
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def        a_		(       __lowercase   :	str ) ->  str:
			# 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        a_		(       __lowercase   :	List[Any]       ,					__lowercase   :	Optional[Any] ) ->  Tuple:
			for key in orig_state_dict.copy().keys():
						_snake_case		=			orig_state_dict.pop(__lowercase )

						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(__lowercase )
									# 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        a_		(       ) ->  str:
			_snake_case		=			'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case		=			Image.open(requests.get(__lowercase       ,					stream=__lowercase ).raw )
			return im






@torch.no_grad()
def        a_		(       __lowercase   :	List[str]       ,					__lowercase   :	List[str]       ,					__lowercase   :	List[str]="groupvit-gcc-yfcc"       ,					__lowercase   :	Union[str, Any]=False ) ->  List[str]:
			_snake_case		=			GroupViTConfig()
			_snake_case		=			GroupViTModel(__lowercase ).eval()

			_snake_case		=			torch.load(__lowercase       ,					map_location='cpu' )['model']
			_snake_case		=			convert_state_dict(__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case		=			model.load_state_dict(__lowercase       ,					strict=__lowercase )
			assert missing_keys == ["text_model.embeddings.position_ids"]
			assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowercase ) == 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=__lowercase       ,					padding=__lowercase       ,					return_tensors='pt' )

			with torch.no_grad():
						_snake_case		=			model(**__lowercase )

			if model_name == "groupvit-gcc-yfcc":
						_snake_case		=			torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]] )
			elif model_name == "groupvit-gcc-redcaps":
						_snake_case		=			torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]] )
			else:
						raise ValueError(f'''Model name {model_name} not supported.''' )
			assert torch.allclose(outputs.logits_per_image       ,					__lowercase       ,					atol=1E-3 )

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

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


if __name__ == "__main__":
			_lowerCamelCase       :				Optional[Any]       =			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       :				Tuple       =			parser.parse_args()

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

class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    List[str]			,  lowercase	:    list[int]	):


				'''simple docstring'''


				_snake_case		=			len(lowercase	)
				_snake_case		=			[0] * len_array

				if len_array > 0:
							_snake_case		=			array[0]

				for i in range(1			,  lowercase	):
							_snake_case		=			self.prefix_sum[i - 1] + array[i]



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if start == 0:
							return self.prefix_sum[end]

				return self.prefix_sum[end] - self.prefix_sum[start - 1]





	def  A							(  self	:    Union[str, Any]			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			{0}
				for sum_item in self.prefix_sum:
							if sum_item - target_sum in sums:
										return True

							sums.add(lowercase	)

				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()