





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A							:			Union[str, Any]         =			logging.get_logger(__name__)

__A							:			Optional[Any]         =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A							:			Any         =			{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A							:			Union[str, Any]         =			{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A							:			List[str]         =			{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			VOCAB_FILES_NAMES
     __magic_name__   =			PRETRAINED_VOCAB_FILES_MAP
     __magic_name__   =			PRETRAINED_INIT_CONFIGURATION
     __magic_name__   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__   =			SqueezeBertTokenizer




     def __init__(    self				,     snake_case_=None				,     snake_case_=None				,     snake_case_=True				,     snake_case_="[UNK]"				,     snake_case_="[SEP]"				,     snake_case_="[PAD]"				,     snake_case_="[CLS]"				,     snake_case_="[MASK]"				,     snake_case_=True				,     snake_case_=None				,     **snake_case_				,     ):
         super().__init__(
             snake_case_				,     tokenizer_file=snake_case_				,     do_lower_case=snake_case_				,     unk_token=snake_case_				,     sep_token=snake_case_				,     pad_token=snake_case_				,     cls_token=snake_case_				,     mask_token=snake_case_				,     tokenize_chinese_chars=snake_case_				,     strip_accents=snake_case_				,     **snake_case_				,     )

         _A     =							json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
         if (
             normalizer_state.get('lowercase'				,     snake_case_							) != do_lower_case
             or normalizer_state.get('strip_accents'				,     snake_case_							) != strip_accents
             or normalizer_state.get('handle_chinese_chars'				,     snake_case_							) != tokenize_chinese_chars
         ):
             _A     =							getattr(snake_case_				,     normalizer_state.pop('type'							)							)
             _A     =							do_lower_case
             _A     =							strip_accents
             _A     =							tokenize_chinese_chars
             _A     =							normalizer_class(**snake_case_							)

         _A     =							do_lower_case




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=None							):
         _A     =							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							[self.sep_token_id]
         _A     =							[self.cls_token_id]
         if token_ids_a is None:
             return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							self._tokenizer.model.save(snake_case_				,     name=snake_case_							)
         return tuple(snake_case_							)









"""simple docstring"""



from manim import *







class _lowerCAmelCase							( snake_case_      ):







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : List[str]    =       Rectangle(height=0.5		,      width=0.5		)
       snake_case       : Dict    =       Rectangle(height=0.46		,      width=0.46		).set_stroke(width=0		)

       snake_case       : Tuple    =       [mem.copy() for i in range(6		)]
       snake_case       : Any    =       [mem.copy() for i in range(6		)]
       snake_case       : Tuple    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[Any]    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[Any]    =       VGroup(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[Any]    =       Text("CPU"		,      font_size=24		)
       snake_case       : Optional[int]    =       Group(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0.5		,      aligned_edge=UpperCamelCase__		)
       cpu.move_to([-2.5, -0.5, 0]		)
       self.add(UpperCamelCase__		)

       snake_case       : Optional[Any]    =       [mem.copy() for i in range(1		)]
       snake_case       : List[str]    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)
       snake_case       : Optional[int]    =       Text("GPU"		,      font_size=24		)
       snake_case       : Tuple    =       Group(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0.5		,      aligned_edge=UpperCamelCase__		)
       gpu.align_to(UpperCamelCase__		,      UpperCamelCase__		)
       gpu.set_x(gpu.get_x() - 1		)

       self.add(UpperCamelCase__		)

       snake_case       : Any    =       [mem.copy() for i in range(6		)]
       snake_case       : Any    =       VGroup(*UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0		)

       snake_case       : Dict    =       Text("Model"		,      font_size=24		)
       snake_case       : Optional[Any]    =       Group(UpperCamelCase__		,      UpperCamelCase__		).arrange(UpperCamelCase__		,      buff=0.5		,      aligned_edge=UpperCamelCase__		)
       model.move_to([3, -1.0, 0]		)

       self.play(
           Create(UpperCamelCase__		,      run_time=1		)		,      Create(UpperCamelCase__		,      run_time=1		)		,      Create(UpperCamelCase__		,      run_time=1		)		,      )

       snake_case       : Optional[Any]    =       MarkupText(
           F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'		,      font_size=24		,      )

       snake_case       : Any    =       Square(side_length=2.2		)
       key.move_to([-5, 2, 0]		)

       snake_case       : Any    =       MarkupText(
           F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'		,      font_size=18		,      )

       key_text.move_to([-5, 2.4, 0]		)

       step_a.move_to([2, 2, 0]		)
       self.play(Write(UpperCamelCase__		,      run_time=2.5		)		,      Write(UpperCamelCase__		)		,      Write(UpperCamelCase__		)		)

       self.add(UpperCamelCase__		)

       snake_case       : Optional[Any]    =       []
       snake_case       : Dict    =       []
       snake_case       : Union[str, Any]    =       []
       for i, rect in enumerate(UpperCamelCase__		):
           snake_case       : Any    =       Rectangle(height=0.46		,      width=0.46		).set_stroke(width=0.0		).set_fill(UpperCamelCase__		,      opacity=0.7		)
           cpu_target.move_to(UpperCamelCase__		)
           cpu_target.generate_target()
           snake_case       : Optional[int]    =       0.46 / 4
           snake_case       : Any    =       0.46 / 3

           if i == 0:
               cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)		,      buff=0.02		,      direction=UpperCamelCase__		)
               cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
           elif i == 3:
               cpu_target.target.next_to(cpu_targs[0].target		,      direction=UpperCamelCase__		,      buff=0.0		)
           else:
               cpu_target.target.next_to(cpu_targs[i - 1].target		,      direction=UpperCamelCase__		,      buff=0.0		)
           cpu_targs.append(UpperCamelCase__		)

           first_animations.append(rect.animate(run_time=0.5		).set_stroke(UpperCamelCase__		)		)
           second_animations.append(MoveToTarget(UpperCamelCase__		,      run_time=1.5		)		)

       self.play(*UpperCamelCase__		)
       self.play(*UpperCamelCase__		)

       self.wait()





