lowerCAmelCase					:						Dict					=					torch.tensor(batch['''entity_ids''']					).shape[1]
       __lowerCAmelCase					:						Optional[int]					=					self.tokenizer.padding_side
       if padding_side == "right":
             __lowerCAmelCase					:						Any					=					[
                 list(A_					) + [self.label_pad_token_id] * (sequence_length - len(A_					)) for label in labels
             ]
       else:
             __lowerCAmelCase					:						Optional[int]					=					[
                 [self.label_pad_token_id] * (sequence_length - len(A_					)) + list(A_					) for label in labels
             ]

       __lowerCAmelCase					:						Tuple					=					[feature['''ner_tags'''] for feature in features]
       __lowerCAmelCase					:						List[Any]					=					padding_tensor(A_      ,							-1      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					[feature['''original_entity_spans'''] for feature in features]
       __lowerCAmelCase					:						Any					=					padding_tensor(A_      ,							(-1, -1)      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[Any]					=					{k: torch.tensor(A_      ,							dtype=torch.intaa					) for k, v in batch.items()}

       return batch


