


"""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


SCREAMING_SNAKE_CASE					:       Dict							   =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE					:       Optional[int]							   =							{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE					:       Any							   =							{
    """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					:       Dict							   =							{
    """openai/clip-vit-base-patch32""": 77,
}



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

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



		def __init__(self							,			a_=None							,			a_=None							,			a_=None							,			a_="<|endoftext|>"							,			a_="<|startoftext|>"							,			a_="<|endoftext|>"							,			a_="<|endoftext|>"							,			**a_							,			):





			'''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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							self.backend_tokenizer.decode

			def new_decode_method(*a_							,			**a_   ):
				__snake_case      :		List[str]         =							orig_decode_method(*a_							,			**a_   )
				__snake_case      :		Optional[Any]         =							text.replace(self.backend_tokenizer.model.end_of_word_suffix							,			''' '''   ).strip()
				return text

			__snake_case      :		Dict         =							new_decode_method



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = None   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							[self.bos_token_id]
			__snake_case      :		str         =							[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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = None   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							[self.bos_token_id]
			__snake_case      :		str         =							[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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = None   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							self._tokenizer.model.save(a_							,			name=a_   )
			return tuple(a_   )









"""simple docstring"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) and precision > 0
	), f"""precision should be positive integer your input : {precision}"""

	# just applying the formula of simpson for approximate integration written in
	# mentioned article in first comment of this file and above this function

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

	result *= h / 3
	return round(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





