



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			{'''vocab_file''': '''vocab.json'''}

__UpperCAmelCase			  =			{
    '''vocab_file''': {
        '''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
    }
}

__UpperCAmelCase			  =			{'''mgp-str''': 27}



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   List[str]  	=    VOCAB_FILES_NAMES
       UpperCAmelCase__							:   Tuple  	=    PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__							:   int  	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_="[GO]",     SCREAMING_SNAKE_CASE_="[GO]",     SCREAMING_SNAKE_CASE_="[s]",     SCREAMING_SNAKE_CASE_="[GO]",     **SCREAMING_SNAKE_CASE_			)	->						int:
          super().__init__(
              unk_token=SCREAMING_SNAKE_CASE_,     bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )

          with open(SCREAMING_SNAKE_CASE_,     encoding='utf-8'			) as vocab_handle:
             UpperCamelCase       :    Optional[int]		      =      json.load(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      {v: k for k, v in self.vocab.items()}
       @property
       def    snake_case_			(     self			)	->						Any:
          return len(self.vocab			)
       def    snake_case_			(     self			)	->						List[Any]:
          return dict(self.vocab,     **self.added_tokens_encoder			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    List[Any]		      =      []
          for s in text:
             char_tokens.extend(SCREAMING_SNAKE_CASE_			)
          return char_tokens
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          return self.vocab.get(SCREAMING_SNAKE_CASE_,     self.vocab.get(self.unk_token			)			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						str:
          return self.decoder.get(SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None			)	->						Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
             logger.error('Vocabulary path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE_			)			)
             return
          UpperCamelCase       :    Dict		      =      os.path.join(
              SCREAMING_SNAKE_CASE_,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

          with open(SCREAMING_SNAKE_CASE_,     'w',     encoding='utf-8'			) as f:
             f.write(json.dumps(self.vocab,     indent=2,     sort_keys=SCREAMING_SNAKE_CASE_,     ensure_ascii=SCREAMING_SNAKE_CASE_			) + '\n'			)

          return (vocab_file,)










'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  _A (					__lowercase		):
		lowercase__:   Any				     =   ['''image_processor''', '''tokenizer''']
		lowercase__:   Any				     =   '''CLIPImageProcessor'''
		lowercase__:   Optional[Any]				     =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

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


					"""simple docstring"""

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

					__snake_case					:      List[str]							      =     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__(__magic_name__  ,      __magic_name__			)

		def __call__(   self					:   int  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Any=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Optional[int]:


					"""simple docstring"""

					if text is None and images is None:
								raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

					if text is not None:
								__snake_case					:      int							      =     self.tokenizer(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if images is not None:
								__snake_case					:      str							      =     self.image_processor(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if text is not None and images is not None:
								__snake_case					:      Union[str, Any]							      =     image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					else:
								return BatchEncoding(data=dict(**__magic_name__			)  ,      tensor_type=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]  ,      *__magic_name__					:   List[Any]  ,      **__magic_name__					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					return self.tokenizer.batch_decode(*__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   List[str]  ,      *__magic_name__					:   Tuple  ,      **__magic_name__					:   List[Any]			)					->    int:


					"""simple docstring"""

					return self.tokenizer.decode(*__magic_name__  ,      **__magic_name__			)

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tokenizer.model_input_names
					__snake_case					:      str							      =     self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

		@property
		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,      __magic_name__  ,      )
					return self.image_processor_class

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Dict:


					"""simple docstring"""

					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,      __magic_name__  ,      )
					return self.image_processor


