




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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A						:							List[str]							=  logging.get_logger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]

			# fmt: off
			# stem:
			rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")   )
			rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")   )

			rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")   )
			rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")   )

			# backbone
			rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")   )
			rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")   )
			rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")   )

			for stage_idx in range(len(config.backbone_config.depths   )   ):
						for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")   )

						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")   )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")   )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")   )

			# transformer encoder
			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""")   )

			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"
						SCREAMING_SNAKE_CASE           =					[(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"""),
						    ]   )
			# fmt: on

			return rename_keys
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			for i in range(config.num_hidden_layers   ):
						if base_model:
									SCREAMING_SNAKE_CASE           =					""""""
						else:
									SCREAMING_SNAKE_CASE           =					"""vit."""
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""   )
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""   )
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    : config.hidden_size, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_bias[: config.hidden_size]
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    -config.hidden_size :, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_bias[-config.hidden_size :]
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					["""head.weight""", """head.bias"""]
			for k in ignore_keys:
						state_dict.pop(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					dct.pop(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					val
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			SCREAMING_SNAKE_CASE           =					Image.open(requests.get(SCREAMING_SNAKE_CASE					,	stream=SCREAMING_SNAKE_CASE   ).raw   )
			return im
@torch.no_grad()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					BitConfig(
			    global_padding="""same"""					,	layer_type="""bottleneck"""					,	depths=(3, 4, 9)					,	out_features=["""stage3"""]					,	embedding_dynamic_padding=SCREAMING_SNAKE_CASE					,	)
			SCREAMING_SNAKE_CASE           =					ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE					,	image_size=3_84					,	num_labels=10_00   )
			SCREAMING_SNAKE_CASE           =					False

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

			# load state_dict of original model, remove and rename some keys
			SCREAMING_SNAKE_CASE           =					timm_model.state_dict()
			if base_model:
						remove_classification_head_(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					create_rename_keys(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			for src, dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			read_in_q_k_v(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					"""huggingface/label-files"""
			SCREAMING_SNAKE_CASE           =					"""imagenet-1k-id2label.json"""
			SCREAMING_SNAKE_CASE           =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	repo_type="""dataset"""   )					,	"""r"""   )   )
			SCREAMING_SNAKE_CASE           =					{int(SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE           =					idalabel
			SCREAMING_SNAKE_CASE           =					{v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						SCREAMING_SNAKE_CASE           =					ViTHybridModel(SCREAMING_SNAKE_CASE   ).eval()
			else:
						SCREAMING_SNAKE_CASE           =					ViTHybridForImageClassification(SCREAMING_SNAKE_CASE   ).eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE   )

			# create image processor
			SCREAMING_SNAKE_CASE           =					create_transform(**resolve_data_config({}					,	model=SCREAMING_SNAKE_CASE   )   )
			SCREAMING_SNAKE_CASE           =					transform.transforms

			SCREAMING_SNAKE_CASE           =					{
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			SCREAMING_SNAKE_CASE           =					ViTHybridImageProcessor(
			    do_resize=SCREAMING_SNAKE_CASE					,	size={"""shortest_edge""": timm_transforms[0].size}					,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]					,	do_center_crop=SCREAMING_SNAKE_CASE					,	crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}					,	do_normalize=SCREAMING_SNAKE_CASE					,	image_mean=timm_transforms[-1].mean.tolist()					,	image_std=timm_transforms[-1].std.tolist()					,	)

			SCREAMING_SNAKE_CASE           =					prepare_img()
			SCREAMING_SNAKE_CASE           =					transform(SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
			SCREAMING_SNAKE_CASE           =					processor(SCREAMING_SNAKE_CASE					,	return_tensors="""pt"""   ).pixel_values

			# verify pixel values
			assert torch.allclose(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			# verify logits
			with torch.no_grad():
						SCREAMING_SNAKE_CASE           =					model(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					outputs.logits

			print("""Predicted class:"""					,	logits.argmax(-1   ).item()   )
			if base_model:
						SCREAMING_SNAKE_CASE           =					timm_model.forward_features(SCREAMING_SNAKE_CASE   )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(SCREAMING_SNAKE_CASE					,	outputs.pooler_output					,	atol=1E-3   )
			else:
						SCREAMING_SNAKE_CASE           =					timm_model(SCREAMING_SNAKE_CASE   )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(SCREAMING_SNAKE_CASE					,	outputs.logits					,	atol=1E-3   )
			print("""Looks ok!"""   )

			if pytorch_dump_folder_path is not None:
						Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
						print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""   )
						model.save_pretrained(SCREAMING_SNAKE_CASE   )
						print(f"""Saving processor to {pytorch_dump_folder_path}"""   )
						processor.save_pretrained(SCREAMING_SNAKE_CASE   )

			if push_to_hub:
						print(f"""Pushing model and processor to the hub {vit_name}"""   )
						model.push_to_hub(f"""ybelkada/{vit_name}"""   )
						processor.push_to_hub(f"""ybelkada/{vit_name}"""   )


if __name__ == "__main__":
		__A						:							Optional[Any]							=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--vit_name""",
		    default="""vit_base_r50_s16_384""",
		    type=str,
		    help="""Name of the hybrid 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."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
		)

		__A						:							List[Any]							=  parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)












from __future__ import annotations




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Optional[int]       ,     a       :	int = 0			)			->					str:
										SCREAMING_SNAKE_CASE           =					key





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

										return [chr(ord(a			) ^ key			) for ch in content]





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

										return [chr(ord(a			) ^ key			) for ch in content]





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key can be any size
										while key > 255:
													key -= 255

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

										for ch in content:
													ans += chr(ord(a			) ^ key			)

										return ans





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key can be any size
										while key > 255:
													key -= 255

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

										for ch in content:
													ans += chr(ord(a			) ^ key			)

										return ans





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	str       ,     a       :	int = 0			)			->					bool:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										try:
													with open(a			) as fin, open("""encrypt.out"""       ,     """w+"""			) as fout:
																# actual encrypt-process
																for line in fin:
																			fout.write(self.encrypt_string(a       ,     a			)			)

										except OSError:
													return False

										return True







							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int			)			->					bool:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										try:
													with open(a			) as fin, open("""decrypt.out"""       ,     """w+"""			) as fout:
																# actual encrypt-process
																for line in fin:
																			fout.write(self.decrypt_string(a       ,     a			)			)

										except OSError:
													return False

										return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")






