






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   :   List[str]      =    logging.get_logger(__name__)





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase=False       ):




	'''simple docstring'''







	__lowercase   							=	[]
	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"
		__lowercase   							=	[(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 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase=False       ):




	'''simple docstring'''







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





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(lowerCamelCase					,	lowerCamelCase       )





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	dct.pop(lowerCamelCase       )
	__lowercase   							=	val





def 					snake_case	(  ):




	'''simple docstring'''







	__lowercase   							=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	__lowercase   							=	Image.open(requests.get(lowerCamelCase					,	stream=lowerCamelCase       ).raw       )
	return im





@torch.no_grad()
def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	ViTConfig()
	__lowercase   							=	False
	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	if vit_name[-5:] == "in21k":
		__lowercase   							=	True
		__lowercase   							=	int(vit_name[-12:-10]       )
		__lowercase   							=	int(vit_name[-9:-6]       )
	else:
		__lowercase   							=	1_000
		__lowercase   							=	"""huggingface/label-files"""
		__lowercase   							=	"""imagenet-1k-id2label.json"""
		__lowercase   							=	json.load(open(hf_hub_download(lowerCamelCase					,	lowerCamelCase					,	repo_type="""dataset"""       )					,	"""r"""       )       )
		__lowercase   							=	{int(lowerCamelCase       ): v for k, v in idalabel.items()}
		__lowercase   							=	idalabel
		__lowercase   							=	{v: k for k, v in idalabel.items()}
		__lowercase   							=	int(vit_name[-6:-4]       )
		__lowercase   							=	int(vit_name[-3:]       )
	# size of the architecture
	if "deit" in vit_name:
		if vit_name[9:].startswith("""tiny"""       ):
			__lowercase   							=	192
			__lowercase   							=	768
			__lowercase   							=	12
			__lowercase   							=	3
		elif vit_name[9:].startswith("""small"""       ):
			__lowercase   							=	384
			__lowercase   							=	1_536
			__lowercase   							=	12
			__lowercase   							=	6
		else:
			pass
	else:
		if vit_name[4:].startswith("""small"""       ):
			__lowercase   							=	768
			__lowercase   							=	2_304
			__lowercase   							=	8
			__lowercase   							=	8
		elif vit_name[4:].startswith("""base"""       ):
			pass
		elif vit_name[4:].startswith("""large"""       ):
			__lowercase   							=	1_024
			__lowercase   							=	4_096
			__lowercase   							=	24
			__lowercase   							=	16
		elif vit_name[4:].startswith("""huge"""       ):
			__lowercase   							=	1_280
			__lowercase   							=	5_120
			__lowercase   							=	32
			__lowercase   							=	16

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

	# load state_dict of original model, remove and rename some keys
	__lowercase   							=	timm_model.state_dict()
	if base_model:
		remove_classification_head_(lowerCamelCase       )
	__lowercase   							=	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":
		__lowercase   							=	ViTModel(lowerCamelCase       ).eval()
	else:
		__lowercase   							=	ViTForImageClassification(lowerCamelCase       ).eval()
	model.load_state_dict(lowerCamelCase       )

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

	if base_model:
		__lowercase   							=	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:
		__lowercase   							=	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   :   Union[str, Any]      =    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   :   Optional[int]      =    parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8				)			->						str:
       SCREAMING_SNAKE_CASE_							:				str		=ascii_letters + digits + punctuation
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Any		=i // 3
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       SCREAMING_SNAKE_CASE_							:				int		=(
           chars_incl
           + random(UpperCAmelCase_			,							quotient + remainder				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
       )
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(UpperCAmelCase_				)
       shuffle(UpperCAmelCase_				)
       return "".join(UpperCAmelCase_				)

       # random is a generalised function for letters, characters and numbers
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Tuple				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Tuple				)			->						Dict:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     List[str]				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int = 8				)			->						bool:

       if len(UpperCAmelCase_				) < min_length:
              # Your Password must be at least 8 characters long
              return False

       SCREAMING_SNAKE_CASE_							:				List[str]		=any(char in ascii_uppercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=any(char in ascii_lowercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=any(char in digits for char in password				)
       SCREAMING_SNAKE_CASE_							:				str		=any(char in punctuation for char in password				)

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Any		=int(input('''Please indicate the max length of your password: '''				).strip()				)
       SCREAMING_SNAKE_CASE_							:				Any		=input(
           '''Please indicate the characters that must be in your password: '''				).strip()
       print('''Password generated:'''			,							password_generator(UpperCAmelCase_				)				)
       print(
           '''Alternative Password generated:'''			,							alternative_password_generator(UpperCAmelCase_			,							UpperCAmelCase_				)			,							)
       print('''[If you are thinking of using this passsword, You better save it.]'''				)


if __name__ == "__main__":
 main()
