




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()
UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Dict										=[]
				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"
								__magic_name__						:						List[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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=False ):


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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						int										=["""head.weight""", """head.bias"""]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase						,  lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=dct.pop(lowerCamelCase )
				__magic_name__						:						str										=val




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Union[str, Any]										="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__						:						List[str]										=Image.open(requests.get(lowerCamelCase						,  stream=lowerCamelCase ).raw )
				return im




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

    # load original model from timm
				__magic_name__						:						Optional[int]										=timm.create_model(lowerCamelCase						,  pretrained=lowerCamelCase )
				timm_model.eval()

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

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

				if base_model:
								__magic_name__						:						List[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:
								__magic_name__						:						int										=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__":
							UpperCAmelCase_		:					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."
							)

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






import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for shard in shards:
						for i in range(a	):
									yield {"i": i, "shard": shard}


def    a      ( )     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= int(os.environ['''RANK''']	)
			SCREAMING_SNAKE_CASE     						= int(os.environ['''WORLD_SIZE''']	)

			SCREAMING_SNAKE_CASE     						= ArgumentParser()
			parser.add_argument('''--streaming'''		,						type=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						default=0	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= args.streaming
			SCREAMING_SNAKE_CASE     						= args.num_workers

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			SCREAMING_SNAKE_CASE     						= NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE     						= full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)	)

			SCREAMING_SNAKE_CASE     						= sum(1 for _ in dataloader	)
			if local_size != expected_local_size:
						raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()