





'''simple docstring'''






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def        UpperCamelCase__				(		lowerCAmelCase  ) ->    Tuple:





      """simple docstring"""


      _lowerCAmelCase												=							[]
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
              f"stage{idx}.patch_embed.proj.weight",
          )  )
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
              f"stage{idx}.patch_embed.proj.bias",
          )  )
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
              f"stage{idx}.patch_embed.norm.weight",
          )  )
      embed.append(
          (
              f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
              f"stage{idx}.patch_embed.norm.bias",
          )  )
      return embed
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ) ->    int:





      """simple docstring"""


      _lowerCAmelCase												=							[]
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
              f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
              f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
              f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
              f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
              f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
              f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
              f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
              f"stage{idx}.blocks.{cnt}.attn.proj.weight",
          )  )
      attention_weights.append(
          (
              f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
              f"stage{idx}.blocks.{cnt}.attn.proj.bias",
          )  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")  )
      attention_weights.append(
          (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")  )
      return attention_weights
def        UpperCamelCase__				(		lowerCAmelCase  ) ->    Union[str, Any]:





      """simple docstring"""


      _lowerCAmelCase												=							[]
      token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")  )
      return token
def        UpperCamelCase__				(		) ->    Tuple:





      """simple docstring"""


      _lowerCAmelCase												=							[]
      head.append(("""layernorm.weight""", """norm.weight""")  )
      head.append(("""layernorm.bias""", """norm.bias""")  )
      head.append(("""classifier.weight""", """head.weight""")  )
      head.append(("""classifier.bias""", """head.bias""")  )
      return head
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ) ->    Any:





      """simple docstring"""


      _lowerCAmelCase												=							"""imagenet-1k-id2label.json"""
      _lowerCAmelCase												=							10_00

      _lowerCAmelCase												=							"""huggingface/label-files"""
      _lowerCAmelCase												=							num_labels
      _lowerCAmelCase												=							json.load(open(cached_download(hf_hub_url(lowerCAmelCase							,     lowerCAmelCase							,     repo_type="""dataset"""  )  )							,     """r"""  )  )
      _lowerCAmelCase												=							{int(lowerCAmelCase  ): v for k, v in idalabel.items()}

      _lowerCAmelCase												=							idalabel
      _lowerCAmelCase												=							{v: k for k, v in idalabel.items()}

      _lowerCAmelCase												=							_lowerCAmelCase												=							CvtConfig(num_labels=lowerCAmelCase							,     idalabel=lowerCAmelCase							,     labelaid=lowerCAmelCase  )

      # For depth size 13 (13 = 1+2+10)
      if cvt_model.rsplit("""/"""							,     1  )[-1][4:6] == "13":
            _lowerCAmelCase												=							[1, 2, 10]

      # For depth size 21 (21 = 1+4+16)
      elif cvt_model.rsplit("""/"""							,     1  )[-1][4:6] == "21":
            _lowerCAmelCase												=							[1, 4, 16]

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            _lowerCAmelCase												=							[2, 2, 20]
            _lowerCAmelCase												=							[3, 12, 16]
            _lowerCAmelCase												=							[1_92, 7_68, 10_24]

      _lowerCAmelCase												=							CvtForImageClassification(lowerCAmelCase  )
      _lowerCAmelCase												=							AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""  )
      _lowerCAmelCase												=							image_size
      _lowerCAmelCase												=							torch.load(lowerCAmelCase							,     map_location=torch.device("""cpu"""  )  )

      _lowerCAmelCase												=							OrderedDict()
      _lowerCAmelCase												=							[]

      for idx in range(len(config.depth  )  ):
            if config.cls_token[idx]:
                  _lowerCAmelCase												=							list_of_state_dict + cls_token(lowerCAmelCase  )
            _lowerCAmelCase												=							list_of_state_dict + embeddings(lowerCAmelCase  )
            for cnt in range(config.depth[idx]  ):
                  _lowerCAmelCase												=							list_of_state_dict + attention(lowerCAmelCase							,     lowerCAmelCase  )

      _lowerCAmelCase												=							list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(lowerCAmelCase  )
      for i in range(len(lowerCAmelCase  )  ):
            _lowerCAmelCase												=							original_weights[list_of_state_dict[i][1]]

      model.load_state_dict(lowerCAmelCase  )
      model.save_pretrained(lowerCAmelCase  )
      image_processor.save_pretrained(lowerCAmelCase  )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
      A__    :       Optional[int]              =argparse.ArgumentParser()
      parser.add_argument(
          '''--cvt_model''',
          default='''cvt-w24''',
          type=str,
          help='''Name of the cvt model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--image_size''',
          default=3_84,
          type=int,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--cvt_file_name''',
          default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
          type=str,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      A__    :       str              =parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)













'''simple docstring'''






from manim import *




class 			UpperCAmelCase					(      snake_case_       ):




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				str:
            _lowerCAmelCase												=							Rectangle(height=0.5      ,      width=0.5  )
            _lowerCAmelCase												=							Rectangle(height=0.46      ,      width=0.46  ).set_stroke(width=0  )

            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							[mem.copy() for i in range(6  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							VGroup(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							Text("""CPU"""      ,      font_size=24  )
            _lowerCAmelCase												=							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  )

            _lowerCAmelCase												=							[mem.copy() for i in range(4  )]
            _lowerCAmelCase												=							VGroup(*__snake_case  ).arrange(__snake_case      ,      buff=0  )
            _lowerCAmelCase												=							Text("""GPU"""      ,      font_size=24  )
            _lowerCAmelCase												=							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  )

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

            _lowerCAmelCase												=							Text("""Model"""      ,      font_size=24  )
            _lowerCAmelCase												=							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  )

            _lowerCAmelCase												=							[]
            for i, rect in enumerate(__snake_case  ):
                  rect.set_stroke(__snake_case  )
                  # target = fill.copy().set_fill(YELLOW, opacity=0.7)
                  # target.move_to(rect)
                  # self.add(target)

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

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

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

            _lowerCAmelCase												=							Text("""Loaded Checkpoint"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      aligned_edge=__snake_case      ,      buff=0.4  )
            checkpoint.move_to([3, 0.5, 0]  )

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

            _lowerCAmelCase												=							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(__snake_case      ,      __snake_case  )

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

            blue_text.next_to(__snake_case      ,      DOWN * 2.4      ,      aligned_edge=key_text.get_left()  )

            _lowerCAmelCase												=							MarkupText(
                f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."      ,      font_size=24      ,      )
            step_a.move_to([2, 2, 0]  )
            self.play(Write(__snake_case  )      ,      Write(__snake_case  )  )

            self.play(Write(__snake_case      ,      run_time=1  )      ,      Create(__snake_case      ,      run_time=1  )  )

            _lowerCAmelCase												=							[]
            _lowerCAmelCase												=							[]
            for i, rect in enumerate(__snake_case  ):
                  _lowerCAmelCase												=							fill.copy().set_fill(__snake_case      ,      opacity=0.7  )
                  target.move_to(__snake_case  )
                  first_animations.append(GrowFromCenter(__snake_case      ,      run_time=1  )  )

                  _lowerCAmelCase												=							target.copy()
                  cpu_target.generate_target()
                  if i < 5:
                        cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
                  else:
                        cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
                  second_animations.append(MoveToTarget(__snake_case      ,      run_time=1.5  )  )

            self.play(*__snake_case  )
            self.play(*__snake_case  )
            self.wait()






