
"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(KDPMaDiscreteScheduler,)
   __lowerCAmelCase				=							10
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							List[str]									=				{
              "num_train_timesteps": 1_100,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]      ,       [0.0_0_0_2, 0.0_0_2, 0.0_2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          scheduler.set_timesteps(self.num_inference_steps							)

          lowercase       :							Optional[int]									=				self.dummy_model()
          lowercase       :							Union[str, Any]									=				self.dummy_sample_deter * scheduler.init_noise_sigma
          lowercase       :							List[Any]									=				sample.to(_a							)

          for i, t in enumerate(scheduler.timesteps							):
                 lowercase       :							Tuple									=				scheduler.scale_model_input(_a      ,       _a							)

                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

                 lowercase       :							Optional[int]									=				scheduler.step(_a      ,       _a      ,       _a							)
                 lowercase       :							Tuple									=				output.prev_sample

          lowercase       :							List[Any]									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Optional[int]									=				torch.mean(torch.abs(_a							)							)

          if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6934E-07							) < 1E-2
                 assert abs(result_mean.item() - 6.1112E-10							) < 1E-3
          else:
                 # CUDA
                 assert abs(result_sum.item() - 4.693428650170972E-07							) < 1E-2
                 assert abs(result_mean.item() - 0.0_0_0_2							) < 1E-3
   def      __magic_name__     (     self							):
          if torch_device == "mps":
                 return
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Optional[Any]									=				self.get_scheduler_config()
          lowercase       :							str									=				scheduler_class(**_a							)

          scheduler.set_timesteps(self.num_inference_steps							)

          lowercase       :							Tuple									=				self.dummy_model()
          lowercase       :							Union[str, Any]									=				self.dummy_sample_deter * scheduler.init_noise_sigma
          lowercase       :							Any									=				sample.to(_a							)

          for i, t in enumerate(scheduler.timesteps							):
                 lowercase       :							int									=				scheduler.scale_model_input(_a      ,       _a							)

                 lowercase       :							Optional[Any]									=				model(_a      ,       _a							)

                 lowercase       :							List[Any]									=				scheduler.step(_a      ,       _a      ,       _a							)
                 lowercase       :							List[Any]									=				output.prev_sample

          lowercase       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Optional[Any]									=				torch.mean(torch.abs(_a							)							)

          if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 2_0.4_1_2_5							) < 1E-2
                 assert abs(result_mean.item() - 0.0_2_6_6							) < 1E-3
          else:
                 # CUDA
                 assert abs(result_sum.item() - 2_0.4_1_2_5							) < 1E-2
                 assert abs(result_mean.item() - 0.0_2_6_6							) < 1E-3

   def      __magic_name__     (     self							):
          if torch_device == "mps":
                 return
          lowercase       :							List[str]									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							List[str]									=				scheduler_class(**_a							)

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

          lowercase       :							Dict									=				self.dummy_model()
          lowercase       :							Dict									=				self.dummy_sample_deter.to(_a							) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                 lowercase       :							Optional[Any]									=				scheduler.scale_model_input(_a      ,       _a							)

                 lowercase       :							Tuple									=				model(_a      ,       _a							)

                 lowercase       :							Tuple									=				scheduler.step(_a      ,       _a      ,       _a							)
                 lowercase       :							Any									=				output.prev_sample

          lowercase       :							List[Any]									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Dict									=				torch.mean(torch.abs(_a							)							)

          if str(_a							).startswith("cpu"							):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 2_0.4_1_2_5							) < 1E-2
                 assert abs(result_mean.item() - 0.0_2_6_6							) < 1E-3
          else:
                 # CUDA
                 assert abs(result_sum.item() - 2_0.4_1_2_5							) < 1E-2
                 assert abs(result_mean.item() - 0.0_2_6_6							) < 1E-3




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


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


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

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

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					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	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			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     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


