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()
lowerCAmelCase__     :   Optional[int]		=   logging.get_logger(__name__)
def  UpperCamelCase__						(				A__			, A__=False    )   ->					List[Any]:
				snake_case__					:	List[str]												=      []
				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"
								snake_case__					:	Dict												=      [(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    )   ->					Dict:
				for i in range(config.num_hidden_layers    ):
								if base_model:
												snake_case__					:	Union[str, Any]												=      ''
								else:
												snake_case__					:	Optional[Any]												=      'vit.'
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								snake_case__					:	Dict												=      state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""    )
								snake_case__					:	Tuple												=      state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""    )
								# next, add query, keys and values (in that order) to the state dict
								snake_case__					:	Union[str, Any]												=      in_proj_weight[
								    : config.hidden_size, :
								]
								snake_case__					:	Union[str, Any]												=      in_proj_bias[: config.hidden_size]
								snake_case__					:	Union[str, Any]												=      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								snake_case__					:	int												=      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								snake_case__					:	Dict												=      in_proj_weight[
								    -config.hidden_size :, :
								]
								snake_case__					:	Optional[Any]												=      in_proj_bias[-config.hidden_size :]
def  UpperCamelCase__						(				A__    )   ->					Optional[Any]:
				snake_case__					:	int												=      ['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(lowerCAmelCase_			, lowerCAmelCase_    )
def  UpperCamelCase__						(				A__			, A__			, A__    )   ->					List[str]:
				snake_case__					:	Tuple												=      dct.pop(lowerCAmelCase_    )
				snake_case__					:	Optional[Any]												=      val
def  UpperCamelCase__						(				)   ->					Any:
				snake_case__					:	Tuple												=      'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__					:	List[Any]												=      Image.open(requests.get(lowerCAmelCase_			, stream=lowerCAmelCase_    ).raw    )
				return im
@torch.no_grad()
def  UpperCamelCase__						(				A__			, A__    )   ->					Any:
				snake_case__					:	Any												=      ViTConfig()
				snake_case__					:	List[Any]												=      False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								snake_case__					:	List[Any]												=      True
								snake_case__					:	str												=      int(vit_name[-12:-10]    )
								snake_case__					:	Any												=      int(vit_name[-9:-6]    )
				else:
								snake_case__					:	str												=      1000
								snake_case__					:	List[Any]												=      'huggingface/label-files'
								snake_case__					:	int												=      'imagenet-1k-id2label.json'
								snake_case__					:	Any												=      json.load(open(hf_hub_download(lowerCAmelCase_			, lowerCAmelCase_			, repo_type='dataset'    )			, 'r'    )    )
								snake_case__					:	Optional[int]												=      {int(lowerCAmelCase_    ): v for k, v in idalabel.items()}
								snake_case__					:	str												=      idalabel
								snake_case__					:	int												=      {v: k for k, v in idalabel.items()}
								snake_case__					:	List[Any]												=      int(vit_name[-6:-4]    )
								snake_case__					:	Optional[int]												=      int(vit_name[-3:]    )
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith('tiny'    ):
												snake_case__					:	Optional[int]												=      192
												snake_case__					:	Dict												=      768
												snake_case__					:	List[Any]												=      12
												snake_case__					:	Union[str, Any]												=      3
								elif vit_name[9:].startswith('small'    ):
												snake_case__					:	Optional[Any]												=      384
												snake_case__					:	str												=      1536
												snake_case__					:	str												=      12
												snake_case__					:	Union[str, Any]												=      6
								else:
												pass
				else:
								if vit_name[4:].startswith('small'    ):
												snake_case__					:	int												=      768
												snake_case__					:	str												=      2304
												snake_case__					:	List[str]												=      8
												snake_case__					:	Optional[int]												=      8
								elif vit_name[4:].startswith('base'    ):
												pass
								elif vit_name[4:].startswith('large'    ):
												snake_case__					:	Any												=      1024
												snake_case__					:	Optional[int]												=      4096
												snake_case__					:	Union[str, Any]												=      24
												snake_case__					:	Any												=      16
								elif vit_name[4:].startswith('huge'    ):
												snake_case__					:	str												=      1280
												snake_case__					:	Dict												=      5120
												snake_case__					:	str												=      32
												snake_case__					:	str												=      16

    # load original model from timm
				snake_case__					:	Union[str, Any]												=      timm.create_model(lowerCAmelCase_			, pretrained=lowerCAmelCase_    )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				snake_case__					:	str												=      timm_model.state_dict()
				if base_model:
								remove_classification_head_(lowerCAmelCase_    )
				snake_case__					:	Optional[Any]												=      create_rename_keys(lowerCAmelCase_			, lowerCAmelCase_    )
				for src, dest in rename_keys:
								rename_key(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_    )
				read_in_q_k_v(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_    )

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								snake_case__					:	Union[str, Any]												=      ViTModel(lowerCAmelCase_    ).eval()
				else:
								snake_case__					:	str												=      ViTForImageClassification(lowerCAmelCase_    ).eval()
				model.load_state_dict(lowerCAmelCase_    )

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

				if base_model:
								snake_case__					:	Optional[Any]												=      timm_model.forward_features(lowerCAmelCase_    )
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(lowerCAmelCase_			, outputs.pooler_output			, atol=1e-3    )
				else:
								snake_case__					:	Union[str, Any]												=      timm_model(lowerCAmelCase_    )
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCAmelCase_			, outputs.logits			, atol=1e-3    )

				Path(lowerCAmelCase_    ).mkdir(exist_ok=lowerCAmelCase_    )
				print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""    )
				model.save_pretrained(lowerCAmelCase_    )
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""    )
				image_processor.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
				lowerCAmelCase__     :   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.'''
				)

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







'''simple docstring'''

import math



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      bool:
		_a     :      Optional[int]        =							math.loga(math.sqrt(4 * positive_integer + 1  ) / 2 + 1 / 2  )

		return exponent == int(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_ = 1 / 12345  )			->      int:
		_a     :      int        =							0
		_a     :      Optional[Any]        =							0

		_a     :      int        =							3
		while True:
				_a     :      Tuple        =							(integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							int(lowerCAmelCase_  )
						total_partitions += 1
						if check_partition_perfect(lowerCAmelCase_  ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(lowerCAmelCase_  )
				integer += 1


if __name__ == "__main__":
							print(f"""{solution() = }""")




