'''simple docstring'''




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str    =				c_float

       @property
       def 					__A       (      self )		->     int:
           return sizeof(self.value )





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	int,   _UpperCamelCase    :	int = 0   )	->      int:
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
        A_     =   fixed_dimension

    dimension -= num_token_to_add
    return dimension





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	ParameterFormat   )	->      int:
    return num_parameters * dtype.size





def      _UpperCAmelCase    (							_UpperCamelCase    :	str   )	->      Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(_UpperCamelCase   )
    except (ValueError, OSError, KeyError):
        A_   ,A_     =   None, None
        try:
            A_     =   AutoTokenizer.from_pretrained(_UpperCamelCase   )
        except (OSError, KeyError):
            pass
        try:
            A_     =   AutoFeatureExtractor.from_pretrained(_UpperCamelCase   )
        except (OSError, KeyError):
            pass

        if tokenizer is not None and feature_extractor is not None:
            raise ValueError(
                F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''   )
        elif tokenizer is None and feature_extractor is None:
            return None
        elif tokenizer is not None:
            return tokenizer
        else:
            return feature_extractor







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


lowerCAmelCase__							= logging.get_logger(__name__)

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

lowerCAmelCase__							= {
    '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'
        ),
    },
}

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






 '''simple docstring'''






 UpperCAmelCase_	 =					VOCAB_FILES_NAMES
 UpperCAmelCase_	 =					PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_	 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_	 =					['input_ids', 'attention_mask']
 UpperCAmelCase_	 =					CLIPTokenizer




 def __init__(			self					:  Optional[int]			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  str="<|endoftext|>"			,    lowercase__					:  List[str]="<|startoftext|>"			,    lowercase__					:  Union[str, Any]="<|endoftext|>"			,    lowercase__					:  Any="<|endoftext|>"			,    **lowercase__					:  Any			,    ):







        '''simple docstring'''



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

        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[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.backend_tokenizer.decode

        def new_decode_method(*lowercase__					:  str			,    **lowercase__					:  int):
               lowerCAmelCase__     	=					orig_decode_method(*lowercase__			,    **lowercase__)
               lowerCAmelCase__     	=					text.replace(self.backend_tokenizer.model.end_of_word_suffix			,    ' ').strip()
               return text

        lowerCAmelCase__     	=					new_decode_method




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[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					:  Any			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[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					:  Dict			,    lowercase__					:  str			,    lowercase__					:  Optional[str] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self._tokenizer.model.save(lowercase__			,    name=lowercase__)
        return tuple(lowercase__)
