



"""simple docstring"""





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


lowerCamelCase  			=		logging.get_logger(__name__)
logging.set_verbosity_info()
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      if "xprophetnet" in prophetnet_checkpoint_path:
            UpperCAmelCase_     					=				XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase__ )
            UpperCAmelCase_					,       UpperCAmelCase_     					=				XLMProphetNetForConditionalGeneration.from_pretrained(
                lowerCAmelCase__      ,     output_loading_info=lowerCAmelCase__ )
      else:
            UpperCAmelCase_     					=				ProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase__ )
            UpperCAmelCase_					,       UpperCAmelCase_     					=				ProphetNetForConditionalGeneration.from_pretrained(
                lowerCAmelCase__      ,     output_loading_info=lowerCAmelCase__ )

      UpperCAmelCase_     					=				["key_proj", "value_proj", "query_proj"]

      UpperCAmelCase_     					=				{
          "self_attn": "ngram_self_attn",
          "cross_attn": "encoder_attn",
          "cross_attn_layer_norm": "encoder_attn_layer_norm",
          "feed_forward_layer_norm": "final_layer_norm",
          "feed_forward": "",
          "intermediate": "fc1",
          "output": "fc2",
          "key_proj": "k_proj",
          "query_proj": "q_proj",
          "value_proj": "v_proj",
          "word_embeddings": "embed_tokens",
          "embeddings_layer_norm": "emb_layer_norm",
          "relative_pos_embeddings": "relative_linear",
          "ngram_embeddings": "ngram_input_embed",
          "position_embeddings": "embed_positions",
      }

      for key in loading_info["missing_keys"]:
            UpperCAmelCase_     					=				key.split("." )

            if attributes[0] == "lm_head":
                  UpperCAmelCase_     					=				prophet
                  UpperCAmelCase_     					=				prophet_old
            else:
                  UpperCAmelCase_     					=				prophet.prophetnet
                  UpperCAmelCase_     					=				prophet_old.model

            UpperCAmelCase_     					=				False
            for attribute in attributes:
                  if attribute in mapping:
                        UpperCAmelCase_     					=				mapping[attribute]
                        if not hasattr(lowerCAmelCase__      ,     lowerCAmelCase__ ) and len(lowerCAmelCase__ ) > 0:
                              UpperCAmelCase_     					=				attribute
                  elif hasattr(lowerCAmelCase__      ,     lowerCAmelCase__ ):
                        UpperCAmelCase_     					=				attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        UpperCAmelCase_     					=				old_model.weight
                        logger.info(f"""{attribute} is initialized.""" )
                        UpperCAmelCase_     					=				True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        UpperCAmelCase_     					=				old_model.bias
                        logger.info(f"""{attribute} is initialized""" )
                        UpperCAmelCase_     					=				True
                        break
                  elif attribute in special_keys and hasattr(lowerCAmelCase__      ,     "in_proj_weight" ):
                        UpperCAmelCase_     					=				old_model.in_proj_weight.shape[0] // 3
                        UpperCAmelCase_     					=				getattr(lowerCAmelCase__      ,     lowerCAmelCase__ )
                        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                        if attribute == "query_proj":
                              UpperCAmelCase_     					=				nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
                              UpperCAmelCase_     					=				nn.Parameter(old_model.in_proj_bias[:embed_dim] )

                        elif attribute == "key_proj":
                              UpperCAmelCase_     					=				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
                              UpperCAmelCase_     					=				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
                        elif attribute == "value_proj":
                              UpperCAmelCase_     					=				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
                              UpperCAmelCase_     					=				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
                        UpperCAmelCase_     					=				True
                        break
                  elif attribute == "position_embeddings":
                        assert (
                            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                        ), "Hidden size has to match"
                        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                        UpperCAmelCase_     					=				nn.Parameter(old_model.embed_positions.weight[:512, :] )
                        UpperCAmelCase_     					=				True
                        break

                  if attribute.isdigit():
                        UpperCAmelCase_     					=				model[int(lowerCAmelCase__ )]
                        UpperCAmelCase_     					=				old_model[int(lowerCAmelCase__ )]
                  else:
                        UpperCAmelCase_     					=				getattr(lowerCAmelCase__      ,     lowerCAmelCase__ )

                        if old_attribute == "":
                              UpperCAmelCase_     					=				old_model
                        else:
                              if not hasattr(lowerCAmelCase__      ,     lowerCAmelCase__ ):
                                    raise ValueError(f"""{old_model} does not have {old_attribute}""" )
                              UpperCAmelCase_     					=				getattr(lowerCAmelCase__      ,     lowerCAmelCase__ )

            if not is_key_init:
                  raise ValueError(f"""{key} was not correctly initialized!""" )

      print(f"""Saving model to {pytorch_dump_folder_path}""" )
      prophet.save_pretrained(lowerCAmelCase__ )


if __name__ == "__main__":
  lowerCamelCase  			=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  lowerCamelCase  			=		parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)











import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						int							=   (KDPMaDiscreteScheduler,)
  __lowerCAmelCase   :						Union[str, Any]							=   10


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       A_          =       {
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case )
       return config


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """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=__snake_case ,beta_end=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config(prediction_type='''v_prediction''' )
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       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 ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

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

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if str(__snake_case ).startswith('''cpu''' ):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3



