'''simple docstring'''

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						(HeunDiscreteScheduler,)
   __UpperCAmelCase           =						10



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       ={
            """num_train_timesteps""": 1_100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**lowerCAmelCase__     )
        return config



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]					,      [0.0002, 0.002, 0.02]     ):
             self.check_over_configs(beta_start=lowerCAmelCase__					,      beta_end=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        for schedule in ["linear", "scaled_linear", "exp"]:
             self.check_over_configs(beta_schedule=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        for clip_sample_range in [1.0, 2.0, 3.0]:
             self.check_over_configs(clip_sample_range=lowerCAmelCase__					,      clip_sample=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        for prediction_type in ["epsilon", "v_prediction", "sample"]:
             self.check_over_configs(prediction_type=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.scheduler_classes[0]
        _UpperCamelCase  :List[Any]       =self.get_scheduler_config()
        _UpperCamelCase  :Tuple       =scheduler_class(**lowerCAmelCase__     )

        scheduler.set_timesteps(self.num_inference_steps     )

        _UpperCamelCase  :Any       =self.dummy_model()
        _UpperCamelCase  :Dict       =self.dummy_sample_deter * scheduler.init_noise_sigma
        _UpperCamelCase  :List[Any]       =sample.to(lowerCAmelCase__     )

        for i, t in enumerate(scheduler.timesteps     ):
             _UpperCamelCase  :List[Any]       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Union[str, Any]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :str       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :str       =output.prev_sample

        _UpperCamelCase  :Any       =torch.sum(torch.abs(lowerCAmelCase__     )     )
        _UpperCamelCase  :int       =torch.mean(torch.abs(lowerCAmelCase__     )     )

        if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 0.1233     ) < 1e-2
             assert abs(result_mean.item() - 0.0002     ) < 1e-3
        else:
             # CUDA
             assert abs(result_sum.item() - 0.1233     ) < 1e-2
             assert abs(result_mean.item() - 0.0002     ) < 1e-3



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =self.scheduler_classes[0]
        _UpperCamelCase  :Any       =self.get_scheduler_config(prediction_type="""v_prediction"""     )
        _UpperCamelCase  :Union[str, Any]       =scheduler_class(**lowerCAmelCase__     )

        scheduler.set_timesteps(self.num_inference_steps     )

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

        for i, t in enumerate(scheduler.timesteps     ):
             _UpperCamelCase  :Optional[int]       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Dict       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :str       =output.prev_sample

        _UpperCamelCase  :Dict       =torch.sum(torch.abs(lowerCAmelCase__     )     )
        _UpperCamelCase  :int       =torch.mean(torch.abs(lowerCAmelCase__     )     )

        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() - 0.0002     ) < 1e-3



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =self.scheduler_classes[0]
        _UpperCamelCase  :Optional[Any]       =self.get_scheduler_config()
        _UpperCamelCase  :str       =scheduler_class(**lowerCAmelCase__     )

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

        _UpperCamelCase  :int       =self.dummy_model()
        _UpperCamelCase  :int       =self.dummy_sample_deter.to(lowerCAmelCase__     ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
             _UpperCamelCase  :Tuple       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[int]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[int]       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :List[str]       =output.prev_sample

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

        if str(lowerCAmelCase__     ).startswith("""cpu"""     ):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 0.1233     ) < 1e-2
             assert abs(result_mean.item() - 0.0002     ) < 1e-3
        elif str(lowerCAmelCase__     ).startswith("""mps"""     ):
             # Larger tolerance on mps
             assert abs(result_mean.item() - 0.0002     ) < 1e-2
        else:
             # CUDA
             assert abs(result_sum.item() - 0.1233     ) < 1e-2
             assert abs(result_mean.item() - 0.0002     ) < 1e-3




   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :int       =self.scheduler_classes[0]
        _UpperCamelCase  :Optional[Any]       =self.get_scheduler_config()
        _UpperCamelCase  :Optional[int]       =scheduler_class(**lowerCAmelCase__					,      use_karras_sigmas=lowerCAmelCase__     )

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

        _UpperCamelCase  :int       =self.dummy_model()
        _UpperCamelCase  :int       =self.dummy_sample_deter.to(lowerCAmelCase__     ) * scheduler.init_noise_sigma
        _UpperCamelCase  :List[Any]       =sample.to(lowerCAmelCase__     )

        for t in scheduler.timesteps:
             _UpperCamelCase  :int       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :int       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :Optional[Any]       =output.prev_sample

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

        assert abs(result_sum.item() - 0.0_0015     ) < 1e-2
        assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07     ) < 1e-2
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def   a_					(_lowerCAmelCase  :   Tuple       )->				List[Any]:
    if "model" in orig_key:
        snake_case:						List[str]         =   orig_key.replace("""model."""			,	""""""       )
    if "norm1" in orig_key:
        snake_case:						int         =   orig_key.replace("""norm1"""			,	"""attention.output.LayerNorm"""       )
    if "norm2" in orig_key:
        snake_case:						Tuple         =   orig_key.replace("""norm2"""			,	"""output.LayerNorm"""       )
    if "norm" in orig_key:
        snake_case:						List[str]         =   orig_key.replace("""norm"""			,	"""LayerNorm"""       )
    if "transformer" in orig_key:
        snake_case:						Any         =   orig_key.split("""."""       )[0].split("""_"""       )[-1]
        snake_case:						List[Any]         =   orig_key.replace(F"transformer_{layer_num}"			,	F"encoder.layer.{layer_num}"       )
    if "mha.attn" in orig_key:
        snake_case:						List[Any]         =   orig_key.replace("""mha.attn"""			,	"""attention.self"""       )
    if "mha" in orig_key:
        snake_case:						str         =   orig_key.replace("""mha"""			,	"""attention"""       )
    if "W_q" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_q"""			,	"""self.query"""       )
    if "W_k" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_k"""			,	"""self.key"""       )
    if "W_v" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""W_v"""			,	"""self.value"""       )
    if "ff.0" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""ff.0"""			,	"""intermediate.dense"""       )
    if "ff.2" in orig_key:
        snake_case:						Optional[Any]         =   orig_key.replace("""ff.2"""			,	"""output.dense"""       )
    if "ff" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""ff"""			,	"""output.dense"""       )
    if "mlm_class" in orig_key:
        snake_case:						int         =   orig_key.replace("""mlm.mlm_class"""			,	"""cls.predictions.decoder"""       )
    if "mlm" in orig_key:
        snake_case:						Union[str, Any]         =   orig_key.replace("""mlm"""			,	"""cls.predictions.transform"""       )
    if "backbone.backbone.encoders" in orig_key:
        snake_case:						Optional[int]         =   orig_key.replace("""backbone.backbone.encoders"""			,	"""encoder.layer"""       )
    if "cls" not in orig_key:
        snake_case:						Dict         =   """mra.""" + orig_key

    return orig_key

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Any       )->				Dict:
    for key in orig_state_dict.copy().keys():
        snake_case:						Tuple         =   orig_state_dict.pop(_lowerCAmelCase       )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            snake_case:						int         =   val

    snake_case:						str         =   orig_state_dict["""cls.predictions.decoder.bias"""]
    snake_case:						List[str]         =   torch.arange(_lowerCAmelCase       ).expand((1, -1)       ) + 2

    return orig_state_dict

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   Any       )->				List[Any]:
    snake_case:						List[Any]         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )["""model_state_dict"""]
    snake_case:						Optional[int]         =   MraConfig.from_json_file(_lowerCAmelCase       )
    snake_case:						List[str]         =   MraForMaskedLM(_lowerCAmelCase       )

    snake_case:						Any         =   convert_checkpoint_helper(config.max_position_embeddings			,	_lowerCAmelCase       )

    print(model.load_state_dict(_lowerCAmelCase       )       )
    model.eval()
    model.save_pretrained(_lowerCAmelCase       )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
    __lowerCAmelCase    :  Union[str, Any]										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Mra model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    __lowerCAmelCase    :  Dict										= parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


