






'''simple docstring'''



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			(PNDMScheduler,)
      UpperCamelCase_								=			(('num_inference_steps', 50),)






      def 				A__      ( self			:		Any     ,      **UpperCAmelCase			:		Any		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Dict				  ={
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0_0_0_1,
           '''beta_end''': 0.0_2,
           '''beta_schedule''': '''linear''',
       }

       config.update(**UpperCAmelCase		)
       return config






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]=0     ,      **UpperCAmelCase			:		int		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Dict				  =dict(self.forward_default_kwargs		)
       lowercase					:   Any				  =kwargs.pop('''num_inference_steps'''     ,      UpperCAmelCase		)
       lowercase					:   Optional[int]				  =self.dummy_sample
       lowercase					:   Union[str, Any]				  =0.1 * sample
       lowercase					:   List[str]				  =[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(UpperCAmelCase		)
         lowercase					:   Tuple				  =scheduler_class.from_pretrained(UpperCAmelCase		)
         new_scheduler.set_timesteps(UpperCAmelCase		)
         # copy over dummy past residuals
         lowercase					:   List[Any]				  =dummy_past_residuals[:]

        lowercase					:   Dict				  =scheduler.step_prk(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample
        lowercase					:   Any				  =new_scheduler.step_prk(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample

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

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

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






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


       '''simple docstring'''

       pass






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		List[Any]=0     ,      **UpperCAmelCase			:		List[Any]		)  ->  str:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =dict(self.forward_default_kwargs		)
       lowercase					:   Union[str, Any]				  =kwargs.pop('''num_inference_steps'''     ,      UpperCAmelCase		)
       lowercase					:   int				  =self.dummy_sample
       lowercase					:   Dict				  =0.1 * sample
       lowercase					:   List[str]				  =[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
        lowercase					:   Dict				  =self.get_scheduler_config()
        lowercase					:   str				  =scheduler_class(**UpperCAmelCase		)
        scheduler.set_timesteps(UpperCAmelCase		)

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

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

         # copy over dummy past residual (must be after setting timesteps)
         lowercase					:   Union[str, Any]				  =dummy_past_residuals[:]

        lowercase					:   Optional[Any]				  =scheduler.step_prk(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample
        lowercase					:   List[str]				  =new_scheduler.step_prk(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample

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

        lowercase					:   List[Any]				  =scheduler.step_plms(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample
        lowercase					:   Tuple				  =new_scheduler.step_plms(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample

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






      def 				A__      ( self			:		Optional[Any]     ,      **UpperCAmelCase			:		Dict		)  ->  Tuple:


       '''simple docstring'''

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

       lowercase					:   str				  =10
       lowercase					:   Tuple				  =self.dummy_model()
       lowercase					:   Optional[Any]				  =self.dummy_sample_deter
       scheduler.set_timesteps(UpperCAmelCase		)

       for i, t in enumerate(scheduler.prk_timesteps		):
        lowercase					:   Any				  =model(UpperCAmelCase     ,      UpperCAmelCase		)
        lowercase					:   int				  =scheduler.step_prk(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		).prev_sample

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

       return sample






      def 				A__      ( self			:		List[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Dict				  =dict(self.forward_default_kwargs		)

       lowercase					:   int				  =kwargs.pop('''num_inference_steps'''     ,      UpperCAmelCase		)

       for scheduler_class in self.scheduler_classes:
        lowercase					:   List[Any]				  =self.get_scheduler_config()
        lowercase					:   List[str]				  =scheduler_class(**UpperCAmelCase		)

        lowercase					:   List[Any]				  =self.dummy_sample
        lowercase					:   Optional[int]				  =0.1 * sample

        if num_inference_steps is not None and hasattr(UpperCAmelCase     ,      '''set_timesteps'''		):
         scheduler.set_timesteps(UpperCAmelCase		)
        elif num_inference_steps is not None and not hasattr(UpperCAmelCase     ,      '''set_timesteps'''		):
         lowercase					:   Optional[int]				  =num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        lowercase					:   Any				  =[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
        lowercase					:   Dict				  =dummy_past_residuals[:]

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

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

        lowercase					:   Optional[int]				  =scheduler.step_plms(UpperCAmelCase     ,      0     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample
        lowercase					:   int				  =scheduler.step_plms(UpperCAmelCase     ,      1     ,      UpperCAmelCase     ,      **UpperCAmelCase		).prev_sample

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






      def 				A__      ( self			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

       for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=UpperCAmelCase		)






      def 				A__      ( self			:		Dict		)  ->  int:


       '''simple docstring'''

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

       lowercase					:   List[Any]				  =self.scheduler_classes[0]
       lowercase					:   Optional[Any]				  =self.get_scheduler_config(steps_offset=1		)
       lowercase					:   Dict				  =scheduler_class(**UpperCAmelCase		)
       scheduler.set_timesteps(10		)
       assert torch.equal(
           scheduler.timesteps     ,      torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)     ,      )






      def 				A__      ( self			:		int		)  ->  List[Any]:


       '''simple docstring'''

       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]     ,      [0.0_0_2, 0.0_2]		):
        self.check_over_configs(beta_start=UpperCAmelCase     ,      beta_end=UpperCAmelCase		)






      def 				A__      ( self			:		int		)  ->  Optional[Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

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






      def 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       for t in [1, 5, 10]:
        self.check_over_forward(time_step=UpperCAmelCase		)






      def 				A__      ( self			:		Optional[Any]		)  ->  Any:


       '''simple docstring'''

       for t, num_inference_steps in zip([1, 5, 10]     ,      [10, 50, 100]		):
        self.check_over_forward(num_inference_steps=UpperCAmelCase		)






      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Dict				  =27

       for scheduler_class in self.scheduler_classes:
        lowercase					:   Optional[int]				  =self.dummy_sample
        lowercase					:   Optional[int]				  =0.1 * sample

        lowercase					:   str				  =self.get_scheduler_config()
        lowercase					:   int				  =scheduler_class(**UpperCAmelCase		)

        scheduler.set_timesteps(UpperCAmelCase		)

        # 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]		):
         lowercase					:   Any				  =scheduler.step_prk(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		).prev_sample






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


       '''simple docstring'''

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

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






      def 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.full_loop()
       lowercase					:   List[str]				  =torch.sum(torch.abs(UpperCAmelCase		)		)
       lowercase					:   Union[str, Any]				  =torch.mean(torch.abs(UpperCAmelCase		)		)

       assert abs(result_sum.item() - 1_9_8.1_3_1_8		) < 1e-2
       assert abs(result_mean.item() - 0.2_5_8_0		) < 1e-3






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   int				  =self.full_loop(prediction_type='''v_prediction'''		)
       lowercase					:   Optional[int]				  =torch.sum(torch.abs(UpperCAmelCase		)		)
       lowercase					:   Tuple				  =torch.mean(torch.abs(UpperCAmelCase		)		)

       assert abs(result_sum.item() - 6_7.3_9_8_6		) < 1e-2
       assert abs(result_mean.item() - 0.0_8_7_8		) < 1e-3






      def 				A__      ( self			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.full_loop(set_alpha_to_one=UpperCAmelCase     ,      beta_start=0.0_1		)
       lowercase					:   Optional[int]				  =torch.sum(torch.abs(UpperCAmelCase		)		)
       lowercase					:   List[str]				  =torch.mean(torch.abs(UpperCAmelCase		)		)

       assert abs(result_sum.item() - 2_3_0.0_3_9_9		) < 1e-2
       assert abs(result_mean.item() - 0.2_9_9_5		) < 1e-3






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


       '''simple docstring'''

       lowercase					:   Tuple				  =self.full_loop(set_alpha_to_one=UpperCAmelCase     ,      beta_start=0.0_1		)
       lowercase					:   List[Any]				  =torch.sum(torch.abs(UpperCAmelCase		)		)
       lowercase					:   Any				  =torch.mean(torch.abs(UpperCAmelCase		)		)

       assert abs(result_sum.item() - 1_8_6.9_4_8_2		) < 1e-2
       assert abs(result_mean.item() - 0.2_4_3_4		) < 1e-3











'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



