





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__  :     int   			=logging.get_logger(__name__)





def        lowerCamelCase__   (  _UpperCAmelCase     , _UpperCAmelCase=False			)  ->					str:
				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        lowerCamelCase__   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=False			)  ->					int:
				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        lowerCamelCase__   (  _UpperCAmelCase			)  ->					Optional[Any]:
				lowerCamelCase			=["""head.weight""", """head.bias"""]
				for k in ignore_keys:
								state_dict.pop(_UpperCAmelCase     , _UpperCAmelCase			)





def        lowerCamelCase__   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Tuple:
				lowerCamelCase			=dct.pop(_UpperCAmelCase			)
				lowerCamelCase			=val





def        lowerCamelCase__   (  )  ->					Dict:
				lowerCamelCase			="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase			=Image.open(requests.get(_UpperCAmelCase     , stream=_UpperCAmelCase			).raw			)
				return im





@torch.no_grad()
def        lowerCamelCase__   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					str:
				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			=10_00
								lowerCamelCase			="""huggingface/label-files"""
								lowerCamelCase			="""imagenet-1k-id2label.json"""
								lowerCamelCase			=json.load(open(hf_hub_download(_UpperCAmelCase     , _UpperCAmelCase     , repo_type="""dataset"""			)     , """r"""			)			)
								lowerCamelCase			={int(_UpperCAmelCase			): 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			=1_92
												lowerCamelCase			=7_68
												lowerCamelCase			=12
												lowerCamelCase			=3
								elif vit_name[9:].startswith("""small"""			):
												lowerCamelCase			=3_84
												lowerCamelCase			=15_36
												lowerCamelCase			=12
												lowerCamelCase			=6
								else:
												pass
				else:
								if vit_name[4:].startswith("""small"""			):
												lowerCamelCase			=7_68
												lowerCamelCase			=23_04
												lowerCamelCase			=8
												lowerCamelCase			=8
								elif vit_name[4:].startswith("""base"""			):
												pass
								elif vit_name[4:].startswith("""large"""			):
												lowerCamelCase			=10_24
												lowerCamelCase			=40_96
												lowerCamelCase			=24
												lowerCamelCase			=16
								elif vit_name[4:].startswith("""huge"""			):
												lowerCamelCase			=12_80
												lowerCamelCase			=51_20
												lowerCamelCase			=32
												lowerCamelCase			=16

    # load original model from timm
				lowerCamelCase			=timm.create_model(_UpperCAmelCase     , pretrained=_UpperCAmelCase			)
				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_(_UpperCAmelCase			)
				lowerCamelCase			=create_rename_keys(_UpperCAmelCase     , _UpperCAmelCase			)
				for src, dest in rename_keys:
								rename_key(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)
				read_in_q_k_v(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								lowerCamelCase			=ViTModel(_UpperCAmelCase			).eval()
				else:
								lowerCamelCase			=ViTForImageClassification(_UpperCAmelCase			).eval()
				model.load_state_dict(_UpperCAmelCase			)

				# 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(_UpperCAmelCase			)

				if base_model:
								lowerCamelCase			=timm_model.forward_features(_UpperCAmelCase			)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(_UpperCAmelCase     , outputs.pooler_output     , atol=1e-3			)
				else:
								lowerCamelCase			=timm_model(_UpperCAmelCase			)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(_UpperCAmelCase     , outputs.logits     , atol=1e-3			)

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


if __name__ == "__main__":
							UpperCAmelCase__  :     Tuple   			=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__  :     str   			=parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)












# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  :     Union[str, Any]   			=subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  :     List[str]   			=(
    subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode('''utf-8''').split()
)

UpperCAmelCase__  :     Tuple   			='''|'''.join(sys.argv[1:])
UpperCAmelCase__  :     List[str]   			=re.compile(rF"^({joined_dirs}).*?\.py$")

UpperCAmelCase__  :     Any   			=[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')





