






from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE								=  {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE								=  {
    'openai/clip-vit-base-patch32': 77,
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Any	=   VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:     Dict	=   PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:     int	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   ["input_ids", "attention_mask"]
 _SCREAMING_SNAKE_CASE							:     Dict	=   CLIPTokenizer


 def __init__(		self							,					_A=None							,					_A=None							,					_A=None							,					_A="<|endoftext|>"							,					_A="<|startoftext|>"							,					_A="<|endoftext|>"							,					_A="<|endoftext|>"							,					**_A							,					)	->						Optional[Any]:

     """simple docstring"""



     super().__init__(
         _A							,					_A							,					tokenizer_file=_A							,					unk_token=_A							,					bos_token=_A							,					eos_token=_A							,					pad_token=_A							,					**_A							,					)

     if not isinstance(self.backend_tokenizer.pre_tokenizer							,					pre_tokenizers.Sequence):
         raise ValueError(
             '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
             ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
             ''' to be compatible with this version.The easiest way to do so is'''
             ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
             ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
             ''' transformers.''')

     self._wrap_decode_method_backend_tokenizer()


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

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			self.backend_tokenizer.decode

     def new_decode_method(*_A							,					**_A):
         _UpperCAmelCase			: Any     					=			orig_decode_method(*_A							,					**_A)
         _UpperCAmelCase			: List[Any]     					=			text.replace(self.backend_tokenizer.model.end_of_word_suffix							,					''' ''').strip()
         return text

     _UpperCAmelCase			: Union[str, Any]     					=			new_decode_method


 def 	snake_case__			(		self							,					_A							,					_A = None)	->						List[int]:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			[self.bos_token_id]
     _UpperCAmelCase			: Dict     					=			[self.eos_token_id]

     if token_ids_a is None:
         return bos_token + token_ids_a + eos_token
     return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token


 def 	snake_case__			(		self							,					_A							,					_A = None)	->						List[int]:

     """simple docstring"""



     _UpperCAmelCase			: Union[str, Any]     					=			[self.bos_token_id]
     _UpperCAmelCase			: Tuple     					=			[self.eos_token_id]

     if token_ids_a is None:
         return len(bos_token + token_ids_a + eos_token) * [0]
     return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]


 def 	snake_case__			(		self							,					_A							,					_A = None)	->						Tuple[str]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			self._tokenizer.model.save(_A							,					name=_A)
     return tuple(_A)












def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int	) ->   str:
    if not root or root not in binary_tree_mirror_dictionary:
        return
    _UpperCAmelCase					,	_UpperCAmelCase			: int     					=			binary_tree_mirror_dictionary[root][:2]
    _UpperCAmelCase			: int     					=			[right_child, left_child]
    binary_tree_mirror_dict(__A						,						__A	)
    binary_tree_mirror_dict(__A						,						__A	)

def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int = 1	) ->   dict:
    if not binary_tree:
        raise ValueError('''binary tree cannot be empty'''	)
    if root not in binary_tree:
        _UpperCAmelCase			: Dict     					=			f'''root {root} is not present in the binary_tree'''
        raise ValueError(__A	)
    _UpperCAmelCase			: int     					=			dict(__A	)
    binary_tree_mirror_dict(__A						,						__A	)
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE								=  {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'Binary tree: {binary_tree}')
  SCREAMING_SNAKE_CASE								=  binary_tree_mirror(binary_tree, 5)
  print(F'Binary tree mirror: {binary_tree_mirror_dictionary}')




