





"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Optional[Any]      					=(DDIMParallelScheduler,)
							lowerCamelCase   :					Optional[int]      					=(("""eta""", 0.0), ("""num_inference_steps""", 50))






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> Optional[int]:
								a      :		Any	=				{
								    "num_train_timesteps": 1000,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "clip_sample": True,
								}

								config.update(**lowerCAmelCase__      )
								return config






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> List[Any]:
								a      :		List[Any]	=				self.scheduler_classes[0]
								a      :		Any	=				self.get_scheduler_config(**lowerCAmelCase__      )
								a      :		Optional[int]	=				scheduler_class(**lowerCAmelCase__      )

								a,       a      :		Dict	=				10, 0.0

								a      :		Optional[int]	=				self.dummy_model()
								a      :		Dict	=				self.dummy_sample_deter

								scheduler.set_timesteps(lowerCAmelCase__      )

								for t in scheduler.timesteps:
									a      :		List[Any]	=				model(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		int	=				scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								return sample






							def     __a   (   self      )	-> List[str]:
								for timesteps in [100, 500, 1000]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase__      )






							def     __a   (   self      )	-> Optional[Any]:
								for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=lowerCAmelCase__      )

								a      :		Dict	=				self.scheduler_classes[0]
								a      :		str	=				self.get_scheduler_config(steps_offset=1      )
								a      :		Any	=				scheduler_class(**lowerCAmelCase__      )
								scheduler.set_timesteps(5      )
								assert torch.equal(scheduler.timesteps      ,					torch.LongTensor([801, 601, 401, 201, 1]      )      )






							def     __a   (   self      )	-> Optional[Any]:
								for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      ,					[0.002, 0.02, 0.2, 2]      ):
									self.check_over_configs(beta_start=lowerCAmelCase__      ,					beta_end=lowerCAmelCase__      )






							def     __a   (   self      )	-> int:
								for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=lowerCAmelCase__      )






							def     __a   (   self      )	-> Any:
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCAmelCase__      )






							def     __a   (   self      )	-> Any:
								for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=lowerCAmelCase__      )






							def     __a   (   self      )	-> Optional[Any]:
								for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=lowerCAmelCase__      )






							def     __a   (   self      )	-> str:
								for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__      )






							def     __a   (   self      )	-> str:
								self.check_over_configs(thresholding=lowerCAmelCase__      )
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(
										    thresholding=lowerCAmelCase__      ,					prediction_type=lowerCAmelCase__      ,					sample_max_value=lowerCAmelCase__      ,					)






							def     __a   (   self      )	-> List[str]:
								for t in [1, 10, 49]:
									self.check_over_forward(time_step=lowerCAmelCase__      )






							def     __a   (   self      )	-> Optional[Any]:
								for t, num_inference_steps in zip([1, 10, 50]      ,					[10, 50, 500]      ):
									self.check_over_forward(time_step=lowerCAmelCase__      ,					num_inference_steps=lowerCAmelCase__      )






							def     __a   (   self      )	-> List[Any]:
								for t, eta in zip([1, 10, 49]      ,					[0.0, 0.5, 1.0]      ):
									self.check_over_forward(time_step=lowerCAmelCase__      ,					eta=lowerCAmelCase__      )






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		List[str]	=				self.scheduler_classes[0]
								a      :		Tuple	=				self.get_scheduler_config()
								a      :		Tuple	=				scheduler_class(**lowerCAmelCase__      )

								assert torch.sum(torch.abs(scheduler._get_variance(0      ,					0      ) - 0.0      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(420      ,					400      ) - 0.14_771      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(980      ,					960      ) - 0.32_460      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0      ,					0      ) - 0.0      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487      ,					486      ) - 0.00_979      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999      ,					998      ) - 0.02      )      ) < 1E-5






							def     __a   (   self      )	-> Tuple:
								a      :		Optional[Any]	=				self.scheduler_classes[0]
								a      :		Tuple	=				self.get_scheduler_config()
								a      :		str	=				scheduler_class(**lowerCAmelCase__      )

								a,       a      :		str	=				10, 0.0
								scheduler.set_timesteps(lowerCAmelCase__      )

								a      :		Tuple	=				self.dummy_model()
								a      :		str	=				self.dummy_sample_deter
								a      :		Dict	=				self.dummy_sample_deter + 0.1
								a      :		Tuple	=				self.dummy_sample_deter - 0.1

								a      :		int	=				samplea.shape[0]
								a      :		str	=				torch.stack([samplea, samplea, samplea]      ,					dim=0      )
								a      :		Any	=				torch.arange(lowerCAmelCase__      )[0:3, None].repeat(1      ,					lowerCAmelCase__      )

								a      :		Any	=				model(samples.flatten(0      ,					1      )      ,					timesteps.flatten(0      ,					1      )      )
								a      :		List[Any]	=				scheduler.batch_step_no_noise(lowerCAmelCase__      ,					timesteps.flatten(0      ,					1      )      ,					samples.flatten(0      ,					1      )      ,					lowerCAmelCase__      )

								a      :		Optional[Any]	=				torch.sum(torch.abs(lowerCAmelCase__      )      )
								a      :		Optional[Any]	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_sum.item() - 1_147.7_904      ) < 1E-2
								assert abs(result_mean.item() - 0.4_982      ) < 1E-3






							def     __a   (   self      )	-> Optional[Any]:
								a      :		List[str]	=				self.full_loop()

								a      :		Any	=				torch.sum(torch.abs(lowerCAmelCase__      )      )
								a      :		str	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_sum.item() - 172.0_067      ) < 1E-2
								assert abs(result_mean.item() - 0.223_967      ) < 1E-3






							def     __a   (   self      )	-> Dict:
								a      :		Dict	=				self.full_loop(prediction_type="v_prediction"      )

								a      :		Optional[int]	=				torch.sum(torch.abs(lowerCAmelCase__      )      )
								a      :		List[str]	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_sum.item() - 52.5_302      ) < 1E-2
								assert abs(result_mean.item() - 0.0_684      ) < 1E-3






							def     __a   (   self      )	-> Any:
								# We specify different beta, so that the first alpha is 0.99
								a      :		Dict	=				self.full_loop(set_alpha_to_one=lowerCAmelCase__      ,					beta_start=0.01      )
								a      :		Optional[Any]	=				torch.sum(torch.abs(lowerCAmelCase__      )      )
								a      :		List[Any]	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_sum.item() - 149.8_295      ) < 1E-2
								assert abs(result_mean.item() - 0.1_951      ) < 1E-3






							def     __a   (   self      )	-> Dict:
								# We specify different beta, so that the first alpha is 0.99
								a      :		Optional[int]	=				self.full_loop(set_alpha_to_one=lowerCAmelCase__      ,					beta_start=0.01      )
								a      :		List[str]	=				torch.sum(torch.abs(lowerCAmelCase__      )      )
								a      :		Any	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_sum.item() - 149.0_784      ) < 1E-2
								assert abs(result_mean.item() - 0.1_941      ) < 1E-3









'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
           'decoder.output_projection.weight',
       ]
       for k in ignore_keys:
              state_dict.pop(__lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )['model']
       remove_ignore_keys_(__lowercase     )
       __UpperCamelCase       =  state_dict['encoder.embed_tokens.weight'].shape[0]

       __UpperCamelCase       =  MBartConfig.from_pretrained(__lowercase				,  vocab_size=__lowercase     )
       if mbart_aa and finetuned:
              __UpperCamelCase       =  'relu'

       __UpperCamelCase       =  state_dict['decoder.embed_tokens.weight']
       __UpperCamelCase       =  MBartForConditionalGeneration(__lowercase     )
       model.model.load_state_dict(__lowercase     )

       if finetuned:
              __UpperCamelCase       =  make_linear_from_emb(model.model.shared     )

       return model


if __name__ == "__main__":
    a__							:							Dict =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
    parser.add_argument(
        '''--hf_config''',
        default='''facebook/mbart-large-cc25''',
        type=str,
        help='''Which huggingface architecture to use: mbart-large''',
    )
    parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
    parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							str =convert_fairseq_mbart_checkpoint_from_disk(
        args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
    )
    model.save_pretrained(args.pytorch_dump_folder_path)
