



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					(PNDMScheduler,)
     UpperCamelCase__		     =					(('''num_inference_steps''', 50),)




     def 		snake_case_		(					self			,    **a__):
         A__					=       {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**a__)
         return config




     def 		snake_case_		(					self			,    a__=0			,    **a__):
         A__					=       dict(self.forward_default_kwargs)
         A__					=       kwargs.pop('''num_inference_steps'''			,    a__)
         A__					=       self.dummy_sample
         A__					=       0.1 * sample
         A__					=       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

             A__					=       scheduler.step_prk(a__			,    a__			,    a__			,    **a__).prev_sample
             A__					=       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__					=       scheduler.step_plms(a__			,    a__			,    a__			,    **a__).prev_sample
             A__					=       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 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self			,    a__=0			,    **a__):
         A__					=       dict(self.forward_default_kwargs)
         A__					=       kwargs.pop('''num_inference_steps'''			,    a__)
         A__					=       self.dummy_sample
         A__					=       0.1 * sample
         A__					=       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

         for scheduler_class in self.scheduler_classes:
             A__					=       self.get_scheduler_config()
             A__					=       scheduler_class(**a__)
             scheduler.set_timesteps(a__)

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(a__)
                 A__					=       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__					=       dummy_past_residuals[:]

             A__					=       scheduler.step_prk(a__			,    a__			,    a__			,    **a__).prev_sample
             A__					=       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__					=       scheduler.step_plms(a__			,    a__			,    a__			,    **a__).prev_sample
             A__					=       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 		snake_case_		(					self			,    **a__):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config(**a__)
         A__					=       scheduler_class(**a__)

         A__					=       1_0
         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter
         scheduler.set_timesteps(a__)

         for i, t in enumerate(scheduler.prk_timesteps):
             A__					=       model(a__			,    a__)
             A__					=       scheduler.step_prk(a__			,    a__			,    a__).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps):
             A__					=       model(a__			,    a__)
             A__					=       scheduler.step_plms(a__			,    a__			,    a__).prev_sample

         return sample




     def 		snake_case_		(					self):
         A__					=       dict(self.forward_default_kwargs)

         A__					=       kwargs.pop('''num_inference_steps'''			,    a__)

         for scheduler_class in self.scheduler_classes:
             A__					=       self.get_scheduler_config()
             A__					=       scheduler_class(**a__)

             A__					=       self.dummy_sample
             A__					=       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__					=       num_inference_steps

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

             A__					=       scheduler.step_prk(a__			,    0			,    a__			,    **a__).prev_sample
             A__					=       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__					=       scheduler.step_plms(a__			,    0			,    a__			,    **a__).prev_sample
             A__					=       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 		snake_case_		(					self):
         for timesteps in [1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




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

         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config(steps_offset=1)
         A__					=       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 		snake_case_		(					self):
         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=a__			,    beta_end=a__)




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




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




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




     def 		snake_case_		(					self):
         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 		snake_case_		(					self):
         # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
         A__					=       2_7

         for scheduler_class in self.scheduler_classes:
             A__					=       self.dummy_sample
             A__					=       0.1 * sample

             A__					=       self.get_scheduler_config()
             A__					=       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__					=       scheduler.step_prk(a__			,    a__			,    a__).prev_sample




     def 		snake_case_		(					self):
         with self.assertRaises(a__):
             A__					=       self.scheduler_classes[0]
             A__					=       self.get_scheduler_config()
             A__					=       scheduler_class(**a__)

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




     def 		snake_case_		(					self):
         A__					=       self.full_loop()
         A__					=       torch.sum(torch.abs(a__))
         A__					=       torch.mean(torch.abs(a__))

         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 		snake_case_		(					self):
         A__					=       self.full_loop(prediction_type='''v_prediction''')
         A__					=       torch.sum(torch.abs(a__))
         A__					=       torch.mean(torch.abs(a__))

         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 		snake_case_		(					self):
         # We specify different beta, so that the first alpha is 0.99
         A__					=       self.full_loop(set_alpha_to_one=a__			,    beta_start=0.0_1)
         A__					=       torch.sum(torch.abs(a__))
         A__					=       torch.mean(torch.abs(a__))

         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 		snake_case_		(					self):
         # We specify different beta, so that the first alpha is 0.99
         A__					=       self.full_loop(set_alpha_to_one=a__			,    beta_start=0.0_1)
         A__					=       torch.sum(torch.abs(a__))
         A__					=       torch.mean(torch.abs(a__))

         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





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


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''timm_backbone'''




     def __init__(					self			,    a__=None			,    a__=3			,    a__=True			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       backbone
         A__					=       num_channels
         A__					=       features_only
         A__					=       use_pretrained_backbone
         A__					=       True
         A__					=       out_indices if out_indices is not None else (-1,)
