





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      Optional[int]			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     []

		# fmt: off
		# stem:
		rename_keys.append(('cls_token', 'vit.embeddings.cls_token')							)
		rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')							)

		rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')							)
		rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')							)

		# backbone
		rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')							)
		rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')							)
		rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')							)

		for stage_idx in range(len(config.backbone_config.depths							)							):
				for layer_idx in range(config.backbone_config.depths[stage_idx]							):
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")							)
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")							)

				rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")							)
				rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")							)
				rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")							)

		# transformer encoder
		for i in range(config.num_hidden_layers							):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")							)
				rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")							)
				rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")							)
				rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")							)
				rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")							)
				rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")							)
				rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")							)
				rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")							)
				rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")							)
				rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")							)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('norm.weight', 'layernorm.weight'),
				        ('norm.bias', 'layernorm.bias'),
				        ('pre_logits.fc.weight', 'pooler.dense.weight'),
				        ('pre_logits.fc.bias', 'pooler.dense.bias'),
				    ]							)

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				__lowercase											=     [(pair[0], pair[1][4:]) if pair[1].startswith('vit'							) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('norm.weight', 'vit.layernorm.weight'),
				        ('norm.bias', 'vit.layernorm.bias'),
				        ('head.weight', 'classifier.weight'),
				        ('head.bias', 'classifier.bias'),
				    ]							)
		# fmt: on

		return rename_keys



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=False							)		->    Optional[int]:


		for i in range(config.num_hidden_layers							):
				if base_model:
						__lowercase											=     ''
				else:
						__lowercase											=     'vit.'
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				__lowercase											=     state_dict.pop(F"blocks.{i}.attn.qkv.weight"							)
				__lowercase											=     state_dict.pop(F"blocks.{i}.attn.qkv.bias"							)
				# next, add query, keys and values (in that order) to the state dict
				__lowercase											=     in_proj_weight[
				    : config.hidden_size, :
				]
				__lowercase											=     in_proj_bias[: config.hidden_size]
				__lowercase											=     in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				__lowercase											=     in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				__lowercase											=     in_proj_weight[
				    -config.hidden_size :, :
				]
				__lowercase											=     in_proj_bias[-config.hidden_size :]



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    str:
		__lowercase											=     ['head.weight', 'head.bias']
		for k in ignore_keys:
				state_dict.pop(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    List[Any]:
		__lowercase											=     dct.pop(snake_case							)
		__lowercase											=     val



def  SCREAMING_SNAKE_CASE				(      )		->    Optional[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=False							)		->    Tuple:
		__lowercase											=     BitConfig(
		    global_padding='same'						,   layer_type='bottleneck'						,   depths=(3, 4, 9)						,   out_features=['stage3']						,   embedding_dynamic_padding=snake_case						,   )
		__lowercase											=     ViTHybridConfig(backbone_config=snake_case						,   image_size=384						,   num_labels=1_000							)
		__lowercase											=     False

		# load original model from timm
		__lowercase											=     timm.create_model(snake_case						,   pretrained=snake_case							)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		__lowercase											=     timm_model.state_dict()
		if base_model:
				remove_classification_head_(snake_case							)
		__lowercase											=     create_rename_keys(snake_case						,   snake_case							)
		for src, dest in rename_keys:
				rename_key(snake_case						,   snake_case						,   snake_case							)
		read_in_q_k_v(snake_case						,   snake_case						,   snake_case							)

		__lowercase											=     'huggingface/label-files'
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							): v for k, v in idalabel.items()}
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				__lowercase											=     ViTHybridModel(snake_case							).eval()
		else:
				__lowercase											=     ViTHybridForImageClassification(snake_case							).eval()
		model.load_state_dict(snake_case							)

		# create image processor
		__lowercase											=     create_transform(**resolve_data_config({}						,   model=snake_case							)							)
		__lowercase											=     transform.transforms

		__lowercase											=     {
		    'bilinear': PILImageResampling.BILINEAR,
		    'bicubic': PILImageResampling.BICUBIC,
		    'nearest': PILImageResampling.NEAREST,
		}

		__lowercase											=     ViTHybridImageProcessor(
		    do_resize=snake_case						,   size={'shortest_edge': timm_transforms[0].size}						,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,   do_center_crop=snake_case						,   crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}						,   do_normalize=snake_case						,   image_mean=timm_transforms[-1].mean.tolist()						,   image_std=timm_transforms[-1].std.tolist()						,   )

		__lowercase											=     prepare_img()
		__lowercase											=     transform(snake_case							).unsqueeze(0							)
		__lowercase											=     processor(snake_case						,   return_tensors='pt'							).pixel_values

		# verify pixel values
		assert torch.allclose(snake_case						,   snake_case							)

		# verify logits
		with torch.no_grad():
				__lowercase											=     model(snake_case							)
				__lowercase											=     outputs.logits

		print('Predicted class:'						,   logits.argmax(-1							).item()							)
		if base_model:
				__lowercase											=     timm_model.forward_features(snake_case							)
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(snake_case						,   outputs.pooler_output						,   atol=1E-3							)
		else:
				__lowercase											=     timm_model(snake_case							)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(snake_case						,   outputs.logits						,   atol=1E-3							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				Path(snake_case							).mkdir(exist_ok=snake_case							)
				print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"							)
				model.save_pretrained(snake_case							)
				print(F"Saving processor to {pytorch_dump_folder_path}"							)
				processor.save_pretrained(snake_case							)

		if push_to_hub:
				print(F"Pushing model and processor to the hub {vit_name}"							)
				model.push_to_hub(F"ybelkada/{vit_name}"							)
				processor.push_to_hub(F"ybelkada/{vit_name}"							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Tuple			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--vit_name''',
		    default='''vit_base_r50_s16_384''',
		    type=str,
		    help='''Name of the hybrid ViT timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
		)

		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore


def       snake_case   (	snake_case      : float					,		snake_case      : float					,		snake_case      : int			) ->  float:


    """simple docstring"""


    lowerCAmelCase 						=							x
    lowerCAmelCase 						=							y
    for step in range(snake_case			):  # noqa: B007
        lowerCAmelCase 						=							a * a - b * b + x
        lowerCAmelCase 						=							2 * a * b + y
        lowerCAmelCase 						=							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   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(snake_case					,		1					,		1			)			)


def       snake_case   (	snake_case      : int = 800					,		snake_case      : int = 600					,		snake_case      : float = -0.6					,		snake_case      : float = 0					,		snake_case      : float = 3.2					,		snake_case      : int = 50					,		snake_case      : bool = True					,		) ->  Image.Image:


    """simple docstring"""


    lowerCAmelCase 						=							Image.new('RGB'					,		(image_width, image_height)			)
    lowerCAmelCase 						=							img.load()

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

            lowerCAmelCase 						=							get_distance(snake_case					,		snake_case					,		snake_case			)

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

    return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     _UpperCamelCase						:  Dict							       =				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()





