



'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






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

						return name






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Tuple												=  orig_state_dict.pop(UpperCAmelCase_				)

												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
																		_UpperCamelCase   :						Dict												=  key.split('.'				)
																		_UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  int(key_split[2]				), int(key_split[4]				)
																		_UpperCamelCase   :						int												=  config.vision_config.hidden_size
																		if "weight" in key:
																								_UpperCamelCase   :						Tuple												=  val[:dim, :]
																								_UpperCamelCase   :						int												=  val[dim : dim * 2, :]
																								_UpperCamelCase   :						Union[str, Any]												=  val[-dim:, :]
																		else:
																								_UpperCamelCase   :						List[Any]												=  val[:dim]
																								_UpperCamelCase   :						Optional[Any]												=  val[dim : dim * 2]
																								_UpperCamelCase   :						Tuple												=  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
																		_UpperCamelCase   :						Dict												=  key.split('.'				)
																		_UpperCamelCase   :						List[str]												=  int(key_split[3]				)
																		_UpperCamelCase   :						Optional[int]												=  config.text_config.hidden_size
																		if "weight" in key:
																								_UpperCamelCase   :						Any												=  val[:dim, :]
																								_UpperCamelCase   :						Union[str, Any]												=  val[
																								    dim : dim * 2, :
																								]
																								_UpperCamelCase   :						Tuple												=  val[-dim:, :]
																		else:
																								_UpperCamelCase   :						Tuple												=  val[:dim]
																								_UpperCamelCase   :						str												=  val[dim : dim * 2]
																								_UpperCamelCase   :						List[str]												=  val[-dim:]
												else:
																		_UpperCamelCase   :						Tuple												=  rename_key(UpperCAmelCase_				)
																		# 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
																		):
																								_UpperCamelCase   :						List[Any]												=  val.squeeze_()
																		else:
																								_UpperCamelCase   :						Optional[int]												=  val

						return orig_state_dict






def A__       (	):
						_UpperCamelCase   :						Dict												=  'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCamelCase   :						Union[str, Any]												=  Image.open(requests.get(UpperCAmelCase_    ,    stream=UpperCAmelCase_				).raw				)
						return im






@torch.no_grad()
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_="groupvit-gcc-yfcc"    ,    UpperCAmelCase_=False				):
						_UpperCamelCase   :						Dict												=  GroupViTConfig()
						_UpperCamelCase   :						Any												=  GroupViTModel(UpperCAmelCase_				).eval()

						_UpperCamelCase   :						str												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model']
						_UpperCamelCase   :						Tuple												=  convert_state_dict(UpperCAmelCase_    ,    UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  model.load_state_dict(UpperCAmelCase_    ,    strict=UpperCAmelCase_				)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase_				) == 0)

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

						with torch.no_grad():
												_UpperCamelCase   :						List[Any]												=  model(**UpperCAmelCase_				)

						if model_name == "groupvit-gcc-yfcc":
												_UpperCamelCase   :						Optional[int]												=  torch.tensor([[13.3_523, 6.3_629]]				)
						elif model_name == "groupvit-gcc-redcaps":
												_UpperCamelCase   :						str												=  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    ,    UpperCAmelCase_    ,    atol=1E-3				)

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

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


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

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







'''simple docstring'''
def A__       (	UpperCAmelCase_ = 6_0_0_8_5_1_4_7_5_1_4_3				):

						try:
												_UpperCamelCase   :						str												=  int(UpperCAmelCase_				)
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.'				)
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.'				)
						_UpperCamelCase   :						int												=  1
						_UpperCamelCase   :						Optional[Any]												=  2
						while i * i <= n:
												while n % i == 0:
																		_UpperCamelCase   :						Union[str, Any]												=  i
																		n //= i
												i += 1
						if n > 1:
												_UpperCamelCase   :						Dict												=  n
						return int(UpperCAmelCase_				)


if __name__ == "__main__":
					print(F"""{solution() = }""")


