






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 						lowerCAmelCase_		(lowercase__    :							Optional[int]							)		->     Dict:


				'''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 						lowerCAmelCase_		(lowercase__    :							Union[str, Any]  ,       lowercase__    :							Dict							)		->     Any:


				'''simple docstring'''




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

								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(lowerCAmelCase__							)
												# 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 						lowerCAmelCase_		()		->     str:


				'''simple docstring'''




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





@torch.no_grad()
def 						lowerCAmelCase_		(lowercase__    :							Union[str, Any]  ,       lowercase__    :							Any  ,       lowercase__    :							Any="groupvit-gcc-yfcc"  ,       lowercase__    :							Optional[Any]=False							)		->     str:


				'''simple docstring'''




				lowerCAmelCase__         =    GroupViTConfig()
				lowerCAmelCase__         =    GroupViTModel(lowerCAmelCase__							).eval()

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

				with torch.no_grad():
								lowerCAmelCase__         =    model(**lowerCAmelCase__							)

				if model_name == "groupvit-gcc-yfcc":
								lowerCAmelCase__         =    torch.tensor([[13.3523, 6.3629]]							)
				elif model_name == "groupvit-gcc-redcaps":
								lowerCAmelCase__         =    torch.tensor([[16.1873, 8.6230]]							)
				else:
								raise ValueError(f'Model name {model_name} not supported.'							)
				assert torch.allclose(outputs.logits_per_image  ,       lowerCAmelCase__  ,       atol=1e-3							)

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

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


if __name__ == "__main__":
			_UpperCAmelCase      :		Dict 					=     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`.",
			)
			_UpperCAmelCase      :		Union[str, Any] 					=     parser.parse_args()

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







"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    (DDPMScheduler,)




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCAmelCase   )
							return config




				def        UpperCAmelCase__						(				self   ):
							for timesteps in [1, 5, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,						[0.0_0_2, 0.0_2, 0.2, 2]   ):
										self.check_over_configs(beta_start=UpperCAmelCase							,						beta_end=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							self.check_over_configs(thresholding=UpperCAmelCase   )
							for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=UpperCAmelCase							,						prediction_type=UpperCAmelCase							,						sample_max_value=UpperCAmelCase							,						)




				def        UpperCAmelCase__						(				self   ):
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for t in [0, 500, 999]:
										self.check_over_forward(time_step=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0_9_7_9   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.0_2   )   ) < 1e-5




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter
							lowerCamelCase_					=  torch.manual_seed(0   )

							for t in reversed(range(UpperCAmelCase   )   ):
										# 1. predict noise residual
										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										# 2. predict previous mean of sample x_t-1
										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						generator=UpperCAmelCase   ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
							assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config(prediction_type='''v_prediction'''   )
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter
							lowerCamelCase_					=  torch.manual_seed(0   )

							for t in reversed(range(UpperCAmelCase   )   ):
										# 1. predict noise residual
										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										# 2. predict previous mean of sample x_t-1
										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						generator=UpperCAmelCase   ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
							assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=UpperCAmelCase   )

							lowerCamelCase_					=  scheduler.timesteps

							for i, timestep in enumerate(UpperCAmelCase   ):
										if i == len(UpperCAmelCase   ) - 1:
													lowerCamelCase_					=  -1
										else:
													lowerCamelCase_					=  timesteps[i + 1]

										lowerCamelCase_					=  scheduler.previous_timestep(UpperCAmelCase   )
										lowerCamelCase_					=  prev_t.item()

										self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 51, 0]

							with self.assertRaises(UpperCAmelCase							,						msg='''`custom_timesteps` must be in descending order.'''   ):
										scheduler.set_timesteps(timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]
							lowerCamelCase_					=  len(UpperCAmelCase   )

							with self.assertRaises(UpperCAmelCase							,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
										scheduler.set_timesteps(num_inference_steps=UpperCAmelCase							,						timesteps=UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    UpperCAmelCase							,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,						):
										scheduler.set_timesteps(timesteps=UpperCAmelCase   )



