'''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()
__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   str ,  _A:   Any=False					)       ->      List[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    []
		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     =    [(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 							snake_case__				(     _A:   Optional[int] ,  _A:   Optional[Any] ,  _A:   Union[str, Any]=False					)       ->      int:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   List[Any]					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    ["""head.weight""", """head.bias"""]
		for k in ignore_keys:
				state_dict.pop(_A ,  _A					)






def 							snake_case__				(     _A:   Any ,  _A:   Tuple ,  _A:   Union[str, Any]					)       ->      Tuple:

		'''simple docstring'''


		lowerCAmelCase     =    dct.pop(_A					)
		lowerCAmelCase     =    val






def 							snake_case__				(     )       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase     =    Image.open(requests.get(_A ,  stream=_A					).raw					)
		return im






@torch.no_grad()
def 							snake_case__				(     _A:   int ,  _A:   Dict					)       ->      Union[str, Any]:

		'''simple docstring'''


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

    # load original model from timm
		lowerCAmelCase     =    timm.create_model(_A ,  pretrained=_A					)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowerCAmelCase     =    timm_model.state_dict()
		if base_model:
				remove_classification_head_(_A					)
		lowerCAmelCase     =    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     =    ViTModel(_A					).eval()
		else:
				lowerCAmelCase     =    ViTForImageClassification(_A					).eval()
		model.load_state_dict(_A					)

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

		if base_model:
				lowerCAmelCase     =    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     =    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__":
						__lowercase        =    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.'''
						)

						__lowercase        =    parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





'''simple docstring'''




from functools import lru_cache






@lru_cache
def 							snake_case__				(     _A:   int					)       ->      int:

		'''simple docstring'''


		if num < 0:
				raise ValueError("""Number should not be negative."""					)

		return 1 if num in (0, 1) else num * factorial(num - 1					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




