






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        =				logging.get_logger(__name__)




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




def 				A		(					lowercase__  :  Any  )		-> Optional[Any]:
	UpperCamelCase__ :Optional[Any]						=      ["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(lowercase__ ,		lowercase__  )




def 				A		(					lowercase__  :  Optional[int] ,		lowercase__  :  Union[str, Any] ,		lowercase__  :  Any  )		-> Optional[Any]:
	UpperCamelCase__ :Union[str, Any]						=      dct.pop(lowercase__  )
	UpperCamelCase__ :Tuple						=      val




def 				A		(					)		-> Any:
	UpperCamelCase__ :List[str]						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCamelCase__ :Union[str, Any]						=      Image.open(requests.get(lowercase__ ,		stream=lowercase__  ).raw  )
	return im




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

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

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		UpperCamelCase__ :int						=      ViTModel(lowercase__  ).eval()
	else:
		UpperCamelCase__ :List[str]						=      ViTForImageClassification(lowercase__  ).eval()
	model.load_state_dict(lowercase__  )

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

	if base_model:
		UpperCamelCase__ :str						=      timm_model.forward_features(lowercase__  )
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(lowercase__ ,		outputs.pooler_output ,		atol=1E-3  )
	else:
		UpperCamelCase__ :int						=      timm_model(lowercase__  )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(lowercase__ ,		outputs.logits ,		atol=1E-3  )

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


if __name__ == "__main__":
							UpperCamelCase        =				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        =				parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

"""simple docstring"""
import operator as op


SCREAMING_SNAKE_CASE_					     =	'''scaler.pt'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model'''
SCREAMING_SNAKE_CASE_					     =	'''random_states'''
SCREAMING_SNAKE_CASE_					     =	'''optimizer'''
SCREAMING_SNAKE_CASE_					     =	'''scheduler'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''1.10.2'''
SCREAMING_SNAKE_CASE_					     =	'''py38'''
SCREAMING_SNAKE_CASE_					     =	'''4.17.0'''
SCREAMING_SNAKE_CASE_					     =	['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
SCREAMING_SNAKE_CASE_					     =	['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
SCREAMING_SNAKE_CASE_					     =	['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
SCREAMING_SNAKE_CASE_					     =	'''2.0.1'''
SCREAMING_SNAKE_CASE_					     =	['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
SCREAMING_SNAKE_CASE_					     =	['''default''', '''reduce-overhead''', '''max-autotune''']

SCREAMING_SNAKE_CASE_					     =	{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
SCREAMING_SNAKE_CASE_					     =	[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']
