



"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 	__snake_case	(     _lowercase):
  snake_case__    :       List[str]        =						(DDPMParallelScheduler,)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, **__lowerCAmelCase					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**__lowerCAmelCase		)
         return config




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]						, [0.0_02, 0.02, 0.2, 2]		):
                self.check_over_configs(beta_start=__lowerCAmelCase						, beta_end=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         for variance in ["fixed_small", "fixed_large", "other"]:
                self.check_over_configs(variance_type=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         self.check_over_configs(thresholding=__lowerCAmelCase		)
         for threshold in [0.5, 1.0, 2.0]:
                for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=__lowerCAmelCase						, prediction_type=__lowerCAmelCase						, sample_max_value=__lowerCAmelCase						, )




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         for t in [0, 5_0_0, 9_9_9]:
                self.check_over_forward(time_step=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			self.scheduler_classes[0]
         _lowerCamelCase						:    List[str]         =			self.get_scheduler_config()
         _lowerCamelCase						:    List[Any]         =			scheduler_class(**__lowerCAmelCase		)

         assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.0_09_79		)		) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.02		)		) < 1E-5




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			self.scheduler_classes[0]
         _lowerCamelCase						:    Optional[int]         =			self.get_scheduler_config()
         _lowerCamelCase						:    Any         =			scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[int]         =			len(__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			self.dummy_model()
         _lowerCamelCase						:    Optional[Any]         =			self.dummy_sample_deter
         _lowerCamelCase						:    Optional[int]         =			self.dummy_sample_deter + 0.1
         _lowerCamelCase						:    Any         =			self.dummy_sample_deter - 0.1

         _lowerCamelCase						:    Optional[int]         =			samplea.shape[0]
         _lowerCamelCase						:    str         =			torch.stack([samplea, samplea, samplea]						, dim=0		)
         _lowerCamelCase						:    Union[str, Any]         =			torch.arange(__lowerCAmelCase		)[0:3, None].repeat(1						, __lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			model(samples.flatten(0						, 1		)						, timesteps.flatten(0						, 1		)		)
         _lowerCamelCase						:    Dict         =			scheduler.batch_step_no_noise(__lowerCAmelCase						, timesteps.flatten(0						, 1		)						, samples.flatten(0						, 1		)		)

         _lowerCamelCase						:    str         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:    Optional[Any]         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 11_53.18_33		) < 1E-2
         assert abs(result_mean.item() - 0.50_05		) < 1E-3




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			self.scheduler_classes[0]
         _lowerCamelCase						:    Optional[int]         =			self.get_scheduler_config()
         _lowerCamelCase						:    Union[str, Any]         =			scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:    str         =			len(__lowerCAmelCase		)

         _lowerCamelCase						:    List[str]         =			self.dummy_model()
         _lowerCamelCase						:    str         =			self.dummy_sample_deter
         _lowerCamelCase						:    Optional[int]         =			torch.manual_seed(0		)

         for t in reversed(range(__lowerCAmelCase		)		):
                # 1. predict noise residual
                _lowerCamelCase						:    Union[str, Any]         =			model(__lowerCAmelCase						, __lowerCAmelCase		)

                # 2. predict previous mean of sample x_t-1
                _lowerCamelCase						:    List[Any]         =			scheduler.step(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, generator=__lowerCAmelCase		).prev_sample

                _lowerCamelCase						:    List[Any]         =			pred_prev_sample

         _lowerCamelCase						:    int         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:    List[str]         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 2_58.96_06		) < 1E-2
         assert abs(result_mean.item() - 0.33_72		) < 1E-3




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			self.scheduler_classes[0]
         _lowerCamelCase						:    int         =			self.get_scheduler_config(prediction_type='''v_prediction'''		)
         _lowerCamelCase						:    Union[str, Any]         =			scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:    Dict         =			len(__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			self.dummy_model()
         _lowerCamelCase						:    Union[str, Any]         =			self.dummy_sample_deter
         _lowerCamelCase						:    Dict         =			torch.manual_seed(0		)

         for t in reversed(range(__lowerCAmelCase		)		):
                # 1. predict noise residual
                _lowerCamelCase						:    Optional[Any]         =			model(__lowerCAmelCase						, __lowerCAmelCase		)

                # 2. predict previous mean of sample x_t-1
                _lowerCamelCase						:    Dict         =			scheduler.step(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, generator=__lowerCAmelCase		).prev_sample

                _lowerCamelCase						:    Optional[int]         =			pred_prev_sample

         _lowerCamelCase						:    List[str]         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:    Union[str, Any]         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

         assert abs(result_sum.item() - 2_02.02_96		) < 1E-2
         assert abs(result_mean.item() - 0.26_31		) < 1E-3




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			self.scheduler_classes[0]
         _lowerCamelCase						:    Union[str, Any]         =			self.get_scheduler_config()
         _lowerCamelCase						:    Union[str, Any]         =			scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[int]         =			[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=__lowerCAmelCase		)

         _lowerCamelCase						:    str         =			scheduler.timesteps

         for i, timestep in enumerate(__lowerCAmelCase		):
                if i == len(__lowerCAmelCase		) - 1:
                       _lowerCamelCase						:    Tuple         =			-1
                else:
                       _lowerCamelCase						:    List[str]         =			timesteps[i + 1]

                _lowerCamelCase						:    Tuple         =			scheduler.previous_timestep(__lowerCAmelCase		)
                _lowerCamelCase						:    Any         =			prev_t.item()

                self.assertEqual(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			self.scheduler_classes[0]
         _lowerCamelCase						:    str         =			self.get_scheduler_config()
         _lowerCamelCase						:    Optional[int]         =			scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:    Tuple         =			[1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(__lowerCAmelCase						, msg='''`custom_timesteps` must be in descending order.'''		):
                scheduler.set_timesteps(timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			self.scheduler_classes[0]
         _lowerCamelCase						:    Any         =			self.get_scheduler_config()
         _lowerCamelCase						:    Union[str, Any]         =			scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			[1_0_0, 8_7, 5_0, 1, 0]
         _lowerCamelCase						:    str         =			len(__lowerCAmelCase		)

         with self.assertRaises(__lowerCAmelCase						, msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
                scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase						, timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			self.scheduler_classes[0]
         _lowerCamelCase						:    str         =			self.get_scheduler_config()
         _lowerCamelCase						:    Tuple         =			scheduler_class(**__lowerCAmelCase		)

         _lowerCamelCase						:    str         =			[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __lowerCAmelCase						, msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''						, ):
                scheduler.set_timesteps(timesteps=__lowerCAmelCase		)







"""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 	snake_case_  (				A_ :		str,    A_ :		str						):


       '''simple docstring'''





       if "xprophetnet" in prophetnet_checkpoint_path:
              _lowerCamelCase						:    Any         =			XLMProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    List[str]         =			XLMProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)
       else:
              _lowerCamelCase						:    str         =			ProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			ProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)

       _lowerCamelCase						:    Optional[Any]         =			['''key_proj''', '''value_proj''', '''query_proj''']

       _lowerCamelCase						:    List[Any]         =			{
           '''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"]:
              _lowerCamelCase						:    Union[str, Any]         =			key.split('''.'''						)

              if attributes[0] == "lm_head":
                     _lowerCamelCase						:    str         =			prophet
                     _lowerCamelCase						:    List[Any]         =			prophet_old
              else:
                     _lowerCamelCase						:    Optional[int]         =			prophet.prophetnet
                     _lowerCamelCase						:    Optional[Any]         =			prophet_old.model

              _lowerCamelCase						:    Any         =			False
              for attribute in attributes:
                     if attribute in mapping:
                            _lowerCamelCase						:    Optional[int]         =			mapping[attribute]
                            if not hasattr(A_,    A_						) and len(A_						) > 0:
                                   _lowerCamelCase						:    int         =			attribute
                     elif hasattr(A_,    A_						):
                            _lowerCamelCase						:    int         =			attribute

                     if attribute == "weight":
                            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                            _lowerCamelCase						:    Optional[int]         =			old_model.weight
                            logger.info(F'''{attribute} is initialized.'''						)
                            _lowerCamelCase						:    List[str]         =			True
                            break
                     elif attribute == "bias":
                            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                            _lowerCamelCase						:    int         =			old_model.bias
                            logger.info(F'''{attribute} is initialized'''						)
                            _lowerCamelCase						:    Union[str, Any]         =			True
                            break
                     elif attribute in special_keys and hasattr(A_,    '''in_proj_weight'''						):
                            _lowerCamelCase						:    Tuple         =			old_model.in_proj_weight.shape[0] // 3
                            _lowerCamelCase						:    List[Any]         =			getattr(A_,    A_						)
                            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":
                                   _lowerCamelCase						:    Optional[Any]         =			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]						)
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_bias[:embed_dim]						)

                            elif attribute == "key_proj":
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
                            elif attribute == "value_proj":
                                   _lowerCamelCase						:    Tuple         =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
                            _lowerCamelCase						:    Dict         =			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] == 5_12, "We want 512 position_embeddings."
                            _lowerCamelCase						:    List[str]         =			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
                            _lowerCamelCase						:    Optional[Any]         =			True
                            break

                     if attribute.isdigit():
                            _lowerCamelCase						:    Optional[int]         =			model[int(A_						)]
                            _lowerCamelCase						:    List[Any]         =			old_model[int(A_						)]
                     else:
                            _lowerCamelCase						:    List[str]         =			getattr(A_,    A_						)

                            if old_attribute == "":
                                   _lowerCamelCase						:    str         =			old_model
                            else:
                                   if not hasattr(A_,    A_						):
                                          raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
                                   _lowerCamelCase						:    Optional[int]         =			getattr(A_,    A_						)

              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(A_						)


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)


