


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class 						__SCREAMING_SNAKE_CASE					(      _a ,			_a ):
     UpperCAmelCase 					=				'''swin'''

     UpperCAmelCase 					=				{
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }






     def __init__(			self     ,							__UpperCamelCase=224     ,							__UpperCamelCase=4     ,							__UpperCamelCase=3     ,							__UpperCamelCase=96     ,							__UpperCamelCase=[2, 2, 6, 2]     ,							__UpperCamelCase=[3, 6, 12, 24]     ,							__UpperCamelCase=7     ,							__UpperCamelCase=4.0     ,							__UpperCamelCase=True     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=False     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-5     ,							__UpperCamelCase=32     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(**_A		)

          _a						=		image_size
          _a						=		patch_size
          _a						=		num_channels
          _a						=		embed_dim
          _a						=		depths
          _a						=		len(_A		)
          _a						=		num_heads
          _a						=		window_size
          _a						=		mlp_ratio
          _a						=		qkv_bias
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		drop_path_rate
          _a						=		hidden_act
          _a						=		use_absolute_embeddings
          _a						=		layer_norm_eps
          _a						=		initializer_range
          _a						=		encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          _a						=		int(embed_dim * 2 ** (len(_A		) - 1)		)
          _a						=		["stem"] + [f"stage{idx}" for idx in range(1     ,							len(_A		) + 1		)]
          _a			,			_a						=		get_aligned_output_features_output_indices(
              out_features=_A     ,							out_indices=_A     ,							stage_names=self.stage_names		)







class 						__SCREAMING_SNAKE_CASE					(      _a ):
     UpperCAmelCase 					=				version.parse('''1.11''' )






     @property
     def  a_					(			self		)       ->    Any:
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)






     @property
     def  a_					(			self		)       ->    Tuple:
          return 1e-4





'''simple docstring'''

from manim import *





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def  a_					(			self		)       ->    str:
          _a						=		Rectangle(height=0.5     ,							width=0.5		)
          _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0		)

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

          _a						=		[mem.copy() for i in range(1		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("GPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          gpu.align_to(__UpperCamelCase     ,							__UpperCamelCase		)
          gpu.set_x(gpu.get_x() - 1		)

          self.add(__UpperCamelCase		)

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

          _a						=		Text("Model"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          model.move_to([3, -1.0, 0]		)

          self.play(
              Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							)

          _a						=		MarkupText(
              f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."     ,							font_size=24     ,							)

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

          _a						=		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(__UpperCamelCase     ,							run_time=2.5		)     ,							Write(__UpperCamelCase		)     ,							Write(__UpperCamelCase		)		)

          self.add(__UpperCamelCase		)

          _a						=		[]
          _a						=		[]
          _a						=		[]
          for i, rect in enumerate(__UpperCamelCase		):
               _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0.0		).set_fill(__UpperCamelCase     ,							opacity=0.7		)
               cpu_target.move_to(__UpperCamelCase		)
               cpu_target.generate_target()
               _a						=		0.46 / 4
               _a						=		0.46 / 3

               if i == 0:
                    cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)     ,							buff=0.02     ,							direction=__UpperCamelCase		)
                    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=__UpperCamelCase     ,							buff=0.0		)
               else:
                    cpu_target.target.next_to(cpu_targs[i - 1].target     ,							direction=__UpperCamelCase     ,							buff=0.0		)
               cpu_targs.append(__UpperCamelCase		)

               first_animations.append(rect.animate(run_time=0.5		).set_stroke(__UpperCamelCase		)		)
               second_animations.append(MoveToTarget(__UpperCamelCase     ,							run_time=1.5		)		)

          self.play(*__UpperCamelCase		)
          self.play(*__UpperCamelCase		)

          self.wait()

