
"""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						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, 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					:      str          =   [(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_,						a_,						a_=False      ):





		'''simple docstring'''


		for i in range(config.num_hidden_layers      ):
				if base_model:
						lowerCamelCase					:      Tuple          =   ''
				else:
						lowerCamelCase					:      Any          =   'vit.'
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowerCamelCase					:      Any          =   state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""      )
				lowerCamelCase					:      Optional[int]          =   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					:      Dict          =   in_proj_bias[: config.hidden_size]
				lowerCamelCase					:      List[str]          =   in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCamelCase					:      Dict          =   in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCamelCase					:      int          =   in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCamelCase					:      List[Any]          =   in_proj_bias[-config.hidden_size :]
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   ['head.weight', 'head.bias']
		for k in ignore_keys:
				state_dict.pop(__A,						__A      )
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   dct.pop(__A      )
		lowerCamelCase					:      Any          =   val
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      Optional[int]          =   Image.open(requests.get(__A,						stream=__A      ).raw      )
		return im
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   ViTConfig()
		lowerCamelCase					:      List[Any]          =   False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				lowerCamelCase					:      List[str]          =   True
				lowerCamelCase					:      int          =   int(vit_name[-12:-10]      )
				lowerCamelCase					:      Optional[Any]          =   int(vit_name[-9:-6]      )
		else:
				lowerCamelCase					:      Union[str, Any]          =   1000
				lowerCamelCase					:      List[str]          =   'huggingface/label-files'
				lowerCamelCase					:      List[Any]          =   'imagenet-1k-id2label.json'
				lowerCamelCase					:      Tuple          =   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					:      Optional[Any]          =   idalabel
				lowerCamelCase					:      Tuple          =   {v: k for k, v in idalabel.items()}
				lowerCamelCase					:      Any          =   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					:      Union[str, Any]          =   192
						lowerCamelCase					:      List[str]          =   768
						lowerCamelCase					:      List[str]          =   12
						lowerCamelCase					:      Any          =   3
				elif vit_name[9:].startswith('small'      ):
						lowerCamelCase					:      Optional[int]          =   384
						lowerCamelCase					:      int          =   1536
						lowerCamelCase					:      List[str]          =   12
						lowerCamelCase					:      Any          =   6
				else:
						pass
		else:
				if vit_name[4:].startswith('small'      ):
						lowerCamelCase					:      str          =   768
						lowerCamelCase					:      Optional[Any]          =   2304
						lowerCamelCase					:      List[str]          =   8
						lowerCamelCase					:      int          =   8
				elif vit_name[4:].startswith('base'      ):
						pass
				elif vit_name[4:].startswith('large'      ):
						lowerCamelCase					:      List[str]          =   1024
						lowerCamelCase					:      Union[str, Any]          =   4096
						lowerCamelCase					:      Optional[int]          =   24
						lowerCamelCase					:      int          =   16
				elif vit_name[4:].startswith('huge'      ):
						lowerCamelCase					:      List[Any]          =   1280
						lowerCamelCase					:      List[str]          =   5120
						lowerCamelCase					:      Any          =   32
						lowerCamelCase					:      str          =   16

    # load original model from timm
		lowerCamelCase					:      int          =   timm.create_model(__A,						pretrained=__A      )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowerCamelCase					:      List[str]          =   timm_model.state_dict()
		if base_model:
				remove_classification_head_(__A      )
		lowerCamelCase					:      Any          =   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					:      Dict          =   ViTModel(__A      ).eval()
		else:
				lowerCamelCase					:      str          =   ViTForImageClassification(__A      ).eval()
		model.load_state_dict(__A      )

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

		if base_model:
				lowerCamelCase					:      Dict          =   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					:      Optional[Any]          =   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						=			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						=			parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








"""simple docstring"""

def 		UpperCAmelCase (		a_ = 10      ):





		'''simple docstring'''


		if not isinstance(a_,						a_      ) or n < 0:
				raise ValueError('Invalid input'      )
		lowerCamelCase					:      Union[str, Any]          =   10**n
		lowerCamelCase					:      int          =   2_8433 * (pow(2,						783_0457,						a_      )) + 1
		return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"""{solution(1_0) = }""")






