'''simple docstring'''




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


_A										= logging.get_logger(__name__)

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

_A										= {
    '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'
        ),
    },
}

_A										= {
    'openai/clip-vit-base-patch32': 7_7,
}


class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							List[str]			     =     VOCAB_FILES_NAMES
   A				:							int			     =     PRETRAINED_VOCAB_FILES_MAP
   A				:							Union[str, Any]			     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A				:							Optional[Any]			     =     ['''input_ids''', '''attention_mask''']
   A				:							Optional[int]			     =     CLIPTokenizer
   def __init__(self							,					_a=None							,					_a=None							,					_a=None							,					_a="<|endoftext|>"							,					_a="<|startoftext|>"							,					_a="<|endoftext|>"							,					_a="<|endoftext|>"							,					**_a							,					)     ->  Tuple:
        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 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							str  					=       self.backend_tokenizer.decode

        def new_decode_method(*_a							,					**_a					):
             lowercase_       :							Optional[int]  					=       orig_decode_method(*_a							,					**_a					)
             lowercase_       :							int  					=       text.replace(self.backend_tokenizer.model.end_of_word_suffix							,					' '					).strip()
             return text

        lowercase_       :							Dict  					=       new_decode_method
   def 		_lowerCamelCase			(self							,					_a							,					_a = None					)     ->  List[int]:
        lowercase_       :							List[str]  					=       [self.bos_token_id]
        lowercase_       :							Tuple  					=       [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 		_lowerCamelCase			(self							,					_a							,					_a = None					)     ->  List[int]:
        lowercase_       :							int  					=       [self.bos_token_id]
        lowercase_       :							int  					=       [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 		_lowerCamelCase			(self							,					_a							,					_a = None					)     ->  Tuple[str]:
        lowercase_       :							str  					=       self._tokenizer.model.save(_a							,					name=_a					)
        return tuple(_a					)





'''simple docstring'''




_A										= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_A										= [{'type': 'code', 'content': INSTALL_CONTENT}]
_A										= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




