






"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def   __magic_name__     (				__snake_case    :	Tuple  )			->    Union[str, Any]:
       lowercase   :    List[Any]			=							[
           "encoder.version",
           "decoder.version",
           "model.encoder.version",
           "model.decoder.version",
           "_float_tensor",
           "decoder.output_projection.weight",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case					,			__snake_case  )


def   __magic_name__     (				__snake_case    :	str  )			->    List[Any]:
       lowercase   :    str			=							emb.weight.shape
       lowercase   :    Tuple			=							nn.Linear(__snake_case					,			__snake_case					,			bias=__snake_case  )
       lowercase   :    Any			=							emb.weight.data
       return lin_layer


def   __magic_name__     (				__snake_case    :	Optional[Any]					,			__snake_case    :	Tuple="uclanlp/plbart-base"					,			__snake_case    :	Optional[Any]=False					,			__snake_case    :	Union[str, Any]=False  )			->    List[Any]:
       lowercase   :    List[Any]			=							torch.load(__snake_case					,			map_location="cpu"  )["model"]
       remove_ignore_keys_(__snake_case  )
       lowercase   :    Dict			=							state_dict["encoder.embed_tokens.weight"].shape[0]

       lowercase   :    Optional[Any]			=							PLBartConfig.from_pretrained(__snake_case					,			vocab_size=__snake_case  )

       lowercase   :    Dict			=							state_dict["decoder.embed_tokens.weight"]
       if not classification:
              lowercase   :    Optional[Any]			=							PLBartForConditionalGeneration(__snake_case  )
              model.model.load_state_dict(__snake_case  )
              if finetuned:
                     lowercase   :    List[Any]			=							make_linear_from_emb(model.model.shared  )

       else:
              lowercase   :    int			=							{}
              for key, value in state_dict.copy().items():
                     if key.startswith("classification_heads.sentence_classification_head"  ):
                            lowercase   :    Dict			=							value
                            state_dict.pop(__snake_case  )
              lowercase   :    List[str]			=							PLBartForSequenceClassification(__snake_case  )
              model.model.load_state_dict(__snake_case  )
              model.classification_head.load_state_dict(__snake_case  )

       return model


if __name__ == "__main__":
   _A      :	Optional[Any]       		=	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"""
   )
   _A      :	Optional[int]       		=	parser.parse_args()
   _A      :	Any       		=	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)




"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 		a__						(				unittest.TestCase						):
   @property
   def       __magic_name__      (      self    ):
          torch.manual_seed(0    )
          lowercase   :    int			=							UNetaDModel(
              block_out_channels=(32, 64)     ,   layers_per_block=2     ,   sample_size=32     ,   in_channels=3     ,   out_channels=3     ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,   )
          return model
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							"cpu"
          lowercase   :    Dict			=							self.dummy_uncond_unet
          lowercase   :    List[str]			=							DDPMScheduler()

          lowercase   :    Tuple			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Optional[int]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    Optional[int]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"     ,   return_dict=_a    )[0]

          lowercase   :    List[str]			=							image[0, -3:, -3:, -1]
          lowercase   :    Any			=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    List[str]			=							np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04]    )

          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2

   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							self.dummy_uncond_unet
          lowercase   :    Dict			=							DDPMScheduler(prediction_type="sample"    )

          lowercase   :    List[Any]			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Any			=							torch.manual_seed(0    )
          lowercase   :    int			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.manual_seed(0    )
          lowercase   :    Optional[Any]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    )[0]

          lowercase   :    Any			=							image[0, -3:, -3:, -1]
          lowercase   :    Dict			=							image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Tuple			=							1E-2 if torch_device != "mps" else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance


@slow
@require_torch_gpu
class 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							"google/ddpm-cifar10-32"

          lowercase   :    Dict			=							UNetaDModel.from_pretrained(_a    )
          lowercase   :    str			=							DDPMScheduler.from_pretrained(_a    )

          lowercase   :    int			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Dict			=							torch.manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   output_type="numpy"    ).images

          lowercase   :    int			=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Optional[Any]			=							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]    )
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


