




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def 							_A   (						lowerCAmelCase_     :					Union[str, Any]			):
     """simple docstring"""

     lowerCAmelCase__		= [
         "encoder.version",
         "decoder.version",
         "model.encoder.version",
         "model.decoder.version",
         "_float_tensor",
         "decoder.output_projection.weight",
     ]
     for k in ignore_keys:
          state_dict.pop(lowerCAmelCase_   ,				lowerCAmelCase_			)



def 							_A   (						lowerCAmelCase_     :					str			):
     """simple docstring"""

     lowerCAmelCase__	,		lowerCAmelCase__		= emb.weight.shape
     lowerCAmelCase__		= nn.Linear(lowerCAmelCase_   ,				lowerCAmelCase_   ,				bias=lowerCAmelCase_			)
     lowerCAmelCase__		= emb.weight.data
     return lin_layer



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Optional[Any]="uclanlp/plbart-base"   ,				lowerCAmelCase_     :					List[Any]=False   ,				lowerCAmelCase_     :					Optional[int]=False			):
     """simple docstring"""

     lowerCAmelCase__		= torch.load(lowerCAmelCase_   ,				map_location="cpu"			)["model"]
     remove_ignore_keys_(lowerCAmelCase_			)
     lowerCAmelCase__		= state_dict["encoder.embed_tokens.weight"].shape[0]

     lowerCAmelCase__		= PLBartConfig.from_pretrained(lowerCAmelCase_   ,				vocab_size=lowerCAmelCase_			)

     lowerCAmelCase__		= state_dict["decoder.embed_tokens.weight"]
     if not classification:
          lowerCAmelCase__		= PLBartForConditionalGeneration(lowerCAmelCase_			)
          model.model.load_state_dict(lowerCAmelCase_			)
          if finetuned:
               lowerCAmelCase__		= make_linear_from_emb(model.model.shared			)

     else:
          lowerCAmelCase__		= {}
          for key, value in state_dict.copy().items():
               if key.startswith("classification_heads.sentence_classification_head"			):
                    lowerCAmelCase__		= value
                    state_dict.pop(lowerCAmelCase_			)
          lowerCAmelCase__		= PLBartForSequenceClassification(lowerCAmelCase_			)
          model.model.load_state_dict(lowerCAmelCase_			)
          model.classification_head.load_state_dict(lowerCAmelCase_			)

     return model


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     UpperCamelCase								=     parser.parse_args()
     UpperCamelCase								=     convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

         lowerCAmelCase__		= (image / 2 + 0.5).clamp(0   ,	1   )
         lowerCAmelCase__		= image.cpu().permute(0   ,	2   ,	3   ,	1   ).numpy()
         if output_type == "pil":
              lowerCAmelCase__		= self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

         if not return_dict:
              return (image,), "This is a local test"

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   ), "This is a local test"
