






'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class        a_		(    lowerCamelCase       ):
  lowercase  	=   (CMStochasticIterativeScheduler,)
  lowercase  	=   10

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      {
            """num_train_timesteps""": 201,
            """sigma_min""": 0.0_0_2,
            """sigma_max""": 8_0.0,
        }

        config.update(**_SCREAMING_SNAKE_CASE   )
        return config

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      10

        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE   )

        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      scheduler.timesteps[0]
        UpperCamelCase       						=      scheduler.timesteps[1]

        UpperCamelCase       						=      self.dummy_sample
        UpperCamelCase       						=      0.1 * sample

        UpperCamelCase       						=      scheduler.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ).prev_sample
        UpperCamelCase       						=      scheduler.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ).prev_sample

        self.assertEqual(output_a.shape    ,	sample.shape   )
        self.assertEqual(output_a.shape    ,	output_a.shape   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        for clip_denoised in [True, False]:
              self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      1
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      scheduler.timesteps

        UpperCamelCase       						=      torch.manual_seed(0   )

        UpperCamelCase       						=      self.dummy_model()
        UpperCamelCase       						=      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(_SCREAMING_SNAKE_CASE   ):
              # 1. scale model input
              UpperCamelCase       						=      scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # 2. predict noise residual
              UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # 3. predict previous sample x_t-1
              UpperCamelCase       						=      scheduler.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE   ).prev_sample

              UpperCamelCase       						=      pred_prev_sample

        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 1_9_2.7_6_1_4   ) < 1e-2
        assert abs(result_mean.item() - 0.2_5_1_0   ) < 1e-3

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      [106, 0]
        scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      scheduler.timesteps

        UpperCamelCase       						=      torch.manual_seed(0   )

        UpperCamelCase       						=      self.dummy_model()
        UpperCamelCase       						=      self.dummy_sample_deter * scheduler.init_noise_sigma

        for t in timesteps:
              # 1. scale model input
              UpperCamelCase       						=      scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # 2. predict noise residual
              UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # 3. predict previous sample x_t-1
              UpperCamelCase       						=      scheduler.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE   ).prev_sample

              UpperCamelCase       						=      pred_prev_sample

        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 3_4_7.6_3_5_7   ) < 1e-2
        assert abs(result_mean.item() - 0.4_5_2_7   ) < 1e-3

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      [39, 30, 12, 15, 0]

        with self.assertRaises(_SCREAMING_SNAKE_CASE    ,	msg="""`timesteps` must be in descending order."""   ):
              scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      [39, 30, 12, 1, 0]
        UpperCamelCase       						=      len(_SCREAMING_SNAKE_CASE   )

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


  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      [scheduler.config.num_train_timesteps]

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








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		max_position_embeddings=1024	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		encoder_layerdrop=args.encoder_layerdrop	,		decoder_layerdrop=args.decoder_layerdrop	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function="""relu"""	,		)

      UpperCamelCase       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
