


"""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"""
from __future__ import annotations

from queue import Queue






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							a ^= b
							b ^= a
							a ^= b
							return a, b






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    1
							while (1 << j) < max_node:
														for i in range(1   ,    max_node + 1			):
																					A						=    parent[j - 1][parent[j - 1][i]]
														j += 1
							return parent






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							# u must be deeper in the tree than v
							if level[u] < level[v]:
														A,      A						=    swap(UpperCamelCase   ,    UpperCamelCase			)
							# making depth of u same as depth of v
							for i in range(18   ,    -1   ,    -1			):
														if level[u] - (1 << i) >= level[v]:
																					A						=    parent[i][u]
    # at the same depth if u==v that mean lca is found
							if u == v:
														return u
							# moving both nodes upwards till lca in found
							for i in range(18   ,    -1   ,    -1			):
														if parent[i][u] not in [0, parent[i][v]]:
																					A,      A						=    parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
							return parent[0][u]






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = 1   ,    ):
							A						=    0
							A						=    Queue(maxsize=UpperCamelCase			)
							q.put(UpperCamelCase			)
							while q.qsize() != 0:
														A						=    q.get()
														for v in graph[u]:
																					if level[v] == -1:
																												A						=    level[u] + 1
																												q.put(UpperCamelCase			)
																												A						=    u
							return level, parent






def    A__							(     ):
							A						=    13
							# initializing with 0
							A						=    [[0 for _ in range(max_node + 10			)] for _ in range(20			)]
							# initializing with -1 which means every node is unvisited
							A						=    [-1 for _ in range(max_node + 10			)]
							A						=    {
							    1: [2, 3, 4],
							    2: [5],
							    3: [6, 7],
							    4: [8],
							    5: [9, 10],
							    6: [11],
							    7: [],
							    8: [12, 13],
							    9: [],
							    10: [],
							    11: [],
							    12: [],
							    13: [],
							}
							A,      A						=    breadth_first_search(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    1			)
							A						=    create_sparse(UpperCamelCase   ,    UpperCamelCase			)
							print("LCA of node 1 and 3 is: "   ,    lowest_common_ancestor(1   ,    3   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 5 and 6 is: "   ,    lowest_common_ancestor(5   ,    6   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 7 and 11 is: "   ,    lowest_common_ancestor(7   ,    11   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 6 and 7 is: "   ,    lowest_common_ancestor(6   ,    7   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 4 and 12 is: "   ,    lowest_common_ancestor(4   ,    12   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 8 and 8 is: "   ,    lowest_common_ancestor(8   ,    8   ,    UpperCamelCase   ,    UpperCamelCase			)			)


if __name__ == "__main__":
		main()


