
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_  :       Dict					      =     logging.get_logger(__name__)

lowerCamelCase_  :       List[Any]					      =     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :       List[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"""
        ),
    },
}

lowerCamelCase_  :       str					      =     {
    """openai/clip-vit-base-patch32""": 77,
}

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

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

			def __init__(				self       :				int  ,		snake_case_       :				List[str]=None  ,		snake_case_       :				Tuple=None  ,		snake_case_       :				int=None  ,		snake_case_       :				Optional[int]="<|endoftext|>"  ,		snake_case_       :				Union[str, Any]="<|startoftext|>"  ,		snake_case_       :				Dict="<|endoftext|>"  ,		snake_case_       :				List[Any]="<|endoftext|>"  ,		**snake_case_       :				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 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_:							Dict		=  self.backend_tokenizer.decode

							def new_decode_method(*snake_case_       :				str  ,		**snake_case_       :				int					):
											UpperCamelCase_:							List[Any]		=  orig_decode_method(*snake_case_  ,		**snake_case_					)
											UpperCamelCase_:							Dict		=  text.replace(self.backend_tokenizer.model.end_of_word_suffix  ,		""" """					).strip()
											return text

							UpperCamelCase_:							Optional[int]		=  new_decode_method

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








def 				A__    (  lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  len(lowerCamelCase  )
				for _ in range(
				    0   ,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
								for j in range(0   ,			no_of_elements - 1  ):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																UpperCamelCase_:							str		=  lst[j + 1]
																UpperCamelCase_:							Tuple		=  lst[j]
																UpperCamelCase_:							str		=  temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																UpperCamelCase_:							Union[str, Any]		=  lst[no_of_elements - 1 - j]
																UpperCamelCase_:							Dict		=  lst[no_of_elements - 2 - j]
																UpperCamelCase_:							List[Any]		=  temp
				return lst


if __name__ == "__main__":
							print("""enter the list to be sorted""")
							lowerCamelCase_  :       Union[str, Any]					      =     [int(x) for x in input().split()]  # inputing elements of the list in one line
							lowerCamelCase_  :       Union[str, Any]					      =     double_sort(lst)
							print("""the sorted list is""")
							print(sorted_lst)






