
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch






class     UpperCAmelCase						(		A_					):
   A__   :						Optional[Any]			=	"char"
   A__   :						Any			=	"bpe"
   A__   :						Optional[int]			=	"wp"



__lowerCamelCase				=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class     UpperCAmelCase						(		A_					):
   A__   :						int			=	["image_processor", "char_tokenizer"]
   A__   :						int			=	"ViTImageProcessor"
   A__   :						Any			=	"MgpstrTokenizer"



   def __init__(self						:   str       ,		snake_case__						:   Dict=None       ,		snake_case__						:   int=None       ,		**snake_case__						:   Any					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Tuple     							= None
          if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."       ,		snake_case__       ,		)
                 snake_case			:  Optional[Any]     							= kwargs.pop("feature_extractor"					)

          snake_case			:  Tuple     							= 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`."					)

          snake_case			:  List[str]     							= tokenizer
          snake_case			:  Any     							= AutoTokenizer.from_pretrained("gpt2"					)
          snake_case			:  List[str]     							= AutoTokenizer.from_pretrained("bert-base-uncased"					)

          super().__init__(snake_case__       ,		snake_case__					)



   def __call__(self						:   Dict       ,		snake_case__						:   int=None       ,		snake_case__						:   Dict=None       ,		snake_case__						:   Optional[int]=None       ,		**snake_case__						:   int					)						->      Optional[Any]:


          '''simple docstring'''


          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			:  List[Any]     							= self.image_processor(snake_case__       ,		return_tensors=snake_case__       ,		**snake_case__					)
          if text is not None:
                 snake_case			:  Optional[int]     							= self.char_tokenizer(snake_case__       ,		return_tensors=snake_case__       ,		**snake_case__					)

          if text is None:
                 return inputs
          elif images is None:
                 return encodings
          else:
                 snake_case			:  Dict     							= encodings["input_ids"]
                 return inputs



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   Optional[int]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case							,   snake_case							,   snake_case			:  Dict     							= sequences
          snake_case			:  List[Any]     							= char_preds.size(0					)

          snake_case							,   snake_case			:  List[Any]     							= self._decode_helper(snake_case__       ,		"char"					)
          snake_case							,   snake_case			:  Optional[int]     							= self._decode_helper(snake_case__       ,		"bpe"					)
          snake_case							,   snake_case			:  Any     							= self._decode_helper(snake_case__       ,		"wp"					)

          snake_case			:  List[str]     							= []
          snake_case			:  List[str]     							= []
          for i in range(snake_case__					):
                 snake_case			:  Optional[Any]     							= [char_scores[i], bpe_scores[i], wp_scores[i]]
                 snake_case			:  Optional[int]     							= [char_strs[i], bpe_strs[i], wp_strs[i]]
                 snake_case			:  Dict     							= scores.index(max(snake_case__					)					)
                 final_strs.append(strs[max_score_index]					)
                 final_scores.append(scores[max_score_index]					)

          snake_case			:  Union[str, Any]     							= {}
          snake_case			:  Optional[int]     							= final_strs
          snake_case			:  str     							= final_scores
          snake_case			:  Optional[Any]     							= char_strs
          snake_case			:  List[str]     							= bpe_strs
          snake_case			:  int     							= wp_strs
          return out



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Optional[int]					)						->      Tuple:


          '''simple docstring'''


          if format == DecodeType.CHARACTER:
                 snake_case			:  List[Any]     							= self.char_decode
                 snake_case			:  List[str]     							= 1
                 snake_case			:  int     							= "[s]"
          elif format == DecodeType.BPE:
                 snake_case			:  List[str]     							= self.bpe_decode
                 snake_case			:  Dict     							= 2
                 snake_case			:  Union[str, Any]     							= "#"
          elif format == DecodeType.WORDPIECE:
                 snake_case			:  Optional[Any]     							= self.wp_decode
                 snake_case			:  str     							= 1_02
                 snake_case			:  Any     							= "[SEP]"
          else:
                 raise ValueError(f"""Format {format} is not supported."""					)

          snake_case							,   snake_case			:  Optional[Any]     							= [], []
          snake_case			:  Tuple     							= pred_logits.size(0					)
          snake_case			:  List[Any]     							= pred_logits.size(1					)
          snake_case							,   snake_case			:  int     							= pred_logits.topk(1       ,		dim=-1       ,		largest=snake_case__       ,		sorted=snake_case__					)
          snake_case			:  str     							= preds_index.view(-1       ,		snake_case__					)[:, 1:]
          snake_case			:  Tuple     							= decoder(snake_case__					)
          snake_case							,   snake_case			:  str     							= torch.nn.functional.softmax(snake_case__       ,		dim=2					).max(dim=2					)
          snake_case			:  Dict     							= preds_max_prob[:, 1:]

          for index in range(snake_case__					):
                 snake_case			:  Optional[Any]     							= preds_str[index].find(snake_case__					)
                 snake_case			:  List[str]     							= preds_str[index][:pred_eos]
                 snake_case			:  List[str]     							= preds_index[index].cpu().tolist()
                 snake_case			:  Optional[Any]     							= pred_index.index(snake_case__					) if eos_token in pred_index else -1
                 snake_case			:  Tuple     							= preds_max_prob[index][: pred_eos_index + 1]
                 snake_case			:  Optional[Any]     							= pred_max_prob.cumprod(dim=0					)[-1] if pred_max_prob.nelement() != 0 else 0.0
                 dec_strs.append(snake_case__					)
                 conf_scores.append(snake_case__					)

          return dec_strs, conf_scores



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   Optional[int]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  str     							= [seq.replace(" "       ,		""					) for seq in self.char_tokenizer.batch_decode(snake_case__					)]
          return decode_strs



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   int					)						->      List[Any]:


          '''simple docstring'''


          return self.bpe_tokenizer.batch_decode(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   List[str]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= [seq.replace(" "       ,		""					) for seq in self.wp_tokenizer.batch_decode(snake_case__					)]
          return decode_strs







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,   __lowerCamelCase       )


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--bert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained BERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





