




'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase_    	=				{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase_    	=				{
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Tuple        =						VOCAB_FILES_NAMES
       A						:				Optional[int]        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				List[str]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				Union[str, Any]        =						['''input_ids''', '''attention_mask''']
       A						:				List[Any]        =						RobertaTokenizer







       def __init__(      self,							A=None,							A=None,							A=None,							A="replace",							A="<s>",							A="</s>",							A="</s>",							A="<s>",							A="<unk>",							A="<pad>",							A="<mask>",							A=False,							A=True,							**A,							):







             '''simple docstring'''







             super().__init__(
                 A,							A,							tokenizer_file=A,							errors=A,							bos_token=A,							eos_token=A,							sep_token=A,							cls_token=A,							unk_token=A,							pad_token=A,							mask_token=A,							add_prefix_space=A,							trim_offsets=A,							**A,							)

             SCREAMING_SNAKE_CASE   :      Dict    					=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
             if pre_tok_state.get('add_prefix_space',							A  ) != add_prefix_space:
                   SCREAMING_SNAKE_CASE   :      Any    					=						getattr(A,							pre_tok_state.pop('type'  )  )
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						add_prefix_space
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						pre_tok_class(**A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						add_prefix_space

             SCREAMING_SNAKE_CASE   :      str    					=						'post_processor'
             SCREAMING_SNAKE_CASE   :      str    					=						getattr(self.backend_tokenizer,							A,							A  )
             if tokenizer_component_instance:
                   SCREAMING_SNAKE_CASE   :      int    					=						json.loads(tokenizer_component_instance.__getstate__()  )

                   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                   if "sep" in state:
                         SCREAMING_SNAKE_CASE   :      str    					=						tuple(state['sep']  )
                   if "cls" in state:
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tuple(state['cls']  )

                   SCREAMING_SNAKE_CASE   :      List[str]    					=						False

                   if state.get('add_prefix_space',							A  ) != add_prefix_space:
                         SCREAMING_SNAKE_CASE   :      int    					=						add_prefix_space
                         SCREAMING_SNAKE_CASE   :      Any    					=						True

                   if state.get('trim_offsets',							A  ) != trim_offsets:
                         SCREAMING_SNAKE_CASE   :      Dict    					=						trim_offsets
                         SCREAMING_SNAKE_CASE   :      int    					=						True

                   if changes_to_apply:
                         SCREAMING_SNAKE_CASE   :      str    					=						getattr(A,							state.pop('type'  )  )
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						component_class(**A  )
                         setattr(self.backend_tokenizer,							A,							A  )







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if self._mask_token is None:
                   if self.verbose:
                         logger.error('Using mask_token, but it is not set yet.'  )
                   return None
             return str(self._mask_token  )







       @mask_token.setter
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						AddedToken(A,							lstrip=A,							rstrip=A  ) if isinstance(A,							A  ) else value
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						value







       def        UpperCamelCase_							(      self,							*A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						kwargs.get('is_split_into_words',							A  )
             assert self.add_prefix_space or not is_split_into_words, (
                 F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                 "to use it with pretokenized inputs."
             )

             return super()._batch_encode_plus(*A,							**A  )







       def        UpperCamelCase_							(      self,							*A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						kwargs.get('is_split_into_words',							A  )

             assert self.add_prefix_space or not is_split_into_words, (
                 F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                 "to use it with pretokenized inputs."
             )

             return super()._encode_plus(*A,							**A  )







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A=None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                   return output

             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						[self.sep_token_id]
             SCREAMING_SNAKE_CASE   :      Dict    					=						[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 + sep + token_ids_a + sep  ) * [0]




'''simple docstring'''



from manim import *




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					Rectangle(height=0.5       ,  width=0.5 )
												_lowerCAmelCase    		=					Rectangle(height=0.46       ,  width=0.46 ).set_stroke(width=0 )

												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					VGroup(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					Text("""CPU"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												cpu.move_to([-2.5, -0.5, 0] )
												self.add(_lowercase )

												_lowerCAmelCase    		=					[mem.copy() for i in range(1 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )
												_lowerCAmelCase    		=					Text("""GPU"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												gpu.align_to(_lowercase       ,  _lowercase )
												gpu.set_x(gpu.get_x() - 1 )

												self.add(_lowercase )

												_lowerCAmelCase    		=					[mem.copy() for i in range(6 )]
												_lowerCAmelCase    		=					VGroup(*_lowercase ).arrange(_lowercase       ,  buff=0 )

												_lowerCAmelCase    		=					Text("""Model"""       ,  font_size=24 )
												_lowerCAmelCase    		=					Group(_lowercase       ,  _lowercase ).arrange(_lowercase       ,  buff=0.5       ,  aligned_edge=_lowercase )
												model.move_to([3, -1.0, 0] )

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

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

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

												_lowerCAmelCase    		=					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(_lowercase       ,  run_time=2.5 )       ,  Write(_lowercase )       ,  Write(_lowercase ) )

												self.add(_lowercase )

												_lowerCAmelCase    		=					[]
												_lowerCAmelCase    		=					[]
												_lowerCAmelCase    		=					[]
												for i, rect in enumerate(_lowercase ):
																		_lowerCAmelCase    		=					Rectangle(height=0.46       ,  width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase       ,  opacity=0.7 )
																		cpu_target.move_to(_lowercase )
																		cpu_target.generate_target()
																		_lowerCAmelCase    		=					0.46 / 4
																		_lowerCAmelCase    		=					0.46 / 3

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

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

												self.play(*_lowercase )
												self.play(*_lowercase )

												self.wait()
