
'''simple docstring'''

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def 	A  (   UpperCamelCase_       :  str    ,						UpperCamelCase_       :  str   )       ->							str | Literal[False]:







			'''simple docstring'''



			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				0
			for i in range(len(UpperCamelCase_   )   ):
						if lista[i] != lista[i]:
									count += 1
									lowerCAmelCase__										=				"_"
			if count > 1:
						return False
			else:
						return "".join(UpperCamelCase_   )







def 	A  (   UpperCamelCase_       :  list[str]   )       ->							list[str]:







			'''simple docstring'''



			lowerCAmelCase__										=				[]
			while True:
						lowerCAmelCase__										=				["$"] * len(UpperCamelCase_   )
						lowerCAmelCase__										=				[]
						for i in range(len(UpperCamelCase_   )   ):
									for j in range(i + 1    ,						len(UpperCamelCase_   )   ):
												lowerCAmelCase__										=				compare_string(binary[i]    ,						binary[j]   )
												if k is False:
															lowerCAmelCase__										=				"*"
															lowerCAmelCase__										=				"*"
															temp.append("X"   )
						for i in range(len(UpperCamelCase_   )   ):
									if checka[i] == "$":
												pi.append(binary[i]   )
						if len(UpperCamelCase_   ) == 0:
									return pi
						lowerCAmelCase__										=				list(set(UpperCamelCase_   )   )







def 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  Sequence[float]   )       ->							list[str]:







			'''simple docstring'''



			lowerCAmelCase__										=				[]
			for minterm in minterms:
						lowerCAmelCase__										=				""
						for _ in range(UpperCamelCase_   ):
									lowerCAmelCase__										=				str(minterm % 2   ) + string
									minterm //= 2
						temp.append(UpperCamelCase_   )
			return temp







def 	A  (   UpperCamelCase_       :  str    ,						UpperCamelCase_       :  str    ,						UpperCamelCase_       :  int   )       ->							bool:







			'''simple docstring'''



			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				list(UpperCamelCase_   )
			lowerCAmelCase__										=				0
			for i in range(len(UpperCamelCase_   )   ):
						if lista[i] != lista[i]:
									count_n += 1
			return count_n == count







def 	A  (   UpperCamelCase_       :  list[list[int]]    ,						UpperCamelCase_       :  list[str]   )       ->							list[str]:







			'''simple docstring'''



			lowerCAmelCase__										=				[]
			lowerCAmelCase__										=				[0] * len(UpperCamelCase_   )
			for i in range(len(chart[0]   )   ):
						lowerCAmelCase__										=				0
						lowerCAmelCase__										=				-1
						for j in range(len(UpperCamelCase_   )   ):
									if chart[j][i] == 1:
												count += 1
												lowerCAmelCase__										=				j
						if count == 1:
									lowerCAmelCase__										=				1
			for i in range(len(UpperCamelCase_   )   ):
						if select[i] == 1:
									for j in range(len(chart[0]   )   ):
												if chart[i][j] == 1:
															for k in range(len(UpperCamelCase_   )   ):
																		lowerCAmelCase__										=				0
									temp.append(prime_implicants[i]   )
			while True:
						lowerCAmelCase__										=				0
						lowerCAmelCase__										=				-1
						lowerCAmelCase__										=				0
						for i in range(len(UpperCamelCase_   )   ):
									lowerCAmelCase__										=				chart[i].count(1   )
									if count_n > max_n:
												lowerCAmelCase__										=				count_n
												lowerCAmelCase__										=				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(UpperCamelCase_   )   ):
															lowerCAmelCase__										=				0







def 	A  (   UpperCamelCase_       :  list[str]    ,						UpperCamelCase_       :  list[str]   )       ->							list[list[int]]:







			'''simple docstring'''



			lowerCAmelCase__										=				[[0 for x in range(len(UpperCamelCase_   )   )] for x in range(len(UpperCamelCase_   )   )]
			for i in range(len(UpperCamelCase_   )   ):
						lowerCAmelCase__										=				prime_implicants[i].count("_"   )
						for j in range(len(UpperCamelCase_   )   ):
									if is_for_table(prime_implicants[i]    ,						binary[j]    ,						UpperCamelCase_   ):
												lowerCAmelCase__										=				1

			return chart







def 	A  (   )       ->							None:







			'''simple docstring'''



			lowerCAmelCase__										=				int(input("Enter the no. of variables\n"   )   )
			lowerCAmelCase__										=				[
			    float(UpperCamelCase_   )
			    for x in input(
			        "Enter the decimal representation of Minterms 'Spaces Separated'\n"   ).split()
			]
			lowerCAmelCase__										=				decimal_to_binary(UpperCamelCase_    ,						UpperCamelCase_   )

			lowerCAmelCase__										=				check(UpperCamelCase_   )
			print("Prime Implicants are:"   )
			print(UpperCamelCase_   )
			lowerCAmelCase__										=				prime_implicant_chart(UpperCamelCase_    ,						UpperCamelCase_   )

			lowerCAmelCase__										=				selection(UpperCamelCase_    ,						UpperCamelCase_   )
			print("Essential Prime Implicants are:"   )
			print(UpperCamelCase_   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()











'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						lowerCamelCase__   (				a							):
						# vision encoder
						if "img_encoder.pos_embed" in name:
												__snake_case						 =   name.replace('img_encoder.pos_embed'    ,	'vision_model.embeddings.position_embeddings'							)
						if "img_encoder.patch_embed.proj" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.proj'    ,	'vision_model.embeddings.patch_embeddings.projection'							)
						if "img_encoder.patch_embed.norm" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.norm'    ,	'vision_model.embeddings.layernorm'							)
						if "img_encoder.layers" in name:
												__snake_case						 =   name.replace('img_encoder.layers'    ,	'vision_model.encoder.stages'							)
						if "blocks" in name and "res" not in name:
												__snake_case						 =   name.replace('blocks'    ,	'layers'							)
						if "attn" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('attn'    ,	'self_attn'							)
						if "proj" in name and "self_attn" in name and "text" not in name:
												__snake_case						 =   name.replace('proj'    ,	'out_proj'							)
						if "pre_assign_attn.attn.proj" in name:
												__snake_case						 =   name.replace('pre_assign_attn.attn.proj'    ,	'pre_assign_attn.attn.out_proj'							)
						if "norm1" in name:
												__snake_case						 =   name.replace('norm1'    ,	'layer_norm1'							)
						if "norm2" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('norm2'    ,	'layer_norm2'							)
						if "img_encoder.norm" in name:
												__snake_case						 =   name.replace('img_encoder.norm'    ,	'vision_model.layernorm'							)
						# text encoder
						if "text_encoder.token_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.token_embedding'    ,	'text_model.embeddings.token_embedding'							)
						if "text_encoder.positional_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.positional_embedding'    ,	'text_model.embeddings.position_embedding.weight'							)
						if "text_encoder.transformer.resblocks." in name:
												__snake_case						 =   name.replace('text_encoder.transformer.resblocks.'    ,	'text_model.encoder.layers.'							)
						if "ln_1" in name:
												__snake_case						 =   name.replace('ln_1'    ,	'layer_norm1'							)
						if "ln_2" in name:
												__snake_case						 =   name.replace('ln_2'    ,	'layer_norm2'							)
						if "c_fc" in name:
												__snake_case						 =   name.replace('c_fc'    ,	'fc1'							)
						if "c_proj" in name:
												__snake_case						 =   name.replace('c_proj'    ,	'fc2'							)
						if "text_encoder" in name:
												__snake_case						 =   name.replace('text_encoder'    ,	'text_model'							)
						if "ln_final" in name:
												__snake_case						 =   name.replace('ln_final'    ,	'final_layer_norm'							)
						# projection layers
						if "img_projector.linear_hidden." in name:
												__snake_case						 =   name.replace('img_projector.linear_hidden.'    ,	'visual_projection.'							)
						if "img_projector.linear_out." in name:
												__snake_case						 =   name.replace('img_projector.linear_out.'    ,	'visual_projection.3.'							)
						if "text_projector.linear_hidden" in name:
												__snake_case						 =   name.replace('text_projector.linear_hidden'    ,	'text_projection'							)
						if "text_projector.linear_out" in name:
												__snake_case						 =   name.replace('text_projector.linear_out'    ,	'text_projection.3'							)

						return name
def 						lowerCamelCase__   (				a    ,	a							):
						for key in orig_state_dict.copy().keys():
												__snake_case						 =   orig_state_dict.pop(a							)

												if "qkv" in key:
																		# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case ,       __snake_case						 =   int(key_split[2]							), int(key_split[4]							)
																		__snake_case						 =   config.vision_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[dim : dim * 2, :]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												elif "in_proj" in key:
																		# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case						 =   int(key_split[3]							)
																		__snake_case						 =   config.text_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[
																								    dim : dim * 2, :
																								]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												else:
																		__snake_case						 =   rename_key(a							)
																		# squeeze if necessary
																		if (
																		    "text_projection.0" in new_name
																		    or "text_projection.3" in new_name
																		    or "visual_projection.0" in new_name
																		    or "visual_projection.3" in new_name
																		):
																								__snake_case						 =   val.squeeze_()
																		else:
																								__snake_case						 =   val

						return orig_state_dict
def 						lowerCamelCase__   (				):
						__snake_case						 =   'http://images.cocodataset.org/val2017/000000039769.jpg'
						__snake_case						 =   Image.open(requests.get(a    ,	stream=a							).raw							)
						return im
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a="groupvit-gcc-yfcc"    ,	a=False							):
						__snake_case						 =   GroupViTConfig()
						__snake_case						 =   GroupViTModel(a							).eval()

						__snake_case						 =   torch.load(a    ,	map_location='cpu'							)['model']
						__snake_case						 =   convert_state_dict(a    ,	a							)
						__snake_case ,       __snake_case						 =   model.load_state_dict(a    ,	strict=a							)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a							) == 0)

						# verify result
						__snake_case						 =   CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'							)
						__snake_case						 =   prepare_img()
						__snake_case						 =   processor(text=['a photo of a cat', 'a photo of a dog']    ,	images=a    ,	padding=a    ,	return_tensors='pt'							)

						with torch.no_grad():
												__snake_case						 =   model(**a							)

						if model_name == "groupvit-gcc-yfcc":
												__snake_case						 =   torch.tensor([[13.3_523, 6.3_629]]							)
						elif model_name == "groupvit-gcc-redcaps":
												__snake_case						 =   torch.tensor([[16.1_873, 8.6_230]]							)
						else:
												raise ValueError(f'Model name {model_name} not supported.'							)
						assert torch.allclose(outputs.logits_per_image    ,	a    ,	atol=1E-3							)

						processor.save_pretrained(a							)
						model.save_pretrained(a							)
						print('Successfully saved processor and model to'    ,	a							)

						if push_to_hub:
												print('Pushing to the hub...'							)
												processor.push_to_hub(a    ,	organization='nielsr'							)
												model.push_to_hub(a    ,	organization='nielsr'							)


if __name__ == "__main__":
	_lowercase  =			argparse.ArgumentParser()
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
	)
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
	parser.add_argument(
	    """--model_name""",
	    default="""groupvit-gccy-fcc""",
	    type=str,
	    help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
	)
	_lowercase  =			parser.parse_args()

	convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

