


'''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	:	Union[str, Any]     =     logging.get_logger(__name__)





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Union[str, Any]=False ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[]
		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_		:							Tuple			       =						[(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      (		lowerCamelCase_  :str				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :int=False ):







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





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							Any			       =						["""head.weight""", """head.bias"""]
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase_				,      lowerCamelCase_ )





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						dct.pop(lowerCamelCase_ )
		snake_case_		:							List[str]			       =						val





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							str			       =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		snake_case_		:							str			       =						Image.open(requests.get(lowerCamelCase_				,      stream=lowerCamelCase_ ).raw )
		return im





@torch.no_grad()
def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						ViTConfig()
		snake_case_		:							str			       =						False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				snake_case_		:							int			       =						True
				snake_case_		:							Any			       =						int(vit_name[-12:-10] )
				snake_case_		:							Optional[Any]			       =						int(vit_name[-9:-6] )
		else:
				snake_case_		:							Tuple			       =						10_00
				snake_case_		:							Tuple			       =						"""huggingface/label-files"""
				snake_case_		:							List[str]			       =						"""imagenet-1k-id2label.json"""
				snake_case_		:							int			       =						json.load(open(hf_hub_download(lowerCamelCase_				,      lowerCamelCase_				,      repo_type="""dataset""" )				,      """r""" ) )
				snake_case_		:							Tuple			       =						{int(lowerCamelCase_ ): v for k, v in idalabel.items()}
				snake_case_		:							Optional[Any]			       =						idalabel
				snake_case_		:							int			       =						{v: k for k, v in idalabel.items()}
				snake_case_		:							Any			       =						int(vit_name[-6:-4] )
				snake_case_		:							Optional[Any]			       =						int(vit_name[-3:] )
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith("""tiny""" ):
						snake_case_		:							Optional[int]			       =						1_92
						snake_case_		:							List[str]			       =						7_68
						snake_case_		:							List[Any]			       =						12
						snake_case_		:							List[str]			       =						3
				elif vit_name[9:].startswith("""small""" ):
						snake_case_		:							str			       =						3_84
						snake_case_		:							str			       =						15_36
						snake_case_		:							Optional[Any]			       =						12
						snake_case_		:							List[Any]			       =						6
				else:
						pass
		else:
				if vit_name[4:].startswith("""small""" ):
						snake_case_		:							Any			       =						7_68
						snake_case_		:							List[Any]			       =						23_04
						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_		:							List[str]			       =						10_24
						snake_case_		:							Tuple			       =						40_96
						snake_case_		:							Optional[Any]			       =						24
						snake_case_		:							Tuple			       =						16
				elif vit_name[4:].startswith("""huge""" ):
						snake_case_		:							List[Any]			       =						12_80
						snake_case_		:							str			       =						51_20
						snake_case_		:							Optional[int]			       =						32
						snake_case_		:							List[str]			       =						16

    # load original model from timm
		snake_case_		:							List[Any]			       =						timm.create_model(lowerCamelCase_				,      pretrained=lowerCamelCase_ )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		snake_case_		:							Union[str, Any]			       =						timm_model.state_dict()
		if base_model:
				remove_classification_head_(lowerCamelCase_ )
		snake_case_		:							str			       =						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_		:							Optional[int]			       =						ViTModel(lowerCamelCase_ ).eval()
		else:
				snake_case_		:							List[Any]			       =						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_		:							Optional[Any]			       =						ViTImageProcessor(size=config.image_size )
		snake_case_		:							Tuple			       =						image_processor(images=prepare_img()				,      return_tensors="""pt""" )
		snake_case_		:							List[str]			       =						encoding["""pixel_values"""]
		snake_case_		:							List[Any]			       =						model(lowerCamelCase_ )

		if base_model:
				snake_case_		:							Union[str, 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_		:							List[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__":
							__A	:	Optional[int]     =     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	:	Any     =     parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))