
from manim import *



class 		UpperCamelCase__  (	__lowercase   ):
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  Union[str, Any]		    =  Rectangle(height=0.5 ,    width=0.5       )
    __a			:  Union[str, Any]		    =  Rectangle(height=0.46 ,    width=0.46       ).set_stroke(width=0       )

    __a			:  Optional[int]		    =  Rectangle(height=0.25 ,    width=0.25       )

    __a			:  Tuple		    =  [mem.copy() for i in range(6       )]
    __a			:  Dict		    =  [mem.copy() for i in range(6       )]
    __a			:  str		    =  VGroup(*snake_case_       ).arrange(snake_case_ ,    buff=0       )
    __a			:  Optional[int]		    =  VGroup(*snake_case_       ).arrange(snake_case_ ,    buff=0       )
    __a			:  Dict		    =  VGroup(snake_case_ ,    snake_case_       ).arrange(snake_case_ ,    buff=0       )
    __a			:  str		    =  Text('''CPU''' ,    font_size=2_4       )
    __a			:  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_       )

    __a			:  List[Any]		    =  [mem.copy() for i in range(4       )]
    __a			:  List[Any]		    =  VGroup(*snake_case_       ).arrange(snake_case_ ,    buff=0       )
    __a			:  List[Any]		    =  Text('''GPU''' ,    font_size=2_4       )
    __a			:  Union[str, Any]		    =  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_       )

    __a			:  str		    =  [mem.copy() for i in range(6       )]
    __a			:  Optional[Any]		    =  VGroup(*snake_case_       ).arrange(snake_case_ ,    buff=0       )

    __a			:  Tuple		    =  Text('''Model''' ,    font_size=2_4       )
    __a			:  Dict		    =  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_       )

    __a			:  List[str]		    =  []
    __a			:  Any		    =  []

    for i, rect in enumerate(snake_case_       ):
     __a			:  Dict		    =  fill.copy().set_fill(snake_case_ ,    opacity=0.8       )
     target.move_to(snake_case_       )
     model_arr.append(snake_case_       )

     __a			:  int		    =  Rectangle(height=0.46 ,    width=0.46       ).set_stroke(width=0.0       ).set_fill(snake_case_ ,    opacity=0.8       )
     cpu_target.move_to(cpu_left_col_base[i]       )
     model_cpu_arr.append(snake_case_       )

    self.add(*snake_case_ ,    *snake_case_       )

    __a			:  List[str]		    =  [meta_mem.copy() for i in range(6       )]
    __a			:  Union[str, Any]		    =  [meta_mem.copy() for i in range(6       )]
    __a			:  Union[str, Any]		    =  VGroup(*snake_case_       ).arrange(snake_case_ ,    buff=0       )
    __a			:  Dict		    =  VGroup(*snake_case_       ).arrange(snake_case_ ,    buff=0       )
    __a			:  Tuple		    =  VGroup(snake_case_ ,    snake_case_       ).arrange(snake_case_ ,    buff=0       )
    __a			:  int		    =  Text('''Disk''' ,    font_size=2_4       )
    __a			:  Any		    =  Group(snake_case_ ,    snake_case_       ).arrange(snake_case_ ,    buff=0.5 ,    aligned_edge=snake_case_       )
    disk.move_to([-4, -1.25, 0]       )
    self.add(snake_case_ ,    snake_case_       )

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

    __a			:  Optional[int]		    =  MarkupText(
        f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" ,    font_size=1_8 ,    )

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

    self.add(snake_case_ ,    snake_case_       )

    __a			:  Dict		    =  MarkupText(
        f"<span fgcolor='{BLUE}'>●</span> Checkpoint" ,    font_size=1_8 ,    )

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

    __a			:  Optional[int]		    =  MarkupText(
        f"Now watch as an input is passed through the model\nand how the memory is utilized and handled." ,    font_size=2_4 ,    )
    step_a.move_to([2, 2, 0]       )

    self.play(Write(snake_case_       )       )

    __a			:  Any		    =  Square(0.3       )
    input.set_fill(snake_case_ ,    opacity=1.0       )
    input.set_stroke(width=0.0       )
    input.next_to(model_base[0] ,    snake_case_ ,    buff=0.5       )

    self.play(Write(snake_case_       )       )

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

    self.play(FadeOut(snake_case_       )       )

    __a			:  Dict		    =  Arrow(start=snake_case_ ,    end=snake_case_ ,    color=snake_case_ ,    buff=0.5       )
    a.next_to(model_arr[0].get_left() ,    snake_case_ ,    buff=0.2       )

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

    __a			:  Optional[Any]		    =  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=2_4 ,    )
    step_a.move_to([2, 2, 0]       )

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

    __a			:  Any		    =  {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}

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

    __a			:  List[Any]		    =  a.copy()
    for i in range(6       ):
     a_c.next_to(model_arr[i].get_right() + 0.02 ,    snake_case_ ,    buff=0.2       )

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

     __a			:  List[Any]		    =  AnimationGroup(
         FadeOut(snake_case_ ,    run_time=0.5       ) ,    MoveToTarget(snake_case_ ,    run_time=0.5       ) ,    FadeIn(snake_case_ ,    run_time=0.5       ) ,    lag_ratio=0.2       )

     self.play(snake_case_       )

     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			:  int		    =  0.7

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

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

     __a			:  Any		    =  a_c
     __a			:  Dict		    =  a_c.copy()

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

    __a			:  List[str]		    =  MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed." ,    font_size=2_4       )
    step_a.move_to([2, 2, 0]       )

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

    self.wait()


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
