





'''simple docstring'''




from manim import *


class  SCREAMING_SNAKE_CASE    (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       Rectangle(height=0.5						,	width=0.5)
          __A						: Dict      =       Rectangle(height=0.46						,	width=0.46).set_stroke(width=0)

          __A						: Tuple      =       Rectangle(height=0.25						,	width=0.25)

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

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

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

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

          __A						: Tuple      =       []
          __A						: Dict      =       []

          for i, rect in enumerate(_UpperCAmelCase):
               __A						: Any      =       fill.copy().set_fill(_UpperCAmelCase						,	opacity=0.8)
               target.move_to(_UpperCAmelCase)
               model_arr.append(_UpperCAmelCase)

               __A						: Union[str, Any]      =       Rectangle(height=0.46						,	width=0.46).set_stroke(width=0.0).set_fill(_UpperCAmelCase						,	opacity=0.8)
               cpu_target.move_to(cpu_left_col_base[i])
               model_cpu_arr.append(_UpperCAmelCase)

          self.add(*_UpperCAmelCase						,	*_UpperCAmelCase)

          __A						: List[str]      =       [meta_mem.copy() for i in range(6)]
          __A						: Optional[int]      =       [meta_mem.copy() for i in range(6)]
          __A						: Any      =       VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase						,	buff=0)
          __A						: Optional[Any]      =       VGroup(*_UpperCAmelCase).arrange(_UpperCAmelCase						,	buff=0)
          __A						: List[Any]      =       VGroup(_UpperCAmelCase						,	_UpperCAmelCase).arrange(_UpperCAmelCase						,	buff=0)
          __A						: int      =       Text('Disk'						,	font_size=24)
          __A						: Optional[Any]      =       Group(_UpperCAmelCase						,	_UpperCAmelCase).arrange(_UpperCAmelCase						,	buff=0.5						,	aligned_edge=_UpperCAmelCase)
          disk.move_to([-4, -1.25, 0])
          self.add(_UpperCAmelCase						,	_UpperCAmelCase)

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

          __A						: Optional[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])

          self.add(_UpperCAmelCase						,	_UpperCAmelCase)

          __A						: str      =       MarkupText(
              F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'						,	font_size=18						,	)

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

          __A						: List[str]      =       MarkupText(
              F'Now watch as an input is passed through the model\nand how the memory is utilized and handled.'						,	font_size=24						,	)
          step_a.move_to([2, 2, 0])

          self.play(Write(_UpperCAmelCase))

          __A						: str      =       Square(0.3)
          input.set_fill(_UpperCAmelCase						,	opacity=1.0)
          input.set_stroke(width=0.0)
          input.next_to(model_base[0]						,	_UpperCAmelCase						,	buff=0.5)

          self.play(Write(_UpperCAmelCase))

          input.generate_target()
          input.target.next_to(model_arr[0]						,	direction=_UpperCAmelCase						,	buff=0.02)
          self.play(MoveToTarget(_UpperCAmelCase))

          self.play(FadeOut(_UpperCAmelCase))

          __A						: List[str]      =       Arrow(start=_UpperCAmelCase						,	end=_UpperCAmelCase						,	color=_UpperCAmelCase						,	buff=0.5)
          a.next_to(model_arr[0].get_left()						,	_UpperCAmelCase						,	buff=0.2)

          model_cpu_arr[0].generate_target()
          model_cpu_arr[0].target.move_to(gpu_rect[0])

          __A						: str      =       MarkupText(
              F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'						,	font_size=24						,	)
          step_a.move_to([2, 2, 0])

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

          __A						: int      =       {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

          self.play(
              Write(_UpperCAmelCase)						,	Circumscribe(model_arr[0]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	Circumscribe(model_cpu_arr[0]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	Circumscribe(gpu_rect[0]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	)
          self.play(MoveToTarget(model_cpu_arr[0]))

          __A						: Dict      =       a.copy()
          for i in range(6):
               a_c.next_to(model_arr[i].get_right() + 0.02						,	_UpperCAmelCase						,	buff=0.2)

               input.generate_target()
               input.target.move_to(model_arr[i].get_right() + 0.02)

               __A						: int      =       AnimationGroup(
                   FadeOut(_UpperCAmelCase						,	run_time=0.5)						,	MoveToTarget(_UpperCAmelCase						,	run_time=0.5)						,	FadeIn(_UpperCAmelCase						,	run_time=0.5)						,	lag_ratio=0.2)

               self.play(_UpperCAmelCase)

               model_cpu_arr[i].generate_target()
               model_cpu_arr[i].target.move_to(cpu_left_col_base[i])

               if i < 5:
                    model_cpu_arr[i + 1].generate_target()
                    model_cpu_arr[i + 1].target.move_to(gpu_rect[0])
                    if i >= 1:
                         __A						: Dict      =       0.7

                    self.play(
                        Circumscribe(model_arr[i]						,	**_UpperCAmelCase)						,	Circumscribe(cpu_left_col_base[i]						,	**_UpperCAmelCase)						,	Circumscribe(cpu_left_col_base[i + 1]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	Circumscribe(gpu_rect[0]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	Circumscribe(model_arr[i + 1]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	)
                    if i < 1:
                         self.play(
                             MoveToTarget(model_cpu_arr[i])						,	MoveToTarget(model_cpu_arr[i + 1])						,	)
                    else:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]						,	run_time=0.7)						,	MoveToTarget(model_cpu_arr[i + 1]						,	run_time=0.7)						,	)
               else:
                    model_cpu_arr[i].generate_target()
                    model_cpu_arr[i].target.move_to(cpu_left_col_base[-1])
                    input.generate_target()
                    input.target.next_to(model_arr[-1].get_right()						,	RIGHT + 0.02						,	buff=0.2)

                    self.play(
                        Circumscribe(model_arr[-1]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	Circumscribe(cpu_left_col_base[-1]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	Circumscribe(gpu_rect[0]						,	color=_UpperCAmelCase						,	**_UpperCAmelCase)						,	)

                    self.play(MoveToTarget(model_cpu_arr[i]))

               __A						: Optional[Any]      =       a_c
               __A						: Optional[int]      =       a_c.copy()

          input.generate_target()
          input.target.next_to(model_base[-1]						,	RIGHT + 0.02						,	buff=0.5)
          self.play(
              FadeOut(_UpperCAmelCase)						,	FadeOut(_UpperCAmelCase						,	run_time=0.5)						,	)

          __A						: str      =       MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.'						,	font_size=24)
          step_a.move_to([2, 2, 0])

          self.play(Write(_UpperCAmelCase						,	run_time=3)						,	MoveToTarget(_UpperCAmelCase))

          self.wait()






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      List[str]						=    logging.get_logger(__name__)

lowercase__    :      Dict						=    {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: int      =       hidden_size
          __A						: Union[str, Any]      =       num_hidden_layers
          __A						: List[str]      =       num_attention_heads
          __A						: Optional[int]      =       hidden_act
          __A						: str      =       intermediate_size
          __A						: Union[str, Any]      =       hidden_dropout_prob
          __A						: Dict      =       attention_probs_dropout_prob
          __A						: int      =       max_position_embeddings
          __A						: str      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: int      =       layer_norm_eps
          __A						: Optional[int]      =       position_embedding_type
          __A						: int      =       use_cache
          __A						: Union[str, Any]      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: str      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: List[str]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])