






'''simple docstring'''







import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[int]:
							return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> int:
							lowerCamelCase__      :			Optional[Any] 						= {}

							for key, value in state_dict.items():
														if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
																					continue

														lowerCamelCase__      :			Optional[Any] 						= key.replace("""heads.cmd.mim_head.cls.predictions"""       ,    """mmm_image_head"""   )
														lowerCamelCase__      :			List[Any] 						= key.replace("""heads.cmd.mlm_head.cls.predictions"""       ,    """mmm_text_head"""   )
														lowerCamelCase__      :			Optional[Any] 						= key.replace("""heads.cmd.itm_head.cls"""       ,    """itm_head"""   )
														lowerCamelCase__      :			Optional[Any] 						= key.replace("""heads.cmd.itm_head.pooler"""       ,    """itm_head.pooler"""   )
														lowerCamelCase__      :			Dict 						= key.replace("""heads.cmd.clip_head.logit_scale"""       ,    """flava.logit_scale"""   )
														lowerCamelCase__      :			List[Any] 						= key.replace("""heads.fairseq_mlm.cls.predictions"""       ,    """mlm_head"""   )
														lowerCamelCase__      :			Union[str, Any] 						= key.replace("""heads.imagenet.mim_head.cls.predictions"""       ,    """mim_head"""   )
														lowerCamelCase__      :			Tuple 						= key.replace("""mm_text_projection"""       ,    """flava.text_to_mm_projection"""   )
														lowerCamelCase__      :			Optional[int] 						= key.replace("""mm_image_projection"""       ,    """flava.image_to_mm_projection"""   )
														lowerCamelCase__      :			str 						= key.replace("""image_encoder.module"""       ,    """flava.image_model"""   )
														lowerCamelCase__      :			Optional[Any] 						= key.replace("""text_encoder.module"""       ,    """flava.text_model"""   )
														lowerCamelCase__      :			List[Any] 						= key.replace("""mm_encoder.module.encoder.cls_token"""       ,    """flava.multimodal_model.cls_token"""   )
														lowerCamelCase__      :			List[str] 						= key.replace("""mm_encoder.module"""       ,    """flava.multimodal_model"""   )
														lowerCamelCase__      :			Any 						= key.replace("""text_projection"""       ,    """flava.text_projection"""   )
														lowerCamelCase__      :			Tuple 						= key.replace("""image_projection"""       ,    """flava.image_projection"""   )

														lowerCamelCase__      :			Tuple 						= value.float()

							for key, value in codebook_state_dict.items():
														lowerCamelCase__      :			Union[str, Any] 						= value

							return upgrade





@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None   )   -> Union[str, Any]:





							if config_path is not None:
														lowerCamelCase__      :			Dict 						= FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE__   )
							else:
														lowerCamelCase__      :			Union[str, Any] 						= FlavaConfig()

							lowerCamelCase__      :			Optional[Any] 						= FlavaForPreTraining(SCREAMING_SNAKE_CASE__   ).eval()

							lowerCamelCase__      :			str 						= convert_dalle_checkpoint(SCREAMING_SNAKE_CASE__       ,    SCREAMING_SNAKE_CASE__       ,    save_checkpoint=SCREAMING_SNAKE_CASE__   )

							if os.path.exists(SCREAMING_SNAKE_CASE__   ):
														lowerCamelCase__      :			str 						= torch.load(SCREAMING_SNAKE_CASE__       ,    map_location="""cpu"""   )
							else:
														lowerCamelCase__      :			str 						= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__       ,    map_location="""cpu"""   )

							lowerCamelCase__      :			Tuple 						= upgrade_state_dict(SCREAMING_SNAKE_CASE__       ,    SCREAMING_SNAKE_CASE__   )
							hf_model.load_state_dict(SCREAMING_SNAKE_CASE__   )
							lowerCamelCase__      :			Any 						= hf_model.state_dict()
							lowerCamelCase__      :			Any 						= count_parameters(SCREAMING_SNAKE_CASE__   )
							lowerCamelCase__      :			int 						= count_parameters(SCREAMING_SNAKE_CASE__   ) + count_parameters(SCREAMING_SNAKE_CASE__   )

							assert torch.allclose(SCREAMING_SNAKE_CASE__       ,    SCREAMING_SNAKE_CASE__       ,    atol=1E-3   )

							hf_model.save_pretrained(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
			_A			:    Optional[Any]					  =argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
			parser.add_argument('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			_A			:    List[str]					  =parser.parse_args()

			convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

