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


lowercase 							=							logging.get_logger(__name__)

lowercase 							=							{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase 							=							{
    """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"""
        ),
    },
}

lowercase 							=							{
    """openai/clip-vit-base-patch32""": 7_7,
}





class      __lowercase  (       _A    ):




				'''simple docstring'''


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





				def __init__(						self  :						int	,				_a  :						List[str]=None	,				_a  :						Tuple=None	,				_a  :						int=None	,				_a  :						Optional[int]="<|endoftext|>"	,				_a  :						Union[str, Any]="<|startoftext|>"	,				_a  :						Dict="<|endoftext|>"	,				_a  :						List[Any]="<|endoftext|>"	,				**_a  :						int	,				):
								super().__init__(
								    snake_case_	,				snake_case_	,				tokenizer_file=snake_case_	,				unk_token=snake_case_	,				bos_token=snake_case_	,				eos_token=snake_case_	,				pad_token=snake_case_	,				**snake_case_	,				)

								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 			A_				(						self  :						Optional[Any]							):
								UpperCamelCase__       						=							self.backend_tokenizer.decode

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

								UpperCamelCase__       						=							new_decode_method





				def 			A_				(						self  :						Union[str, Any]	,				_a  :						List[int]	,				_a  :						Optional[List[int]] = None							):
								UpperCamelCase__       						=							[self.bos_token_id]
								UpperCamelCase__       						=							[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 			A_				(						self  :						Optional[Any]	,				_a  :						List[int]	,				_a  :						Optional[List[int]] = None							):
								UpperCamelCase__       						=							[self.bos_token_id]
								UpperCamelCase__       						=							[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 			A_				(						self  :						List[str]	,				_a  :						str	,				_a  :						Optional[str] = None							):
								UpperCamelCase__       						=							self._tokenizer.model.save(snake_case_	,				name=snake_case_							)
								return tuple(snake_case_							)





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowerCamelCase_  :       List[Any]					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				Dict  ,		*snake_case_       :				Optional[int]  ,		**snake_case_       :				List[Any]					):
							warnings.warn(
							    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV2ImageProcessor instead."""  ,		snake_case_  ,		)
							super().__init__(*snake_case_  ,		**snake_case_					)






