


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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

		return name
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__            =      orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

				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
						lowercase__            =      key.split('''.'''     )
						lowercase__     ,		lowercase__            =      int(key_split[2]     ), int(key_split[4]     )
						lowercase__            =      config.vision_config.hidden_size
						if "weight" in key:
								lowercase__            =      val[:dim, :]
								lowercase__            =      val[dim : dim * 2, :]
								lowercase__            =      val[-dim:, :]
						else:
								lowercase__            =      val[:dim]
								lowercase__            =      val[dim : dim * 2]
								lowercase__            =      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
						lowercase__            =      key.split('''.'''     )
						lowercase__            =      int(key_split[3]     )
						lowercase__            =      config.text_config.hidden_size
						if "weight" in key:
								lowercase__            =      val[:dim, :]
								lowercase__            =      val[
								    dim : dim * 2, :
								]
								lowercase__            =      val[-dim:, :]
						else:
								lowercase__            =      val[:dim]
								lowercase__            =      val[dim : dim * 2]
								lowercase__            =      val[-dim:]
				else:
						lowercase__            =      rename_key(SCREAMING_SNAKE_CASE     )
						# 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
						):
								lowercase__            =      val.squeeze_()
						else:
								lowercase__            =      val

		return orig_state_dict
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__            =      Image.open(requests.get(SCREAMING_SNAKE_CASE						,				stream=SCREAMING_SNAKE_CASE     ).raw     )
		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc"						,				SCREAMING_SNAKE_CASE=False     ):


		"""simple docstring"""




		lowercase__            =      GroupViTConfig()
		lowercase__            =      GroupViTModel(SCREAMING_SNAKE_CASE     ).eval()

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

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

		with torch.no_grad():
				lowercase__            =      model(**SCREAMING_SNAKE_CASE     )

		if model_name == "groupvit-gcc-yfcc":
				lowercase__            =      torch.tensor([[13.3_523, 6.3_629]]     )
		elif model_name == "groupvit-gcc-redcaps":
				lowercase__            =      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						,				SCREAMING_SNAKE_CASE						,				atol=1E-3     )

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

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


if __name__ == "__main__":
			lowerCAmelCase       =							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`.',
			)
			lowerCAmelCase       =							parser.parse_args()

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






def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      credit_card_number
		lowercase__            =      0
		lowercase__            =      len(SCREAMING_SNAKE_CASE     ) - 2
		for i in range(SCREAMING_SNAKE_CASE						,				-1						,				-2     ):
				#  double the value of every second digit
				lowercase__            =      int(cc_number[i]     )
				digit *= 2
				# If doubling of a number results in a two digit number
				# i.e greater than 9(e.g., 6 × 2 = 12),
				# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
				# to get a single digit number.
				if digit > 9:
						digit %= 10
						digit += 1
				lowercase__            =      cc_number[:i] + str(SCREAMING_SNAKE_CASE     ) + cc_number[i + 1 :]
				total += digit

		# Sum up the remaining digits
		for i in range(len(SCREAMING_SNAKE_CASE     ) - 1						,				-1						,				-2     ):
				total += int(cc_number[i]     )

		return total % 10 == 0
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      f'{credit_card_number} is an invalid credit card number because'
		if not credit_card_number.isdigit():
				print(f'{error_message} it has nonnumerical characters.'     )
				return False

		if not 13 <= len(SCREAMING_SNAKE_CASE     ) <= 16:
				print(f'{error_message} of its length.'     )
				return False

		if not validate_initial_digits(SCREAMING_SNAKE_CASE     ):
				print(f'{error_message} of its first two digits.'     )
				return False

		if not luhn_validation(SCREAMING_SNAKE_CASE     ):
				print(f'{error_message} it fails the Luhn check.'     )
				return False

		print(f'{credit_card_number} is a valid credit card number.'     )
		return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			validate_credit_card_number('4111111111111111')
			validate_credit_card_number('32323')


