





import colorsys

from PIL import Image  # type: ignore


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						float:
    SCREAMING_SNAKE_CASE__											=			x
    SCREAMING_SNAKE_CASE__											=			y
    for step in range(lowerCAmelCase_	):  # noqa: B007
        SCREAMING_SNAKE_CASE__											=			a * a - b * b + x
        SCREAMING_SNAKE_CASE__											=			2 * a * b + y
        SCREAMING_SNAKE_CASE__											=			a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def 							__snake_case							(    lowerCAmelCase_	)				->						tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def 							__snake_case							(    lowerCAmelCase_	)				->						tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5	) for i in colorsys.hsv_to_rgb(lowerCAmelCase_ ,							1 ,							1	)	)


def 							__snake_case							(    lowerCAmelCase_ = 8_0_0 ,							lowerCAmelCase_ = 6_0_0 ,							lowerCAmelCase_ = -0.6 ,							lowerCAmelCase_ = 0 ,							lowerCAmelCase_ = 3.2 ,							lowerCAmelCase_ = 5_0 ,							lowerCAmelCase_ = True ,							)				->						Image.Image:
    SCREAMING_SNAKE_CASE__											=			Image.new('''RGB''' ,							(image_width, image_height)	)
    SCREAMING_SNAKE_CASE__											=			img.load()

    # loop through the image-coordinates
    for image_x in range(lowerCAmelCase_	):
        for image_y in range(lowerCAmelCase_	):
            # determine the figure-coordinates based on the image-coordinates
            SCREAMING_SNAKE_CASE__											=			figure_width / image_width * image_height
            SCREAMING_SNAKE_CASE__											=			figure_center_x + (image_x / image_width - 0.5) * figure_width
            SCREAMING_SNAKE_CASE__											=			figure_center_y + (image_y / image_height - 0.5) * figure_height

            SCREAMING_SNAKE_CASE__											=			get_distance(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                SCREAMING_SNAKE_CASE__											=			get_color_coded_rgb(lowerCAmelCase_	)
            else:
                SCREAMING_SNAKE_CASE__											=			get_black_and_white_rgb(lowerCAmelCase_	)

    return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  _A					:				Optional[int]				=			get_image()

  # uncomment for colored version, different section, zoomed in
  # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
  # figure_width = 0.8)

  # uncomment for black and white version, full figure
  # img = get_image(use_distance_color_coding = False)

  # uncomment to save the image
  # img.save("mandelbrot.png")

  img.show()







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 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')	)
    return token


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__											=			1_0_0_0

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

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

    SCREAMING_SNAKE_CASE__											=			SCREAMING_SNAKE_CASE__											=			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":
        SCREAMING_SNAKE_CASE__											=			[1, 2, 1_0]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "21":
        SCREAMING_SNAKE_CASE__											=			[1, 4, 1_6]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        SCREAMING_SNAKE_CASE__											=			[2, 2, 2_0]
        SCREAMING_SNAKE_CASE__											=			[3, 1_2, 1_6]
        SCREAMING_SNAKE_CASE__											=			[1_9_2, 7_6_8, 1_0_2_4]

    SCREAMING_SNAKE_CASE__											=			CvtForImageClassification(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
    SCREAMING_SNAKE_CASE__											=			image_size
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location=torch.device('''cpu'''	)	)

    SCREAMING_SNAKE_CASE__											=			OrderedDict()
    SCREAMING_SNAKE_CASE__											=			[]

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

    SCREAMING_SNAKE_CASE__											=			list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(lowerCAmelCase_	)
    for i in range(len(lowerCAmelCase_	)	):
        SCREAMING_SNAKE_CASE__											=			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					:				Union[str, Any]				=			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					:				int				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
