




"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







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

				return name







def      _snake_case			(				lowercase__							,    lowercase__		):
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase     :			Union[str, Any]    =    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
												_lowerCamelCase     :			Union[str, Any]    =    key.split('.'		)
												_lowerCamelCase, _lowerCamelCase     :			Union[str, Any]    =    int(key_split[2]		), int(key_split[4]		)
												_lowerCamelCase     :			List[str]    =    config.vision_config.hidden_size
												if "weight" in key:
																_lowerCamelCase     :			Optional[Any]    =    val[:dim, :]
																_lowerCamelCase     :			int    =    val[dim : dim * 2, :]
																_lowerCamelCase     :			List[str]    =    val[-dim:, :]
												else:
																_lowerCamelCase     :			List[str]    =    val[:dim]
																_lowerCamelCase     :			List[Any]    =    val[dim : dim * 2]
																_lowerCamelCase     :			str    =    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
												_lowerCamelCase     :			List[str]    =    key.split('.'		)
												_lowerCamelCase     :			Optional[Any]    =    int(key_split[3]		)
												_lowerCamelCase     :			Dict    =    config.text_config.hidden_size
												if "weight" in key:
																_lowerCamelCase     :			Dict    =    val[:dim, :]
																_lowerCamelCase     :			List[Any]    =    val[
																    dim : dim * 2, :
																]
																_lowerCamelCase     :			List[str]    =    val[-dim:, :]
												else:
																_lowerCamelCase     :			int    =    val[:dim]
																_lowerCamelCase     :			Any    =    val[dim : dim * 2]
																_lowerCamelCase     :			Dict    =    val[-dim:]
								else:
												_lowerCamelCase     :			Dict    =    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
												):
																_lowerCamelCase     :			Dict    =    val.squeeze_()
												else:
																_lowerCamelCase     :			Tuple    =    val

				return orig_state_dict







def      _snake_case			(				):
				_lowerCamelCase     :			str    =    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase     :			Dict    =    Image.open(requests.get(__lowercase							,    stream=__lowercase		).raw		)
				return im







@torch.no_grad()
def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__="groupvit-gcc-yfcc"							,    lowercase__=False		):
				_lowerCamelCase     :			Optional[Any]    =    GroupViTConfig()
				_lowerCamelCase     :			int    =    GroupViTModel(__lowercase		).eval()

				_lowerCamelCase     :			Any    =    torch.load(__lowercase							,    map_location='cpu'		)['model']
				_lowerCamelCase     :			Optional[int]    =    convert_state_dict(__lowercase							,    __lowercase		)
				_lowerCamelCase, _lowerCamelCase     :			int    =    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
				_lowerCamelCase     :			Optional[Any]    =    CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'		)
				_lowerCamelCase     :			Optional[Any]    =    prepare_img()
				_lowerCamelCase     :			Tuple    =    processor(text=['a photo of a cat', 'a photo of a dog']							,    images=__lowercase							,    padding=__lowercase							,    return_tensors='pt'		)

				with torch.no_grad():
								_lowerCamelCase     :			Any    =    model(**__lowercase		)

				if model_name == "groupvit-gcc-yfcc":
								_lowerCamelCase     :			int    =    torch.tensor([[13.3523, 6.3_6_2_9]]		)
				elif model_name == "groupvit-gcc-redcaps":
								_lowerCamelCase     :			List[str]    =    torch.tensor([[16.1873, 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__":
			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)

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()