'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






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

						return name



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												_lowerCAmelCase										=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
																		_lowerCAmelCase										=key.split('.'      )
																		_lowerCAmelCase						,				_lowerCAmelCase										=int(key_split[2]      ), int(key_split[4]      )
																		_lowerCAmelCase										=config.vision_config.hidden_size
																		if "weight" in key:
																								_lowerCAmelCase										=val[:dim, :]
																								_lowerCAmelCase										=val[dim : dim * 2, :]
																								_lowerCAmelCase										=val[-dim:, :]
																		else:
																								_lowerCAmelCase										=val[:dim]
																								_lowerCAmelCase										=val[dim : dim * 2]
																								_lowerCAmelCase										=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										=key.split('.'      )
																		_lowerCAmelCase										=int(key_split[3]      )
																		_lowerCAmelCase										=config.text_config.hidden_size
																		if "weight" in key:
																								_lowerCAmelCase										=val[:dim, :]
																								_lowerCAmelCase										=val[
																								    dim : dim * 2, :
																								]
																								_lowerCAmelCase										=val[-dim:, :]
																		else:
																								_lowerCAmelCase										=val[:dim]
																								_lowerCAmelCase										=val[dim : dim * 2]
																								_lowerCAmelCase										=val[-dim:]
												else:
																		_lowerCAmelCase										=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
																		):
																								_lowerCAmelCase										=val.squeeze_()
																		else:
																								_lowerCAmelCase										=val

						return orig_state_dict



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										='http://images.cocodataset.org/val2017/000000039769.jpg'
						_lowerCAmelCase										=Image.open(requests.get(a__			,      stream=a__      ).raw      )
						return im



@torch.no_grad()
def 							UpperCamelCase__			(			a__			,      a__			,      a__="groupvit-gcc-yfcc"			,      a__=False      ):


						'''simple docstring'''






						_lowerCAmelCase										=GroupViTConfig()
						_lowerCAmelCase										=GroupViTModel(a__      ).eval()

						_lowerCAmelCase										=torch.load(a__			,      map_location='cpu'      )['model']
						_lowerCAmelCase										=convert_state_dict(a__			,      a__      )
						_lowerCAmelCase						,				_lowerCAmelCase										=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
						_lowerCAmelCase										=CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'      )
						_lowerCAmelCase										=prepare_img()
						_lowerCAmelCase										=processor(text=['a photo of a cat', 'a photo of a dog']			,      images=a__			,      padding=a__			,      return_tensors='pt'      )

						with torch.no_grad():
												_lowerCAmelCase										=model(**a__      )

						if model_name == "groupvit-gcc-yfcc":
												_lowerCAmelCase										=torch.tensor([[13.3_523, 6.3_629]]      )
						elif model_name == "groupvit-gcc-redcaps":
												_lowerCAmelCase										=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)






'''simple docstring'''

from PIL import Image



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						def brightness(a__      ) -> float:
												return 1_2_8 + level + (c - 1_2_8)

						if not -255.0 <= level <= 255.0:
												raise ValueError('level must be between -255.0 (black) and 255.0 (white)'      )
						return img.point(a__      )


if __name__ == "__main__":
							# Load image
							with Image.open('''image_data/lena.jpg''') as img:
														# Change brightness to 100
														lowercase_     		= change_brightness(img, 100)
														brigt_img.save('''image_data/lena_brightness.png''', format='''png''')





