





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class     __lowerCAmelCase	(   __a):
       _lowercase    : List[Any]            =      (PNDMScheduler,)
       _lowercase    : Any            =      (("""num_inference_steps""", 50),)







       def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						str:



         '''simple docstring'''




         a__     : Any  ={
             "num_train_timesteps": 1_0_0_0,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**a__							)
         return config







       def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						List[Any]:



         '''simple docstring'''




         a__     : Tuple  =dict(self.forward_default_kwargs							)
         a__     : Tuple  =kwargs.pop("num_inference_steps"		,				a__							)
         a__     : Union[str, Any]  =self.dummy_sample
         a__     : Union[str, Any]  =0.1 * sample
         a__     : Any  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           a__     : List[str]  =self.get_scheduler_config(**a__							)
           a__     : Dict  =scheduler_class(**a__							)
           scheduler.set_timesteps(a__							)
           # copy over dummy past residuals
           a__     : Optional[int]  =dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(a__							)
             a__     : Union[str, Any]  =scheduler_class.from_pretrained(a__							)
             new_scheduler.set_timesteps(a__							)
             # copy over dummy past residuals
             a__     : Optional[Any]  =dummy_past_residuals[:]

           a__     : str  =scheduler.step_prk(a__		,				a__		,				a__		,				**a__							).prev_sample
           a__     : Dict  =new_scheduler.step_prk(a__		,				a__		,				a__		,				**a__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

           a__     : Any  =scheduler.step_plms(a__		,				a__		,				a__		,				**a__							).prev_sample
           a__     : Union[str, Any]  =new_scheduler.step_plms(a__		,				a__		,				a__		,				**a__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







       def        _lowercase  (   self							)       ->						Dict:



         '''simple docstring'''




         pass







       def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						List[Any]:



         '''simple docstring'''




         a__     : str  =dict(self.forward_default_kwargs							)
         a__     : List[Any]  =kwargs.pop("num_inference_steps"		,				a__							)
         a__     : List[str]  =self.dummy_sample
         a__     : List[str]  =0.1 * sample
         a__     : List[str]  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           a__     : str  =self.get_scheduler_config()
           a__     : List[Any]  =scheduler_class(**a__							)
           scheduler.set_timesteps(a__							)

           # copy over dummy past residuals (must be after setting timesteps)
           a__     : Any  =dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(a__							)
             a__     : Dict  =scheduler_class.from_pretrained(a__							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(a__							)

             # copy over dummy past residual (must be after setting timesteps)
             a__     : int  =dummy_past_residuals[:]

           a__     : List[str]  =scheduler.step_prk(a__		,				a__		,				a__		,				**a__							).prev_sample
           a__     : Union[str, Any]  =new_scheduler.step_prk(a__		,				a__		,				a__		,				**a__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

           a__     : int  =scheduler.step_plms(a__		,				a__		,				a__		,				**a__							).prev_sample
           a__     : Optional[Any]  =new_scheduler.step_plms(a__		,				a__		,				a__		,				**a__							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







       def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						Optional[int]:



         '''simple docstring'''




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

         a__     : List[Any]  =1_0
         a__     : Any  =self.dummy_model()
         a__     : Dict  =self.dummy_sample_deter
         scheduler.set_timesteps(a__							)

         for i, t in enumerate(scheduler.prk_timesteps							):
           a__     : List[str]  =model(a__		,				a__							)
           a__     : Tuple  =scheduler.step_prk(a__		,				a__		,				a__							).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps							):
           a__     : str  =model(a__		,				a__							)
           a__     : List[Any]  =scheduler.step_plms(a__		,				a__		,				a__							).prev_sample

         return sample







       def        _lowercase  (   self							)       ->						Dict:



         '''simple docstring'''




         a__     : Tuple  =dict(self.forward_default_kwargs							)

         a__     : Optional[Any]  =kwargs.pop("num_inference_steps"		,				a__							)

         for scheduler_class in self.scheduler_classes:
           a__     : Union[str, Any]  =self.get_scheduler_config()
           a__     : Optional[int]  =scheduler_class(**a__							)

           a__     : List[str]  =self.dummy_sample
           a__     : str  =0.1 * sample

           if num_inference_steps is not None and hasattr(a__		,				"set_timesteps"							):
             scheduler.set_timesteps(a__							)
           elif num_inference_steps is not None and not hasattr(a__		,				"set_timesteps"							):
             a__     : int  =num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           a__     : str  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           a__     : int  =dummy_past_residuals[:]

           a__     : int  =scheduler.step_prk(a__		,				0		,				a__		,				**a__							).prev_sample
           a__     : Optional[Any]  =scheduler.step_prk(a__		,				1		,				a__		,				**a__							).prev_sample

           self.assertEqual(output_a.shape		,				sample.shape							)
           self.assertEqual(output_a.shape		,				output_a.shape							)

           a__     : str  =scheduler.step_plms(a__		,				0		,				a__		,				**a__							).prev_sample
           a__     : Union[str, Any]  =scheduler.step_plms(a__		,				1		,				a__		,				**a__							).prev_sample

           self.assertEqual(output_a.shape		,				sample.shape							)
           self.assertEqual(output_a.shape		,				output_a.shape							)







       def        _lowercase  (   self							)       ->						Union[str, Any]:



         '''simple docstring'''




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







       def        _lowercase  (   self							)       ->						List[Any]:



         '''simple docstring'''




         for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=a__							)

         a__     : Optional[int]  =self.scheduler_classes[0]
         a__     : List[str]  =self.get_scheduler_config(steps_offset=1							)
         a__     : List[str]  =scheduler_class(**a__							)
         scheduler.set_timesteps(1_0							)
         assert torch.equal(
             scheduler.timesteps		,				torch.LongTensor(
                 [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]							)		,				)







       def        _lowercase  (   self							)       ->						List[Any]:



         '''simple docstring'''




         for beta_start, beta_end in zip([0.00_01, 0.0_01]		,				[0.0_02, 0.02]							):
           self.check_over_configs(beta_start=a__		,				beta_end=a__							)







       def        _lowercase  (   self							)       ->						int:



         '''simple docstring'''




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







       def        _lowercase  (   self							)       ->						Tuple:



         '''simple docstring'''




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







       def        _lowercase  (   self							)       ->						Dict:



         '''simple docstring'''




         for t in [1, 5, 1_0]:
           self.check_over_forward(time_step=a__							)







       def        _lowercase  (   self							)       ->						int:



         '''simple docstring'''




         for t, num_inference_steps in zip([1, 5, 1_0]		,				[1_0, 5_0, 1_0_0]							):
           self.check_over_forward(num_inference_steps=a__							)







       def        _lowercase  (   self							)       ->						str:



         '''simple docstring'''




         a__     : int  =2_7

         for scheduler_class in self.scheduler_classes:
           a__     : Any  =self.dummy_sample
           a__     : Optional[Any]  =0.1 * sample

           a__     : Tuple  =self.get_scheduler_config()
           a__     : List[str]  =scheduler_class(**a__							)

           scheduler.set_timesteps(a__							)

           # before power of 3 fix, would error on first step, so we only need to do two
           for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             a__     : Dict  =scheduler.step_prk(a__		,				a__		,				a__							).prev_sample







       def        _lowercase  (   self							)       ->						Optional[int]:



         '''simple docstring'''




         with self.assertRaises(a__							):
           a__     : int  =self.scheduler_classes[0]
           a__     : str  =self.get_scheduler_config()
           a__     : Dict  =scheduler_class(**a__							)

           scheduler.step_plms(self.dummy_sample		,				1		,				self.dummy_sample							).prev_sample







       def        _lowercase  (   self							)       ->						List[str]:



         '''simple docstring'''




         a__     : Tuple  =self.full_loop()
         a__     : str  =torch.sum(torch.abs(a__							)							)
         a__     : Optional[int]  =torch.mean(torch.abs(a__							)							)

         assert abs(result_sum.item() - 1_98.13_18							) < 1E-2
         assert abs(result_mean.item() - 0.25_80							) < 1E-3







       def        _lowercase  (   self							)       ->						Optional[int]:



         '''simple docstring'''




         a__     : str  =self.full_loop(prediction_type="v_prediction"							)
         a__     : Dict  =torch.sum(torch.abs(a__							)							)
         a__     : List[str]  =torch.mean(torch.abs(a__							)							)

         assert abs(result_sum.item() - 67.39_86							) < 1E-2
         assert abs(result_mean.item() - 0.08_78							) < 1E-3







       def        _lowercase  (   self							)       ->						List[str]:



         '''simple docstring'''




         a__     : List[Any]  =self.full_loop(set_alpha_to_one=a__		,				beta_start=0.01							)
         a__     : str  =torch.sum(torch.abs(a__							)							)
         a__     : Tuple  =torch.mean(torch.abs(a__							)							)

         assert abs(result_sum.item() - 2_30.03_99							) < 1E-2
         assert abs(result_mean.item() - 0.29_95							) < 1E-3







       def        _lowercase  (   self							)       ->						Optional[int]:



         '''simple docstring'''




         a__     : List[Any]  =self.full_loop(set_alpha_to_one=a__		,				beta_start=0.01							)
         a__     : Tuple  =torch.sum(torch.abs(a__							)							)
         a__     : Union[str, Any]  =torch.mean(torch.abs(a__							)							)

         assert abs(result_sum.item() - 1_86.94_82							) < 1E-2
         assert abs(result_mean.item() - 0.24_34							) < 1E-3













import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase     :     Dict							=	logging.get_logger(__name__)

UpperCAmelCase     :     Dict							=	{
    """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : List[Any]            =      """mvp"""
      _lowercase    : Tuple            =      ["""past_key_values"""]
      _lowercase    : Dict            =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







      def __init__(   self		,				lowerCAmelCase__=5_0_2_6_7		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=False		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1		,				lowerCAmelCase__=0		,				lowerCAmelCase__=2		,				lowerCAmelCase__=True		,				lowerCAmelCase__=2		,				lowerCAmelCase__=2		,				lowerCAmelCase__=False		,				lowerCAmelCase__=1_0_0		,				lowerCAmelCase__=8_0_0		,				**lowerCAmelCase__		,				)       ->						str:



        '''simple docstring'''




        a__     : Dict  =vocab_size
        a__     : List[str]  =max_position_embeddings
        a__     : List[str]  =d_model
        a__     : Optional[Any]  =encoder_ffn_dim
        a__     : Dict  =encoder_layers
        a__     : List[str]  =encoder_attention_heads
        a__     : List[str]  =decoder_ffn_dim
        a__     : Tuple  =decoder_layers
        a__     : Tuple  =decoder_attention_heads
        a__     : Any  =dropout
        a__     : str  =attention_dropout
        a__     : str  =activation_dropout
        a__     : Optional[int]  =activation_function
        a__     : Union[str, Any]  =init_std
        a__     : Dict  =encoder_layerdrop
        a__     : List[str]  =decoder_layerdrop
        a__     : Union[str, Any]  =classifier_dropout
        a__     : Union[str, Any]  =use_cache
        a__     : Optional[Any]  =encoder_layers
        a__     : List[str]  =scale_embedding  # scale factor will be sqrt(d_model) if True
        a__     : int  =use_prompt
        a__     : Union[str, Any]  =prompt_length
        a__     : Dict  =prompt_mid_dim

        super().__init__(
            pad_token_id=lowerCAmelCase__		,				bos_token_id=lowerCAmelCase__		,				eos_token_id=lowerCAmelCase__		,				is_encoder_decoder=lowerCAmelCase__		,				decoder_start_token_id=lowerCAmelCase__		,				forced_eos_token_id=lowerCAmelCase__		,				**lowerCAmelCase__		,				)

        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"		,				lowerCAmelCase__							):
          a__     : Tuple  =self.bos_token_id
          warnings.warn(
              F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
              "The config can simply be saved and uploaded again to be fixed."							)






