




"""simple docstring"""
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_				:    Tuple										=     logging.get_logger(__name__)





def 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    Tuple=False     )  ->     str:
			__lowerCamelCase					:	Any	       =				[]
			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''')     )

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ('cls_token', 'vit.embeddings.cls_token'),
			        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
			        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
			        ('pos_embed', 'vit.embeddings.position_embeddings'),
			    ]     )

			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"
						__lowerCamelCase					:	Any	       =				[(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'),
						    ]     )

			return rename_keys





def 						UpperCAmelCase				(			A__:    Tuple							,				A__:    Any							,				A__:    int=False     )  ->     int:
			for i in range(config.num_hidden_layers     ):
						if base_model:
									__lowerCamelCase					:	str	       =				''
						else:
									__lowerCamelCase					:	List[str]	       =				'vit.'
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						__lowerCamelCase					:	str	       =				state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''     )
						__lowerCamelCase					:	Tuple	       =				state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''     )
						# next, add query, keys and values (in that order) to the state dict
						__lowerCamelCase					:	Dict	       =				in_proj_weight[
						    : config.hidden_size, :
						]
						__lowerCamelCase					:	Union[str, Any]	       =				in_proj_bias[: config.hidden_size]
						__lowerCamelCase					:	List[str]	       =				in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__lowerCamelCase					:	int	       =				in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__lowerCamelCase					:	List[Any]	       =				in_proj_weight[
						    -config.hidden_size :, :
						]
						__lowerCamelCase					:	str	       =				in_proj_bias[-config.hidden_size :]





def 						UpperCAmelCase				(			A__:    List[str]     )  ->     Optional[int]:
			__lowerCamelCase					:	Tuple	       =				['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    Optional[int]							,				A__:    Optional[Any]     )  ->     str:
			__lowerCamelCase					:	Any	       =				dct.pop(A__     )
			__lowerCamelCase					:	Optional[int]	       =				val





def 						UpperCAmelCase				(			)  ->     int:
			__lowerCamelCase					:	Any	       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
			__lowerCamelCase					:	Any	       =				Image.open(requests.get(A__							,				stream=A__     ).raw     )
			return im





@torch.no_grad()
def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    List[str]     )  ->     Tuple:
			__lowerCamelCase					:	List[Any]	       =				ViTConfig()
			__lowerCamelCase					:	Union[str, Any]	       =				False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						__lowerCamelCase					:	Dict	       =				True
						__lowerCamelCase					:	Optional[Any]	       =				int(vit_name[-12:-10]     )
						__lowerCamelCase					:	Union[str, Any]	       =				int(vit_name[-9:-6]     )
			else:
						__lowerCamelCase					:	List[str]	       =				1000
						__lowerCamelCase					:	Optional[Any]	       =				'huggingface/label-files'
						__lowerCamelCase					:	Union[str, Any]	       =				'imagenet-1k-id2label.json'
						__lowerCamelCase					:	Optional[int]	       =				json.load(open(hf_hub_download(A__							,				A__							,				repo_type='dataset'     )							,				'r'     )     )
						__lowerCamelCase					:	Optional[int]	       =				{int(A__     ): v for k, v in idalabel.items()}
						__lowerCamelCase					:	List[str]	       =				idalabel
						__lowerCamelCase					:	Union[str, Any]	       =				{v: k for k, v in idalabel.items()}
						__lowerCamelCase					:	Tuple	       =				int(vit_name[-6:-4]     )
						__lowerCamelCase					:	str	       =				int(vit_name[-3:]     )
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith('tiny'     ):
									__lowerCamelCase					:	Dict	       =				192
									__lowerCamelCase					:	Union[str, Any]	       =				768
									__lowerCamelCase					:	int	       =				12
									__lowerCamelCase					:	Any	       =				3
						elif vit_name[9:].startswith('small'     ):
									__lowerCamelCase					:	Optional[Any]	       =				384
									__lowerCamelCase					:	Tuple	       =				1536
									__lowerCamelCase					:	Optional[Any]	       =				12
									__lowerCamelCase					:	Union[str, Any]	       =				6
						else:
									pass
			else:
						if vit_name[4:].startswith('small'     ):
									__lowerCamelCase					:	Any	       =				768
									__lowerCamelCase					:	Dict	       =				2304
									__lowerCamelCase					:	Tuple	       =				8
									__lowerCamelCase					:	List[Any]	       =				8
						elif vit_name[4:].startswith('base'     ):
									pass
						elif vit_name[4:].startswith('large'     ):
									__lowerCamelCase					:	str	       =				1024
									__lowerCamelCase					:	int	       =				4096
									__lowerCamelCase					:	Dict	       =				24
									__lowerCamelCase					:	str	       =				16
						elif vit_name[4:].startswith('huge'     ):
									__lowerCamelCase					:	Optional[Any]	       =				1280
									__lowerCamelCase					:	Optional[Any]	       =				5120
									__lowerCamelCase					:	Dict	       =				32
									__lowerCamelCase					:	Optional[int]	       =				16

    # load original model from timm
			__lowerCamelCase					:	Tuple	       =				timm.create_model(A__							,				pretrained=A__     )
			timm_model.eval()

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

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						__lowerCamelCase					:	int	       =				ViTModel(A__     ).eval()
			else:
						__lowerCamelCase					:	List[str]	       =				ViTForImageClassification(A__     ).eval()
			model.load_state_dict(A__     )

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						__lowerCamelCase					:	Dict	       =				DeiTImageProcessor(size=config.image_size     )
			else:
						__lowerCamelCase					:	Union[str, Any]	       =				ViTImageProcessor(size=config.image_size     )
			__lowerCamelCase					:	Union[str, Any]	       =				image_processor(images=prepare_img()							,				return_tensors='pt'     )
			__lowerCamelCase					:	List[str]	       =				encoding['pixel_values']
			__lowerCamelCase					:	str	       =				model(A__     )

			if base_model:
						__lowerCamelCase					:	int	       =				timm_model.forward_features(A__     )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(A__							,				outputs.pooler_output							,				atol=1E-3     )
			else:
						__lowerCamelCase					:	Tuple	       =				timm_model(A__     )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(A__							,				outputs.logits							,				atol=1E-3     )

			Path(A__     ).mkdir(exist_ok=A__     )
			print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''     )
			model.save_pretrained(A__     )
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''     )
			image_processor.save_pretrained(A__     )


if __name__ == "__main__":
					a_				:    str										=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--vit_name''',
					    default='''vit_base_patch16_224''',
					    type=str,
					    help='''Name of the 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.'''
					)

					a_				:    int										=     parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









"""simple docstring"""
import random





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    List[str]							,				A__:    Union[str, Any]     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				a[left_index]
			__lowerCamelCase					:	int	       =				left_index + 1
			for j in range(left_index + 1							,				A__     ):
						if a[j] < pivot:
									__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				a[i], a[j]
									i += 1
			__lowerCamelCase       ,      __lowerCamelCase					:	str	       =				a[i - 1], a[left_index]
			return i - 1





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Tuple							,				A__:    Tuple     )  ->     Dict:
			if left < right:
						__lowerCamelCase					:	Optional[int]	       =				random.randint(A__							,				right - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				(
						    a[left],
						    a[pivot],
						)  # switches the pivot with the left most bound
						__lowerCamelCase					:	Union[str, Any]	       =				partition(A__							,				A__							,				A__     )
						quick_sort_random(
						    A__							,				A__							,				A__     )  # recursive quicksort to the left of the pivot point
						quick_sort_random(
						    A__							,				pivot_index + 1							,				A__     )  # recursive quicksort to the right of the pivot point





def 						UpperCAmelCase				(			)  ->     int:
			__lowerCamelCase					:	Dict	       =				input('Enter numbers separated by a comma:\n'     ).strip()
			__lowerCamelCase					:	int	       =				[int(A__     ) for item in user_input.split(','     )]

			quick_sort_random(A__							,				0							,				len(A__     )     )

			print(A__     )


if __name__ == "__main__":
					main()



