from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class     snake_case_			(						lowerCAmelCase_			):






   def __init__( self							:	Optional[int]      ,		lowercase_							:	AutoencoderKL      ,		lowercase_							:	CLIPTextModel      ,		lowercase_							:	CLIPTokenizer      ,		lowercase_							:	UNetaDConditionModel      ,		lowercase_							:	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]      ,		)   ->		Any:
       super().__init__()

       self.register_modules(vae=UpperCamelCase_      ,		text_encoder=UpperCamelCase_      ,		tokenizer=UpperCamelCase_      ,		unet=UpperCamelCase_      ,		scheduler=UpperCamelCase_    )






   def  __UpperCamelCase	( self							:	List[Any]      ,		lowercase_							:	Optional[int]    )   ->		int:
       with torch.no_grad():
           lowercase__					:  Dict          =							self.vae.encode(tfms.ToTensor()(UpperCamelCase_    ).unsqueeze(0    ).to(self.device    ) * 2 - 1    )
           lowercase__					:  str          =							0.1_82_15 * latent.latent_dist.sample()
       return latent






   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	Union[str, Any]    )   ->		Dict:
       lowercase__					:  Dict          =							(1 / 0.1_82_15) * latent
       with torch.no_grad():
           lowercase__					:  str          =							self.vae.decode(UpperCamelCase_    ).sample
       lowercase__					:  List[Any]          =							(img / 2 + 0.5).clamp(0      ,		1    )
       lowercase__					:  str          =							img.detach().cpu().permute(0      ,		2      ,		3      ,		1    ).numpy()
       lowercase__					:  Dict          =							(img * 2_55).round().astype("uint8"    )
       return Image.fromarray(img[0]    )






   def  __UpperCamelCase	( self							:	Union[str, Any]      ,		lowercase_							:	Union[str, Any]    )   ->		Any:
       lowercase__					:  List[Any]          =							self.tokenizer(
           UpperCamelCase_      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=UpperCamelCase_      ,		return_tensors="pt"      ,		)

       lowercase__					:  int          =							self.text_encoder(text_input.input_ids.to(self.device    )    )[0]

       lowercase__					:  Tuple          =							self.tokenizer(
           ""      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=UpperCamelCase_      ,		return_tensors="pt"      ,		)

       lowercase__					:  Optional[Any]          =							self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

       return torch.cat([uncond_embedding, text_embedding]    )







   def __call__( self							:	Union[str, Any]      ,		lowercase_							:	Image.Image      ,		lowercase_							:	str      ,		lowercase_							:	float = 0.3      ,		lowercase_							:	float = 0.6      ,		lowercase_							:	float = 0.5      ,		lowercase_							:	int = 42      ,		lowercase_							:	int = 50      ,		lowercase_							:	float = 7.5      ,		)   ->		Optional[int]:
       lowercase__					:  Dict          =							steps - int(kmin * steps    )
       lowercase__					:  int          =							steps - int(kmax * steps    )

       lowercase__					:  List[Any]          =							self.prep_text(UpperCamelCase_    )

       self.scheduler.set_timesteps(UpperCamelCase_    )

       lowercase__					:  Union[str, Any]          =							img.size
       lowercase__					:  Optional[Any]          =							self.encode(UpperCamelCase_    )

       torch.manual_seed(UpperCamelCase_    )
       lowercase__					:  List[Any]          =							torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)      ,		).to(self.device    )

       lowercase__					:  Any          =							self.scheduler.add_noise(
           UpperCamelCase_      ,		UpperCamelCase_      ,		timesteps=self.scheduler.timesteps[tmax]      ,		)

       lowercase__					:  str          =							torch.cat([latents] * 2    )

       lowercase__					:  Dict          =							self.scheduler.scale_model_input(UpperCamelCase_      ,		self.scheduler.timesteps[tmax]    )

       with torch.no_grad():
           lowercase__					:  int          =							self.unet(
               UpperCamelCase_      ,		self.scheduler.timesteps[tmax]      ,		encoder_hidden_states=UpperCamelCase_      ,		).sample

       lowercase__					:  int          =							pred.chunk(2    )
       lowercase__					:  List[Any]          =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

       lowercase__					:  Optional[Any]          =							self.scheduler.step(UpperCamelCase_      ,		self.scheduler.timesteps[tmax]      ,		UpperCamelCase_    ).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps    )    ):
           if i > tmax:
               if i < tmin:  # layout generation phase
                   lowercase__					:  List[Any]          =							self.scheduler.add_noise(
                       UpperCamelCase_      ,		UpperCamelCase_      ,		timesteps=UpperCamelCase_      ,		)

                   lowercase__					:  Dict          =							(mix_factor * latents) + (
                       1 - mix_factor
                   ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                   lowercase__					:  int          =							torch.cat([input] * 2    )

               else:  # content generation phase
                   lowercase__					:  Optional[Any]          =							torch.cat([latents] * 2    )

               lowercase__					:  Dict          =							self.scheduler.scale_model_input(UpperCamelCase_      ,		UpperCamelCase_    )

               with torch.no_grad():
                   lowercase__					:  Dict          =							self.unet(
                       UpperCamelCase_      ,		UpperCamelCase_      ,		encoder_hidden_states=UpperCamelCase_      ,		).sample

               lowercase__					:  int          =							pred.chunk(2    )
               lowercase__					:  Union[str, Any]          =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

               lowercase__					:  Tuple          =							self.scheduler.step(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_    ).prev_sample

       return self.decode(UpperCamelCase_    )





'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "model" in orig_key:
				lowerCAmelCase_   :       Union[str, Any]    							=orig_key.replace('''model.'''  ,					''''''  )
		if "norm1" in orig_key:
				lowerCAmelCase_   :       List[Any]    							=orig_key.replace('''norm1'''  ,					'''attention.output.LayerNorm'''  )
		if "norm2" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm2'''  ,					'''output.LayerNorm'''  )
		if "norm" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm'''  ,					'''LayerNorm'''  )
		if "transformer" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.split('''.'''  )[0].split('''_'''  )[-1]
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace(f'transformer_{layer_num}'  ,					f'encoder.layer.{layer_num}'  )
		if "mha.attn" in orig_key:
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace('''mha.attn'''  ,					'''attention.self'''  )
		if "mha" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mha'''  ,					'''attention'''  )
		if "W_q" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''W_q'''  ,					'''self.query'''  )
		if "W_k" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''W_k'''  ,					'''self.key'''  )
		if "W_v" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''W_v'''  ,					'''self.value'''  )
		if "ff.0" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''ff.0'''  ,					'''intermediate.dense'''  )
		if "ff.2" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.replace('''ff.2'''  ,					'''output.dense'''  )
		if "ff" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''ff'''  ,					'''output.dense'''  )
		if "mlm_class" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mlm.mlm_class'''  ,					'''cls.predictions.decoder'''  )
		if "mlm" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''mlm'''  ,					'''cls.predictions.transform'''  )
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''backbone.backbone.encoders'''  ,					'''encoder.layer'''  )
		if "cls" not in orig_key:
				lowerCAmelCase_   :       List[Any]    							='''mra.''' + orig_key

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowerCAmelCase_   :       Dict    							=val

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

		print(model.load_state_dict(_SCREAMING_SNAKE_CASE  )  )
		model.eval()
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )

		print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )


if __name__ == "__main__":
							__lowercase	=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The json file for Mra model config.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							__lowercase	=    parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






