




import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
       from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{'vocab_file': 'spiece.model'}

UpperCAmelCase_									=	{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_									=	{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

UpperCAmelCase_									=	'▁'





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	List[str]    		=   VOCAB_FILES_NAMES
      UpperCAmelCase__      :	int    		=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__      :	List[str]    		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__      :	Any    		=   ['input_ids', 'attention_mask']
      def __init__(				self: Dict    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Union[str, Any]="</s>"    ,       UpperCamelCase_: Optional[Any]="<unk>"    ,       UpperCamelCase_: Any="<pad>"    ,       UpperCamelCase_: Optional[Any]=1_00    ,       UpperCamelCase_: Optional[int]=None    ,       UpperCamelCase_: Optional[Dict[str, Any]] = None    ,       UpperCamelCase_: Optional[int]=True    ,       **UpperCamelCase_: str    ,       ):
             # Add extra_ids to the special token list
             if extra_ids > 0 and additional_special_tokens is None:
                    __lowerCamelCase   =						[F'<extra_id_{i}>' for i in range(UpperCamelCase_   )]
             elif extra_ids > 0 and additional_special_tokens is not None:
                    # Check that we have the right number of extra_id special tokens
                    __lowerCamelCase   =						len(set(filter(lambda UpperCamelCase_   : bool("""extra_id""" in str(UpperCamelCase_   )   )    ,       UpperCamelCase_   )   )   )
                    if extra_tokens != extra_ids:
                           raise ValueError(
                               F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                               """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                               """ tokens"""   )
             if legacy:
                    logger.warning_once(
                        F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
                        """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""   )

             __lowerCamelCase   =						legacy
             __lowerCamelCase   =						{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 eos_token=UpperCamelCase_    ,       unk_token=UpperCamelCase_    ,       pad_token=UpperCamelCase_    ,       extra_ids=UpperCamelCase_    ,       additional_special_tokens=UpperCamelCase_    ,       sp_model_kwargs=self.sp_model_kwargs    ,       legacy=UpperCamelCase_    ,       **UpperCamelCase_    ,       )

             __lowerCamelCase   =						vocab_file
             __lowerCamelCase   =						extra_ids

             __lowerCamelCase   =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
             self.sp_model.Load(UpperCamelCase_   )
      @staticmethod
      def 			lowerCAmelCase__						(				UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Optional[int]   ):
             if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                    __lowerCamelCase   =						TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
                    if init_max_model_length is not None and init_max_model_length != max_model_length:
                           return init_max_model_length
                    elif init_max_model_length is None:
                           warnings.warn(
                               """This tokenizer was incorrectly instantiated with a model max length of"""
                               F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                               """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                               """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                               F' {pretrained_model_name_or_path} automatically truncating your input to'
                               F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                               F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                               """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                               """ instantiate this tokenizer with `model_max_length` set to your preferred value."""    ,       UpperCamelCase_    ,       )

             return max_model_length
      @property
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             return self.sp_model.get_piece_size() + self._extra_ids
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						{self.convert_ids_to_tokens(UpperCamelCase_   ): i for i in range(self.vocab_size   )}
             vocab.update(self.added_tokens_encoder   )
             return vocab
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None    ,       UpperCamelCase_: bool = False   ):
             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=UpperCamelCase_    ,       token_ids_a=UpperCamelCase_    ,       already_has_special_tokens=UpperCamelCase_   )

             # normal case: some special tokens
             if token_ids_a is None:
                    return ([0] * len(UpperCamelCase_   )) + [1]
             return ([0] * len(UpperCamelCase_   )) + [1] + ([0] * len(UpperCamelCase_   )) + [1]
      def 			lowerCAmelCase__						(				self: List[str]   ):
             return list(
                 set(filter(lambda UpperCamelCase_   : bool(re.search(r"""<extra_id_\d+>"""    ,       UpperCamelCase_   )   ) is not None    ,       self.additional_special_tokens   )   )   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             return [self._convert_token_to_id(UpperCamelCase_   ) for token in self.get_sentinel_tokens()]
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: List[int]   ):
             if len(UpperCamelCase_   ) > 0 and token_ids[-1] == self.eos_token_id:
                    warnings.warn(
                        F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                        """ eos tokens being added."""   )
                    return token_ids
             else:
                    return token_ids + [self.eos_token_id]
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None   ):
             __lowerCamelCase   =						[self.eos_token_id]

             if token_ids_a is None:
                    return len(token_ids_a + eos   ) * [0]
             return len(token_ids_a + eos + token_ids_a + eos   ) * [0]
      def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: List[int]    ,       UpperCamelCase_: Optional[List[int]] = None   ):
             __lowerCamelCase   =						self._add_eos_if_not_present(UpperCamelCase_   )
             if token_ids_a is None:
                    return token_ids_a
             else:
                    __lowerCamelCase   =						self._add_eos_if_not_present(UpperCamelCase_   )
                    return token_ids_a + token_ids_a
      def __getstate__(				self: Dict   ):
             __lowerCamelCase   =						self.__dict__.copy()
             __lowerCamelCase   =						None
             return state
      def __setstate__(				self: int    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						d

             # for backward compatibility
             if not hasattr(self    ,       """sp_model_kwargs"""   ):
                    __lowerCamelCase   =						{}

             __lowerCamelCase   =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
             self.sp_model.Load(self.vocab_file   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: "TextInput"    ,       **UpperCamelCase_: Dict   ):
             # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
             # the beginning of the text
             if not self.legacy:
                    __lowerCamelCase   =						SPIECE_UNDERLINE + text.replace(UpperCamelCase_    ,       """ """   )
             return super().tokenize(UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Any    ,       **UpperCamelCase_: List[Any]   ):
             if not self.legacy:
                    __lowerCamelCase   =						text.startswith(UpperCamelCase_   )
                    if is_first:
                           __lowerCamelCase   =						text[1:]

             __lowerCamelCase   =						self.sp_model.encode(UpperCamelCase_    ,       out_type=UpperCamelCase_   )

             if not self.legacy and not is_first and not text.startswith(""" """   ) and tokens[0].startswith(UpperCamelCase_   ):
                    __lowerCamelCase   =						([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
             return tokens
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Tuple   ):
             if token.startswith("""<extra_id_"""   ):
                    __lowerCamelCase   =						re.match(r"""<extra_id_(\d+)>"""    ,       UpperCamelCase_   )
                    __lowerCamelCase   =						int(match.group(1   )   )
                    return self.vocab_size - num - 1
             return self.sp_model.piece_to_id(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[int]   ):
             if index < self.sp_model.get_piece_size():
                    __lowerCamelCase   =						self.sp_model.IdToPiece(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						F'<extra_id_{self.vocab_size - 1 - index}>'
             return token
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						[]
             __lowerCamelCase   =						""""""
             __lowerCamelCase   =						False
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           if not prev_is_special:
                                  out_string += " "
                           out_string += self.sp_model.decode(UpperCamelCase_   ) + token
                           __lowerCamelCase   =						True
                           __lowerCamelCase   =						[]
                    else:
                           current_sub_tokens.append(UpperCamelCase_   )
                           __lowerCamelCase   =						False
             out_string += self.sp_model.decode(UpperCamelCase_   )
             return out_string.strip()




      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Optional[str] = None   ):
             if not os.path.isdir(UpperCamelCase_   ):
                    logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                    return
             __lowerCamelCase   =						os.path.join(
                 UpperCamelCase_    ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

             if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCamelCase_   ) and os.path.isfile(self.vocab_file   ):
                    copyfile(self.vocab_file    ,       UpperCamelCase_   )
             elif not os.path.isfile(self.vocab_file   ):
                    with open(UpperCamelCase_    ,       """wb"""   ) as fi:
                           __lowerCamelCase   =						self.sp_model.serialized_model_proto()
                           fi.write(UpperCamelCase_   )

             return (out_vocab_file,)






'''simple docstring'''






from manim import *

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		Rectangle(height=0.5		,			width=0.5		)
								UpperCAmelCase :       str 							=		Rectangle(height=0.46		,			width=0.46		).set_stroke(width=0		)

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

								UpperCAmelCase :       Optional[Any] 							=		[mem.copy() for i in range(4		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       List[str] 							=		Text('''GPU'''		,			font_size=24		)
								UpperCAmelCase :       Dict 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			buff=0.5		,			aligned_edge=__snake_case		)
								gpu.move_to([-1, -1, 0]		)
								self.add(__snake_case		)

								UpperCAmelCase :       int 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)

								UpperCAmelCase :       List[str] 							=		Text('''Model'''		,			font_size=24		)
								UpperCAmelCase :       Tuple 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			buff=0.5		,			aligned_edge=__snake_case		)
								model.move_to([3, -1.0, 0]		)
								self.add(__snake_case		)

								UpperCAmelCase :       Any 							=		[]
								for i, rect in enumerate(__snake_case		):
															rect.set_stroke(__snake_case		)
															# target = fill.copy().set_fill(YELLOW, opacity=0.7)
															# target.move_to(rect)
															# self.add(target)

															UpperCAmelCase :       Dict 							=		Rectangle(height=0.46 / 4		,			width=0.46 / 3		).set_stroke(width=0.0		).set_fill(__snake_case		,			opacity=0.7		)

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

								UpperCAmelCase :       int 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       int 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)

								UpperCAmelCase :       Any 							=		Text('''Loaded Checkpoint'''		,			font_size=24		)
								UpperCAmelCase :       Union[str, Any] 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			aligned_edge=__snake_case		,			buff=0.4		)
								checkpoint.move_to([3, 0.5, 0]		)

								UpperCAmelCase :       Optional[int] 							=		Square(side_length=2.2		)
								key.move_to([-5, 2, 0]		)

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

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

								self.add(__snake_case		,			__snake_case		)

								UpperCAmelCase :       Tuple 							=		MarkupText(
								    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""		,			font_size=18		,			)

								blue_text.next_to(__snake_case		,			DOWN * 2.4		,			aligned_edge=key_text.get_left()		)

								UpperCAmelCase :       List[Any] 							=		MarkupText(
								    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""		,			font_size=24		,			)
								step_a.move_to([2, 2, 0]		)
								self.play(Write(__snake_case		)		,			Write(__snake_case		)		)

								self.play(Write(__snake_case		,			run_time=1		)		,			Create(__snake_case		,			run_time=1		)		)

								UpperCAmelCase :       Tuple 							=		[]
								UpperCAmelCase :       int 							=		[]
								for i, rect in enumerate(__snake_case		):
															UpperCAmelCase :       Any 							=		fill.copy().set_fill(__snake_case		,			opacity=0.7		)
															target.move_to(__snake_case		)
															first_animations.append(GrowFromCenter(__snake_case		,			run_time=1		)		)

															UpperCAmelCase :       List[str] 							=		target.copy()
															cpu_target.generate_target()
															if i < 5:
																						cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
															else:
																						cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
															second_animations.append(MoveToTarget(__snake_case		,			run_time=1.5		)		)

								self.play(*__snake_case		)
								self.play(*__snake_case		)
								self.wait()





