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


a_					      = logging.get_logger(__name__)

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

a_					      = {
    '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'
        ),
    },
}

a_					      = {
    'openai/clip-vit-base-patch32': 77,
}





class 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     PRETRAINED_VOCAB_FILES_MAP
 lowercase         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase         =     ['input_ids', 'attention_mask']
 lowercase         =     CLIPTokenizer

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

  """simple docstring"""





  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 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      self.backend_tokenizer.decode

  def new_decode_method(*snake_case			:							Union[str, Any]     ,   **snake_case			:							Union[str, Any]    ):
   UpperCamelCase_      :		Union[str, Any]				=      orig_decode_method(*snake_case     ,   **snake_case    )
   UpperCamelCase_      :		Union[str, Any]				=      text.replace(self.backend_tokenizer.model.end_of_word_suffix     ,   ' '    ).strip()
   return text

  UpperCamelCase_      :		Tuple				=      new_decode_method

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [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 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      [self.bos_token_id]
  UpperCamelCase_      :		List[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			:							Optional[int]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )




from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

a_					      = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Tuple				=      {start}
 UpperCamelCase_      :		Optional[Any]				=      [start]
 UpperCamelCase_      :		Queue				=      Queue()
 queue.put(lowerCamelCase			)
 while not queue.empty():
  UpperCamelCase_      :		Union[str, Any]				=      queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.put(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Dict				=      {start}
 UpperCamelCase_      :		Optional[int]				=      [start]
 UpperCamelCase_      :		Union[str, Any]				=      deque([start]			)
 while queue:
  UpperCamelCase_      :		Optional[int]				=      queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.append(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  str			):
 UpperCamelCase_      :		str				=      F"from __main__ import G, {name}"
 UpperCamelCase_      :		int				=      10000
 UpperCamelCase_      :		Optional[Any]				=      timeit(F"{name}(G, 'A')"						,						setup=lowerCamelCase						,						number=lowerCamelCase			)
 print(F"{name:<35} finished {number} runs in {res:.5f} seconds"			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   benchmark_function('breadth_first_search')
   benchmark_function('breadth_first_search_with_deque')
   # breadth_first_search                finished 10000 runs in 0.20999 seconds
   # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds



