






"""simple docstring"""






from manim import *




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


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

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

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[mem.copy() for i in range(1)]
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		VGroup(*lowercase_).arrange(lowercase_						,	buff=0)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		Text('''GPU'''						,	font_size=24)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		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_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[mem.copy() for i in range(6)]
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		VGroup(*lowercase_).arrange(lowercase_						,	buff=0)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		Text('''Model'''						,	font_size=24)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		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)						,	)

          SCREAMING_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						,	)

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

          SCREAMING_SNAKE_CASE_      :  Union[str, 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(lowercase_						,	run_time=2.5)						,	Write(lowercase_)						,	Write(lowercase_))

          self.add(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Any    						=		[]
          SCREAMING_SNAKE_CASE_      :  Any    						=		[]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[]
          for i, rect in enumerate(lowercase_):
              SCREAMING_SNAKE_CASE_      :  Any    						=		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()
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		0.46 / 4
              SCREAMING_SNAKE_CASE_      :  str    						=		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()







import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length, 2)   , _UpperCAmelCase				)
    else:
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length)   , _UpperCAmelCase				)

    for i, tensor in enumerate(_UpperCAmelCase				):
        if padding_side == "right":
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]
        else:
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]

    return out_tensor.tolist()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				ord(_UpperCAmelCase				)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    __a						       =				unicodedata.category(_UpperCAmelCase				)
    if cat.startswith('''P'''				):
        return True
    return False

@dataclass
class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    PreTrainedTokenizerBase
       UpperCamelCase__  :    Union[bool, str, PaddingStrategy]											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						-100
       UpperCamelCase__  :    str											=						"pt"



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           import torch

           __a						       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
           __a						       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
           __a						       =				self.tokenizer.pad(
               __SCREAMING_SNAKE_CASE     ,		padding=self.padding     ,		max_length=self.max_length     ,		pad_to_multiple_of=self.pad_to_multiple_of     ,		return_tensors='''pt''' if labels is None else None     ,		)

           if labels is None:
               return batch

           __a						       =				torch.tensor(batch['''entity_ids''']).shape[1]
           __a						       =				self.tokenizer.padding_side
           if padding_side == "right":
               __a						       =				[
                   list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
               ]
           else:
               __a						       =				[
                   [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
               ]

           __a						       =				[feature['''ner_tags'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		-1     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				[feature['''original_entity_spans'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		(-1, -1)     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{k: torch.tensor(__SCREAMING_SNAKE_CASE     ,		dtype=torch.intaa) for k, v in batch.items()}

           return batch





