


from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy



class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Tuple       =						"MarkupLMFeatureExtractor"
     UpperCAmelCase__			:	List[str]       =						("MarkupLMTokenizer", "MarkupLMTokenizerFast")
     UpperCAmelCase__			:	Any       =						True





     def __call__(     self       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					int=None     ,						SCREAMING_SNAKE_CASE_       :					str=None     ,						SCREAMING_SNAKE_CASE_       :					int=None     ,						SCREAMING_SNAKE_CASE_       :					List[str]=None     ,						SCREAMING_SNAKE_CASE_       :					Any=None     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					Union[bool, str, PaddingStrategy] = False     ,						SCREAMING_SNAKE_CASE_       :					Union[bool, str, TruncationStrategy] = None     ,						SCREAMING_SNAKE_CASE_       :					Optional[int] = None     ,						SCREAMING_SNAKE_CASE_       :					int = 0     ,						SCREAMING_SNAKE_CASE_       :					Optional[int] = None     ,						SCREAMING_SNAKE_CASE_       :					Optional[bool] = None     ,						SCREAMING_SNAKE_CASE_       :					Optional[bool] = None     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					Optional[Union[str, TensorType]] = None     ,						**SCREAMING_SNAKE_CASE_       :					Any     ,						):
        # first, create nodes and xpaths
        if self.parse_html:
           if html_strings is None:
              raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""				)

           if nodes is not None or xpaths is not None or node_labels is not None:
              raise ValueError(
                  """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""				)

           __lowerCamelCase:					Union[str, Any]	 =						self.feature_extractor(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					List[Any]	 =						features["""nodes"""]
           __lowerCamelCase:					Union[str, Any]	 =						features["""xpaths"""]
        else:
           if html_strings is not None:
              raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""				)
           if nodes is None or xpaths is None:
              raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""				)

        # # second, apply the tokenizer
        if questions is not None and self.parse_html:
           if isinstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				):
              __lowerCamelCase:					Any	 =						[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

        __lowerCamelCase:					str	 =						self.tokenizer(
            text=questions if questions is not None else nodes     ,						text_pair=nodes if questions is not None else None     ,						xpaths=SCREAMING_SNAKE_CASE_     ,						node_labels=SCREAMING_SNAKE_CASE_     ,						add_special_tokens=SCREAMING_SNAKE_CASE_     ,						padding=SCREAMING_SNAKE_CASE_     ,						truncation=SCREAMING_SNAKE_CASE_     ,						max_length=SCREAMING_SNAKE_CASE_     ,						stride=SCREAMING_SNAKE_CASE_     ,						pad_to_multiple_of=SCREAMING_SNAKE_CASE_     ,						return_token_type_ids=SCREAMING_SNAKE_CASE_     ,						return_attention_mask=SCREAMING_SNAKE_CASE_     ,						return_overflowing_tokens=SCREAMING_SNAKE_CASE_     ,						return_special_tokens_mask=SCREAMING_SNAKE_CASE_     ,						return_offsets_mapping=SCREAMING_SNAKE_CASE_     ,						return_length=SCREAMING_SNAKE_CASE_     ,						verbose=SCREAMING_SNAKE_CASE_     ,						return_tensors=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_     ,						)

        return encoded_inputs





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]     ,						*SCREAMING_SNAKE_CASE_       :					int     ,						**SCREAMING_SNAKE_CASE_       :					str				):
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any     ,						*SCREAMING_SNAKE_CASE_       :					Optional[int]     ,						**SCREAMING_SNAKE_CASE_       :					List[str]				):
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_				)







     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        __lowerCamelCase:					Dict	 =						self.tokenizer.model_input_names
        return tokenizer_input_names






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


