


'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	str		=     (KDPMaDiscreteScheduler,)
     lowerCamelCase				:	Optional[int]		=     10





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      **UpperCAmelCase__       :      Optional[int]    )				->  Any:
        lowerCAmelCase     =			{
            'num_train_timesteps': 1_1_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**UpperCAmelCase__    )
        return config





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Tuple:
        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]    ,      [0.0_002, 0.002, 0.02]    ):
           self.check_over_configs(beta_start=UpperCAmelCase__    ,      beta_end=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  int:
        for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Dict:
        lowerCAmelCase     =			self.scheduler_classes[0]
        lowerCAmelCase     =			self.get_scheduler_config(prediction_type='v_prediction'    )
        lowerCAmelCase     =			scheduler_class(**UpperCAmelCase__    )

        scheduler.set_timesteps(self.num_inference_steps    )

        lowerCAmelCase     =			self.dummy_model()
        lowerCAmelCase     =			self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase     =			sample.to(UpperCAmelCase__    )

        for i, t in enumerate(scheduler.timesteps    ):
           lowerCAmelCase     =			scheduler.scale_model_input(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			scheduler.step(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			output.prev_sample

        lowerCAmelCase     =			torch.sum(torch.abs(UpperCAmelCase__    )    )
        lowerCAmelCase     =			torch.mean(torch.abs(UpperCAmelCase__    )    )

        if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07    ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10    ) < 1E-3
        else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07    ) < 1E-2
           assert abs(result_mean.item() - 0.0_002    ) < 1E-3





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Dict:
        if torch_device == "mps":
           return
        lowerCAmelCase     =			self.scheduler_classes[0]
        lowerCAmelCase     =			self.get_scheduler_config()
        lowerCAmelCase     =			scheduler_class(**UpperCAmelCase__    )

        scheduler.set_timesteps(self.num_inference_steps    )

        lowerCAmelCase     =			self.dummy_model()
        lowerCAmelCase     =			self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCAmelCase     =			sample.to(UpperCAmelCase__    )

        for i, t in enumerate(scheduler.timesteps    ):
           lowerCAmelCase     =			scheduler.scale_model_input(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			scheduler.step(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			output.prev_sample

        lowerCAmelCase     =			torch.sum(torch.abs(UpperCAmelCase__    )    )
        lowerCAmelCase     =			torch.mean(torch.abs(UpperCAmelCase__    )    )

        if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4_125    ) < 1E-2
           assert abs(result_mean.item() - 0.0_266    ) < 1E-3
        else:
           # CUDA
           assert abs(result_sum.item() - 20.4_125    ) < 1E-2
           assert abs(result_mean.item() - 0.0_266    ) < 1E-3





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Optional[Any]:
        if torch_device == "mps":
           return
        lowerCAmelCase     =			self.scheduler_classes[0]
        lowerCAmelCase     =			self.get_scheduler_config()
        lowerCAmelCase     =			scheduler_class(**UpperCAmelCase__    )

        scheduler.set_timesteps(self.num_inference_steps    ,      device=UpperCAmelCase__    )

        lowerCAmelCase     =			self.dummy_model()
        lowerCAmelCase     =			self.dummy_sample_deter.to(UpperCAmelCase__    ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
           lowerCAmelCase     =			scheduler.scale_model_input(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			scheduler.step(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			output.prev_sample

        lowerCAmelCase     =			torch.sum(torch.abs(UpperCAmelCase__    )    )
        lowerCAmelCase     =			torch.mean(torch.abs(UpperCAmelCase__    )    )

        if str(UpperCAmelCase__    ).startswith('cpu'    ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4_125    ) < 1E-2
           assert abs(result_mean.item() - 0.0_266    ) < 1E-3
        else:
           # CUDA
           assert abs(result_sum.item() - 20.4_125    ) < 1E-2
           assert abs(result_mean.item() - 0.0_266    ) < 1E-3












import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      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.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



