





'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a     :    Dict      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    List[str]:
							UpperCAmelCase  :	Union[str, 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"""),
														    ]	)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														UpperCAmelCase  :	Union[str, 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    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=False	)   ->    Union[str, Any]:
							for i in range(config.num_hidden_layers	):
														if base_model:
																					UpperCAmelCase  :	Optional[int]							  =	""""""
														else:
																					UpperCAmelCase  :	Any							  =	"""vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCAmelCase  :	Optional[Any]							  =	state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''	)
														UpperCAmelCase  :	Optional[Any]							  =	state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''	)
														# next, add query, keys and values (in that order) to the state dict
														UpperCAmelCase  :	Any							  =	in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCAmelCase  :	Optional[int]							  =	in_proj_bias[: config.hidden_size]
														UpperCAmelCase  :	str							  =	in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase  :	List[Any]							  =	in_proj_bias[-config.hidden_size :]



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	Optional[int]							  =	["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_lowercase			,       _lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	str							  =	dct.pop(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	val



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	Union[str, Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Optional[int]							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)
							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=True	)   ->    Dict:
							UpperCAmelCase  :	int							  =	ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														UpperCAmelCase  :	List[str]							  =	8
							# set labels if required
							if not base_model:
														UpperCAmelCase  :	Optional[int]							  =	1_0_0_0
														UpperCAmelCase  :	List[Any]							  =	"""huggingface/label-files"""
														UpperCAmelCase  :	int							  =	"""imagenet-1k-id2label.json"""
														UpperCAmelCase  :	Union[str, Any]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
														UpperCAmelCase  :	str							  =	{int(_lowercase	): v for k, v in idalabel.items()}
														UpperCAmelCase  :	str							  =	idalabel
														UpperCAmelCase  :	List[str]							  =	{v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														UpperCAmelCase  :	Optional[int]							  =	3_8_4
														UpperCAmelCase  :	List[str]							  =	1_5_3_6
														UpperCAmelCase  :	int							  =	1_2
														UpperCAmelCase  :	Union[str, Any]							  =	6

							# load original model from torch hub
							UpperCAmelCase  :	Union[str, Any]							  =	torch.hub.load("""facebookresearch/dino:main"""			,       _lowercase	)
							original_model.eval()

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

							# load HuggingFace model
							if base_model:
														UpperCAmelCase  :	Optional[int]							  =	ViTModel(_lowercase			,       add_pooling_layer=_lowercase	).eval()
							else:
														UpperCAmelCase  :	Tuple							  =	ViTForImageClassification(_lowercase	).eval()
							model.load_state_dict(_lowercase	)

							# Check outputs on an image, prepared by ViTImageProcessor
							UpperCAmelCase  :	Tuple							  =	ViTImageProcessor()
							UpperCAmelCase  :	Union[str, Any]							  =	image_processor(images=prepare_img()			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	Optional[int]							  =	encoding["""pixel_values"""]
							UpperCAmelCase  :	Dict							  =	model(_lowercase	)

							if base_model:
														UpperCAmelCase  :	Any							  =	original_model(_lowercase	)
														assert torch.allclose(_lowercase			,       outputs.last_hidden_state[:, 0, :]			,       atol=1e-1	)
							else:
														UpperCAmelCase  :	List[Any]							  =	original_model(_lowercase	)
														assert logits.shape == outputs.logits.shape
														assert torch.allclose(_lowercase			,       outputs.logits			,       atol=1e-3	)

							Path(_lowercase	).mkdir(exist_ok=_lowercase	)
							print(F'''Saving model {model_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__":
							a     :    List[Any]      						=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""dino_vitb16""",
							    type=str,
							    help="""Name of the model trained with DINO 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(
							    """--base_model""",
							    action="""store_true""",
							    help="""Whether to only convert the base model (no projection head weights).""",
							)

							parser.set_defaults(base_model=True)
							a     :    Optional[Any]      						=					parser.parse_args()
							convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str | Literal[False]:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	str							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count += 1
																					UpperCAmelCase  :	Optional[Any]							  =	"""_"""
							if count > 1:
														return False
							else:
														return "".join(_lowercase	)



def    __lowerCamelCase						(      _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	List[str]							  =	[]
							while True:
														UpperCAmelCase  :	Optional[int]							  =	["""$"""] * len(_lowercase	)
														UpperCAmelCase  :	int							  =	[]
														for i in range(len(_lowercase	)	):
																					for j in range(i + 1			,       len(_lowercase	)	):
																												UpperCAmelCase  :	str							  =	compare_string(binary[i]			,       binary[j]	)
																												if k is False:
																																			UpperCAmelCase  :	Union[str, Any]							  =	"""*"""
																																			UpperCAmelCase  :	Optional[Any]							  =	"""*"""
																																			temp.append("""X"""	)
														for i in range(len(_lowercase	)	):
																					if checka[i] == "$":
																												pi.append(binary[i]	)
														if len(_lowercase	) == 0:
																					return pi
														UpperCAmelCase  :	List[Any]							  =	list(set(_lowercase	)	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Dict							  =	[]
							for minterm in minterms:
														UpperCAmelCase  :	List[str]							  =	""""""
														for _ in range(_lowercase	):
																					UpperCAmelCase  :	Dict							  =	str(minterm % 2	) + string
																					minterm //= 2
														temp.append(_lowercase	)
							return temp



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    bool:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count_n += 1
							return count_n == count



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Tuple							  =	[]
							UpperCAmelCase  :	Optional[int]							  =	[0] * len(_lowercase	)
							for i in range(len(chart[0]	)	):
														UpperCAmelCase  :	Any							  =	0
														UpperCAmelCase  :	Optional[Any]							  =	-1
														for j in range(len(_lowercase	)	):
																					if chart[j][i] == 1:
																												count += 1
																												UpperCAmelCase  :	str							  =	j
														if count == 1:
																					UpperCAmelCase  :	Optional[int]							  =	1
							for i in range(len(_lowercase	)	):
														if select[i] == 1:
																					for j in range(len(chart[0]	)	):
																												if chart[i][j] == 1:
																																			for k in range(len(_lowercase	)	):
																																										UpperCAmelCase  :	List[str]							  =	0
																					temp.append(prime_implicants[i]	)
							while True:
														UpperCAmelCase  :	int							  =	0
														UpperCAmelCase  :	Tuple							  =	-1
														UpperCAmelCase  :	Union[str, Any]							  =	0
														for i in range(len(_lowercase	)	):
																					UpperCAmelCase  :	Optional[Any]							  =	chart[i].count(1	)
																					if count_n > max_n:
																												UpperCAmelCase  :	Union[str, Any]							  =	count_n
																												UpperCAmelCase  :	Optional[Any]							  =	i

														if max_n == 0:
																					return temp

														temp.append(prime_implicants[rem]	)

														for i in range(len(chart[0]	)	):
																					if chart[rem][i] == 1:
																												for j in range(len(_lowercase	)	):
																																			UpperCAmelCase  :	Optional[Any]							  =	0



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[list[int]]:
							UpperCAmelCase  :	Optional[int]							  =	[[0 for x in range(len(_lowercase	)	)] for x in range(len(_lowercase	)	)]
							for i in range(len(_lowercase	)	):
														UpperCAmelCase  :	Tuple							  =	prime_implicants[i].count("""_"""	)
														for j in range(len(_lowercase	)	):
																					if is_for_table(prime_implicants[i]			,       binary[j]			,       _lowercase	):
																												UpperCAmelCase  :	List[Any]							  =	1

							return chart



def    __lowerCamelCase						(      )   ->    None:
							UpperCAmelCase  :	str							  =	int(input("""Enter the no. of variables\n"""	)	)
							UpperCAmelCase  :	List[Any]							  =	[
							    float(_lowercase	)
							    for x in input(
							        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""	).split()
							]
							UpperCAmelCase  :	str							  =	decimal_to_binary(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	check(_lowercase	)
							print("""Prime Implicants are:"""	)
							print(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	prime_implicant_chart(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	selection(_lowercase			,       _lowercase	)
							print("""Essential Prime Implicants are:"""	)
							print(_lowercase	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

