


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__							:						Optional[int]							     =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Union[str, Any]:
  snake_case__						= os.path.abspath(__lowerCAmelCase				)
  logger.info(F"""Converting TensorFlow checkpoint from {tf_path}"""				)
  # Load weights from TF model
  snake_case__						= tf.train.list_variables(__lowerCAmelCase				)
  snake_case__						= []
  snake_case__						= []
  snake_case__						= []
  for full_name, shape in init_vars:
    # logger.info(f"Loading TF weight {name} with shape {shape}")
    snake_case__						= full_name.split('''/'''				)
    if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
      logger.info(F"""Skipping non-model layer {full_name}"""				)
      continue
    if "optimizer" in full_name:
      logger.info(F"""Skipping optimization layer {full_name}"""				)
      continue
    if name[0] == "model":
      # ignore initial 'model'
      snake_case__						= name[1:]
    # figure out how many levels deep the name is
    snake_case__						= 0
    for _name in name:
      if _name.startswith('''layer_with_weights'''				):
        depth += 1
      else:
        break
    layer_depth.append(__lowerCAmelCase				)
    # read data
    snake_case__						= tf.train.load_variable(__lowerCAmelCase							,   __lowerCAmelCase				)
    names.append('''/'''.join(__lowerCAmelCase				)				)
    arrays.append(__lowerCAmelCase				)
  logger.info(F"""Read a total of {len(__lowerCAmelCase				):,} layers"""				)

  # Sanity check
  if len(set(__lowerCAmelCase				)				) != 1:
    raise ValueError(F"""Found layer names with different depths (layer depth {list(set(__lowerCAmelCase				)				)})"""				)
  snake_case__						= list(set(__lowerCAmelCase				)				)[0]
  if layer_depth != 1:
    raise ValueError(
        '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
        ''' heads.'''				)

  # convert layers
  logger.info('''Converting weights...'''				)
  for full_name, array in zip(__lowerCAmelCase							,   __lowerCAmelCase				):
    snake_case__						= full_name.split('''/'''				)
    snake_case__						= model
    snake_case__						= []
    for i, m_name in enumerate(__lowerCAmelCase				):
      if m_name == ".ATTRIBUTES":
        # variable names end with .ATTRIBUTES/VARIABLE_VALUE
        break
      if m_name.startswith('''layer_with_weights'''				):
        snake_case__						= int(m_name.split('''-'''				)[-1]				)
        if layer_num <= 2:
          # embedding layers
          # layer_num 0: word_embeddings
          # layer_num 1: position_embeddings
          # layer_num 2: token_type_embeddings
          continue
        elif layer_num == 3:
          # embedding LayerNorm
          trace.extend(['''embeddings''', '''LayerNorm''']				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''embeddings'''				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''LayerNorm'''				)
        elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
          # encoder layers
          trace.extend(['''encoder''', '''layer''', str(layer_num - 4				)]				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''encoder'''				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''layer'''				)
          snake_case__						= pointer[layer_num - 4]
        elif layer_num == config.num_hidden_layers + 4:
          # pooler layer
          trace.extend(['''pooler''', '''dense''']				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''pooler'''				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''dense'''				)
      elif m_name == "embeddings":
        trace.append('''embeddings'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''embeddings'''				)
        if layer_num == 0:
          trace.append('''word_embeddings'''				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''word_embeddings'''				)
        elif layer_num == 1:
          trace.append('''position_embeddings'''				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''position_embeddings'''				)
        elif layer_num == 2:
          trace.append('''token_type_embeddings'''				)
          snake_case__						= getattr(__lowerCAmelCase							,   '''token_type_embeddings'''				)
        else:
          raise ValueError(F"""Unknown embedding layer with name {full_name}"""				)
        trace.append('''weight'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''weight'''				)
      elif m_name == "_attention_layer":
        # self-attention layer
        trace.extend(['''attention''', '''self''']				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''attention'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''self'''				)
      elif m_name == "_attention_layer_norm":
        # output attention norm
        trace.extend(['''attention''', '''output''', '''LayerNorm''']				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''attention'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''output'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''LayerNorm'''				)
      elif m_name == "_attention_output_dense":
        # output attention dense
        trace.extend(['''attention''', '''output''', '''dense''']				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''attention'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''output'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''dense'''				)
      elif m_name == "_output_dense":
        # output dense
        trace.extend(['''output''', '''dense''']				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''output'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''dense'''				)
      elif m_name == "_output_layer_norm":
        # output dense
        trace.extend(['''output''', '''LayerNorm''']				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''output'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''LayerNorm'''				)
      elif m_name == "_key_dense":
        # attention key
        trace.append('''key'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''key'''				)
      elif m_name == "_query_dense":
        # attention query
        trace.append('''query'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''query'''				)
      elif m_name == "_value_dense":
        # attention value
        trace.append('''value'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''value'''				)
      elif m_name == "_intermediate_dense":
        # attention intermediate dense
        trace.extend(['''intermediate''', '''dense''']				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''intermediate'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''dense'''				)
      elif m_name == "_output_layer_norm":
        # output layer norm
        trace.append('''output'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''output'''				)
      # weights & biases
      elif m_name in ["bias", "beta"]:
        trace.append('''bias'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''bias'''				)
      elif m_name in ["kernel", "gamma"]:
        trace.append('''weight'''				)
        snake_case__						= getattr(__lowerCAmelCase							,   '''weight'''				)
      else:
        logger.warning(F"""Ignored {m_name}"""				)
        # for certain layers reshape is necessary
    snake_case__						= '''.'''.join(__lowerCAmelCase				)
    if re.match(r'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''							,   __lowerCAmelCase				) or re.match(
        r'''(\S+)\.attention\.output\.dense\.weight'''							,   __lowerCAmelCase				):
      snake_case__						= array.reshape(pointer.data.shape				)
    if "kernel" in full_name:
      snake_case__						= array.transpose()
    if pointer.shape == array.shape:
      snake_case__						= torch.from_numpy(__lowerCAmelCase				)
    else:
      raise ValueError(
          F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
          F""" {array.shape}"""				)
    logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}"""				)
  return model







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[Any]:
  # Instantiate model
  logger.info(F"""Loading model based on config from {config_path}..."""				)
  snake_case__						= BertConfig.from_json_file(__lowerCAmelCase				)
  snake_case__						= BertModel(__lowerCAmelCase				)

  # Load weights from checkpoint
  logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}..."""				)
  load_tfa_weights_in_bert(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)

  # Save pytorch-model
  logger.info(F"""Saving PyTorch model to {pytorch_dump_path}..."""				)
  torch.save(model.state_dict()							,   __lowerCAmelCase				)


if __name__ == "__main__":
   lowerCamelCase__							:						Dict							     =   argparse.ArgumentParser()
   parser.add_argument(
       """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
   )
   parser.add_argument(
       """--bert_config_file""",
       type=str,
       required=True,
       help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
   )
   parser.add_argument(
       """--pytorch_dump_path""",
       type=str,
       required=True,
       help="""Path to the output PyTorch model (must include filename).""",
   )
   lowerCamelCase__							:						Tuple							     =   parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							str	   =       'AutoImageProcessor'
     __lowercase				:							Dict	   =       'AutoTokenizer'

     def __init__(					self:int     ,		_a:List[str]=None     ,		_a:Optional[Any]=None     ,		**_a:List[str]   ):
       snake_case__						= None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''     ,		_a     ,		)
         snake_case__						= kwargs.pop('''feature_extractor'''   )

       snake_case__						= image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''   )
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''   )

       super().__init__(_a     ,		_a   )
       snake_case__						= self.image_processor
       snake_case__						= False

     def __call__(					self:Optional[int]     ,		*_a:str     ,		**_a:int   ):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor(*_a     ,		**_a   )

       snake_case__						= kwargs.pop('''images'''     ,		_a   )
       snake_case__						= kwargs.pop('''text'''     ,		_a   )
       if len(_a   ) > 0:
         snake_case__						= args[0]
         snake_case__						= args[1:]

       if images is None and text is None:
         raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

       if images is not None:
         snake_case__						= self.image_processor(_a     ,		*_a     ,		**_a   )
       if text is not None:
         snake_case__						= self.tokenizer(_a     ,		**_a   )

       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         snake_case__						= encodings['''input_ids''']
         return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		*_a:Union[str, Any]     ,		**_a:Any   ):
       return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		*_a:Union[str, Any]     ,		**_a:Optional[int]   ):
       return self.tokenizer.decode(*_a     ,		**_a   )

     @contextmanager
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       warnings.warn(
           '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
           '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
           '''your images inputs, or in a separate call.'''   )
       snake_case__						= True
       snake_case__						= self.tokenizer
       yield
       snake_case__						= self.image_processor
       snake_case__						= False

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Dict     ,		_a:Dict=False     ,		_a:Optional[int]=None   ):
       if added_vocab is None:
         snake_case__						= self.tokenizer.get_added_vocab()

       snake_case__						= {}

       while tokens:
         snake_case__						= re.search(r'''<s_(.*?)>'''     ,		_a     ,		re.IGNORECASE   )
         if start_token is None:
           break
         snake_case__						= start_token.group(1   )
         snake_case__						= re.search(rF"""</s_{key}>"""     ,		_a     ,		re.IGNORECASE   )
         snake_case__						= start_token.group()
         if end_token is None:
           snake_case__						= tokens.replace(_a     ,		''''''   )
         else:
           snake_case__						= end_token.group()
           snake_case__						= re.escape(_a   )
           snake_case__						= re.escape(_a   )
           snake_case__						= re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""     ,		_a     ,		re.IGNORECASE   )
           if content is not None:
             snake_case__						= content.group(1   ).strip()
             if r"<s_" in content and r"</s_" in content:  # non-leaf node
               snake_case__						= self.tokenajson(_a     ,		is_inner_value=_a     ,		added_vocab=_a   )
               if value:
                 if len(_a   ) == 1:
                   snake_case__						= value[0]
                 snake_case__						= value
             else:  # leaf nodes
               snake_case__						= []
               for leaf in content.split(r'''<sep/>'''   ):
                 snake_case__						= leaf.strip()
                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                   snake_case__						= leaf[1:-2]  # for categorical special tokens
                 output[key].append(_a   )
               if len(output[key]   ) == 1:
                 snake_case__						= output[key][0]

           snake_case__						= tokens[tokens.find(_a   ) + len(_a   ) :].strip()
           if tokens[:6] == r"<sep/>":  # non-leaf nodes
             return [output] + self.tokenajson(tokens[6:]     ,		is_inner_value=_a     ,		added_vocab=_a   )

       if len(_a   ):
         return [output] if is_inner_value else output
       else:
         return [] if is_inner_value else {"text_sequence": tokens}

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,		_a     ,		)
       return self.image_processor_class







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		_a     ,		)
       return self.image_processor

