





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__           =			"▁"

UpperCamelCase__           =			{"vocab_file": "spiece.model"}

UpperCamelCase__           =			{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

UpperCamelCase__           =			{
    "google/pegasus-xsum": 512,
}


UpperCamelCase__           =			logging.get_logger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   str      =							VOCAB_FILES_NAMES

							snake_case  :   List[str]      =							VOCAB_FILES_NAMES
							snake_case  :   List[str]      =							PRETRAINED_VOCAB_FILES_MAP
							snake_case  :   Optional[Any]      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case  :   Optional[Any]      =							["""input_ids""", """attention_mask"""]






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase="<pad>"	,			__lowerCAmelCase="</s>"	,			__lowerCAmelCase="<unk>"	,			__lowerCAmelCase="<mask_2>"	,			__lowerCAmelCase="<mask_1>"	,			__lowerCAmelCase=None	,			__lowerCAmelCase=103	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      offset
														if additional_special_tokens is not None:
																					if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase	):
																												raise TypeError(
																												    f"""additional_special_tokens should be of type {type(__lowerCAmelCase	)}, but is"""
																												    f""" {type(__lowerCAmelCase	)}"""	)

																					UpperCamelCase__					  =      (
																					    ([mask_token_sent] + additional_special_tokens)
																					    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																					    else additional_special_tokens
																					)
																					# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																					additional_special_tokens_extended += [
																					    f"""<unk_{i}>""" for i in range(len(__lowerCAmelCase	)	,			self.offset - 1	)
																					]

																					if len(set(__lowerCAmelCase	)	) != len(__lowerCAmelCase	):
																												raise ValueError(
																												    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																												    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""	)
																					UpperCamelCase__					  =      additional_special_tokens_extended
														else:
																					UpperCamelCase__					  =      [mask_token_sent] if mask_token_sent is not None else []
																					additional_special_tokens += [f"""<unk_{i}>""" for i in range(2	,			self.offset	)]

														UpperCamelCase__					  =      {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    eos_token=__lowerCAmelCase	,			unk_token=__lowerCAmelCase	,			mask_token=__lowerCAmelCase	,			pad_token=__lowerCAmelCase	,			mask_token_sent=__lowerCAmelCase	,			offset=__lowerCAmelCase	,			additional_special_tokens=__lowerCAmelCase	,			sp_model_kwargs=self.sp_model_kwargs	,			**__lowerCAmelCase	,			)
														UpperCamelCase__					  =      mask_token_sent
														UpperCamelCase__					  =      vocab_file
														UpperCamelCase__					  =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
														self.sp_model.Load(__lowerCAmelCase	)

														# add special tokens to encoder dict
														UpperCamelCase__					  =      {
														    0: self.pad_token,
														    1: self.eos_token,
														}

														if self.mask_token_sent is not None:
																					self.encoder.update(
																					    {
																					        2: self.mask_token_sent,
																					        3: self.mask_token,
																					    }	)

														if self.offset > 0:
																					# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																					# mask_token_sent is already added to list -> so start at 1
																					self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1	,			self.offset - 1	)}	)

														UpperCamelCase__					  =      {v: k for k, v in self.encoder.items()}






							@property
							def 					_lowerCamelCase		(    self	):
														return len(self.sp_model	) + self.offset






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      {self.convert_ids_to_tokens(__lowerCAmelCase	): i for i in range(self.vocab_size	)}
														vocab.update(self.added_tokens_encoder	)
														return vocab






							def __getstate__(    self	):
														UpperCamelCase__					  =      self.__dict__.copy()
														UpperCamelCase__					  =      None
														return state






							def __setstate__(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      d

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

														UpperCamelCase__					  =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
														self.sp_model.Load(self.vocab_file	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														return self.sp_model.encode(__lowerCAmelCase	,			out_type=__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														if token in self.decoder:
																					return self.decoder[token]
														elif token in self.added_tokens_decoder:
																					return self.added_tokens_decoder[token]
														UpperCamelCase__					  =      self.sp_model.piece_to_id(__lowerCAmelCase	)
														return sp_id + self.offset






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														if index in self.encoder:
																					return self.encoder[index]
														elif index in self.added_tokens_encoder:
																					return self.added_tokens_encoder[index]
														else:
																					UpperCamelCase__					  =      self.sp_model.IdToPiece(index - self.offset	)
														return token






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      """"""
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(__lowerCAmelCase	) + token
																												UpperCamelCase__					  =      []
																					else:
																												current_sub_tokens.append(__lowerCAmelCase	)
														out_string += self.sp_model.decode(__lowerCAmelCase	)
														return out_string.strip()






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase=False	):
														return 1






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      set(self.all_special_ids	)  # call it once instead of inside list comp
														all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

														return [1 if x in all_special_ids else 0 for x in seq]






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = False	):
														if already_has_special_tokens:
																					return self._special_token_mask(__lowerCAmelCase	)
														elif token_ids_a is None:
																					return self._special_token_mask(__lowerCAmelCase	) + [1]
														else:
																					return self._special_token_mask(token_ids_a + token_ids_a	) + [1]






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	):
														if token_ids_a is None:
																					return token_ids_a + [self.eos_token_id]
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return token_ids_a + token_ids_a + [self.eos_token_id]







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														if not os.path.isdir(__lowerCAmelCase	):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
																					return
														UpperCamelCase__					  =      os.path.join(
														    __lowerCAmelCase	,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

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

														return (out_vocab_file,)











from manim import *

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      Rectangle(height=0.5	,			width=0.5	)
														UpperCamelCase__					  =      Rectangle(height=0.25	,			width=0.25	)
														UpperCamelCase__					  =      Rectangle(height=0.46	,			width=0.46	).set_stroke(width=0	)

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

														UpperCamelCase__					  =      [mem.copy() for i in range(4	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      Text("""GPU"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														gpu.move_to([-1, -1, 0]	)
														self.add(__lowerCAmelCase	)

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

														UpperCamelCase__					  =      Text("""Model"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														model.move_to([3, -1.0, 0]	)
														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []

														for i, rect in enumerate(__lowerCAmelCase	):
																					rect.set_stroke(__lowerCAmelCase	)

																					UpperCamelCase__					  =      Rectangle(height=0.46 / 4	,			width=0.46 / 3	).set_stroke(width=0.0	).set_fill(__lowerCAmelCase	,			opacity=0.7	)

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

														self.add(*__lowerCAmelCase	,			*__lowerCAmelCase	,			*__lowerCAmelCase	)

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

														UpperCamelCase__					  =      Text("""Loaded Checkpoint"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														checkpoint.move_to([3, 0.5, 0]	)

														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      []

														for i, rect in enumerate(__lowerCAmelCase	):
																					UpperCamelCase__					  =      fill.copy().set_fill(__lowerCAmelCase	,			opacity=0.7	)
																					target.move_to(__lowerCAmelCase	)
																					ckpt_arr.append(__lowerCAmelCase	)

																					UpperCamelCase__					  =      target.copy()
																					if i < 5:
																												cpu_target.move_to(cpu_left_col_base[i + 1]	)
																					else:
																												cpu_target.move_to(cpu_right_col_base[i - 5]	)
																					ckpt_cpu_arr.append(__lowerCAmelCase	)
														self.add(*__lowerCAmelCase	,			*__lowerCAmelCase	)

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

														UpperCamelCase__					  =      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(__lowerCAmelCase	,			__lowerCAmelCase	)

														UpperCamelCase__					  =      MarkupText(
														    f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""	,			font_size=18	,			)

														blue_text.next_to(__lowerCAmelCase	,			DOWN * 2.4	,			aligned_edge=key_text.get_left()	)
														self.add(__lowerCAmelCase	)

														UpperCamelCase__					  =      MarkupText(
														    f"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device."""	,			font_size=24	,			)
														step_a.move_to([2, 2, 0]	)

														UpperCamelCase__					  =      [meta_mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      [meta_mem.copy() for i in range(6	)]
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(*__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      VGroup(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0	)
														UpperCamelCase__					  =      Text("""Disk"""	,			font_size=24	)
														UpperCamelCase__					  =      Group(__lowerCAmelCase	,			__lowerCAmelCase	).arrange(__lowerCAmelCase	,			buff=0.5	,			aligned_edge=__lowerCAmelCase	)
														disk.move_to([-4.0, -1.25, 0]	)
														self.play(Write(__lowerCAmelCase	,			run_time=3	)	,			Write(__lowerCAmelCase	,			run_time=1	)	,			Create(__lowerCAmelCase	,			run_time=1	)	)

														UpperCamelCase__					  =      []
														for i, rect in enumerate(__lowerCAmelCase	):
																					UpperCamelCase__					  =      rect.copy()
																					target.generate_target()
																					target.target.move_to(disk_left_col_base[i]	).scale(0.5	)
																					animations.append(MoveToTarget(__lowerCAmelCase	,			run_time=1.5	)	)
														self.play(*__lowerCAmelCase	)

														self.play(FadeOut(__lowerCAmelCase	)	)

														UpperCamelCase__					  =      MarkupText(f"""Then, the checkpoint is removed from memory\nthrough garbage collection."""	,			font_size=24	)
														step_a.move_to([2, 2, 0]	)

														self.play(Write(__lowerCAmelCase	,			run_time=3	)	)

														self.play(
														    FadeOut(__lowerCAmelCase	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			*__lowerCAmelCase	)	,			)

														self.wait()




