
'''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


A			      =      logging.get_logger(__name__)

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

A			      =      {
    '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'
        ),
    },
}

A			      =      {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					_lowerCAmelCase			     =	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,    )

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

									lowerCamelCase					:			Optional[int]									= add_prefix_space

									lowerCamelCase					:			int									= 'post_processor'
									lowerCamelCase					:			Dict									= getattr(self.backend_tokenizer,    A,    A      )
									if tokenizer_component_instance:
													lowerCamelCase					:			Union[str, Any]									= 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:
																	lowerCamelCase					:			Union[str, Any]									= tuple(state['sep']      )
													if "cls" in state:
																	lowerCamelCase					:			Any									= tuple(state['cls']      )

													lowerCamelCase					:			Dict									= False

													if state.get('add_prefix_space',    A      ) != add_prefix_space:
																	lowerCamelCase					:			Tuple									= add_prefix_space
																	lowerCamelCase					:			int									= True

													if state.get('trim_offsets',    A      ) != trim_offsets:
																	lowerCamelCase					:			List[str]									= trim_offsets
																	lowerCamelCase					:			str									= True

													if changes_to_apply:
																	lowerCamelCase					:			Any									= getattr(A,    state.pop('type'      )      )
																	lowerCamelCase					:			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"""


									lowerCamelCase					:			Optional[Any]									= AddedToken(A,    lstrip=A,    rstrip=A      ) if isinstance(A,    A      ) else value
									lowerCamelCase					:			List[Any]									= value
					def     UpperCAmelCase_							(     self,    *A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= 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"""


									lowerCamelCase					:			Any									= 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"""


									lowerCamelCase					:			Optional[Any]									= self._tokenizer.model.save(A,    name=A      )
									return tuple(A      )
					def     UpperCAmelCase_							(     self,    A,    A=None      ):

									"""simple docstring"""


									lowerCamelCase					:			List[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"""


									lowerCamelCase					:			Union[str, Any]									= [self.sep_token_id]
									lowerCamelCase					:			List[Any]									= [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     __snake_case		(   a__):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= Rectangle(height=0.5,    width=0.5      )
									lowerCamelCase					:			List[Any]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0      )

									lowerCamelCase					:			List[str]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			List[Any]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			str									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Any									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Dict									= VGroup(A,    A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			str									= Text('CPU',    font_size=24      )
									lowerCamelCase					:			int									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									cpu.move_to([-2.5, -0.5, 0]      )
									self.add(A      )

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

									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			Optional[Any]									= VGroup(*A      ).arrange(A,    buff=0      )

									lowerCamelCase					:			Any									= Text('Model',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									model.move_to([3, -1.0, 0]      )

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

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

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

									lowerCamelCase					:			Tuple									= 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(A,    run_time=2.5      ),    Write(A      ),    Write(A      )      )

									self.add(A      )

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

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

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

									self.play(*A      )
									self.play(*A      )

									self.wait()
