import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class     a_			(						UpperCamelCase_						):






 '''simple docstring'''






 UpperCAmelCase_     			= (PNDMScheduler,)
 UpperCAmelCase_     			= (("""num_inference_steps""", 50),)




 def 		__snake_case						(      self :  Dict      ,   **lowercase__ :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = {
            """num_train_timesteps""": 1_000,
            """beta_start""": 0.0_001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**_a)
        return config




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Any=0      ,   **lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = dict(self.forward_default_kwargs)
        lowerCAmelCase__					     = kwargs.pop('num_inference_steps'      ,   _a)
        lowerCAmelCase__					     = self.dummy_sample
        lowerCAmelCase__					     = 0.1 * sample
        lowerCAmelCase__					     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

               lowerCAmelCase__					     = scheduler.step_prk(_a      ,   _a      ,   _a      ,   **_a).prev_sample
               lowerCAmelCase__					     = 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"

               lowerCAmelCase__					     = scheduler.step_plms(_a      ,   _a      ,   _a      ,   **_a).prev_sample
               lowerCAmelCase__					     = 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 :  Tuple):







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Optional[Any]=0      ,   **lowercase__ :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = dict(self.forward_default_kwargs)
        lowerCAmelCase__					     = kwargs.pop('num_inference_steps'      ,   _a)
        lowerCAmelCase__					     = self.dummy_sample
        lowerCAmelCase__					     = 0.1 * sample
        lowerCAmelCase__					     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__					     = self.get_scheduler_config()
               lowerCAmelCase__					     = scheduler_class(**_a)
               scheduler.set_timesteps(_a)

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_a)
                      lowerCAmelCase__					     = 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)
                      lowerCAmelCase__					     = dummy_past_residuals[:]

               lowerCAmelCase__					     = scheduler.step_prk(_a      ,   _a      ,   _a      ,   **_a).prev_sample
               lowerCAmelCase__					     = 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"

               lowerCAmelCase__					     = scheduler.step_plms(_a      ,   _a      ,   _a      ,   **_a).prev_sample
               lowerCAmelCase__					     = 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 :  Union[str, Any]      ,   **lowercase__ :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config(**_a)
        lowerCAmelCase__					     = scheduler_class(**_a)

        lowerCAmelCase__					     = 10
        lowerCAmelCase__					     = self.dummy_model()
        lowerCAmelCase__					     = self.dummy_sample_deter
        scheduler.set_timesteps(_a)

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

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

        return sample




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = dict(self.forward_default_kwargs)

        lowerCAmelCase__					     = kwargs.pop('num_inference_steps'      ,   _a)

        for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__					     = self.get_scheduler_config()
               lowerCAmelCase__					     = scheduler_class(**_a)

               lowerCAmelCase__					     = self.dummy_sample
               lowerCAmelCase__					     = 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'):
                      lowerCAmelCase__					     = num_inference_steps

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

               lowerCAmelCase__					     = scheduler.step_prk(_a      ,   0      ,   _a      ,   **_a).prev_sample
               lowerCAmelCase__					     = scheduler.step_prk(_a      ,   1      ,   _a      ,   **_a).prev_sample

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

               lowerCAmelCase__					     = scheduler.step_plms(_a      ,   0      ,   _a      ,   **_a).prev_sample
               lowerCAmelCase__					     = 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 :  List[Any]):







        '''simple docstring'''



        for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=_a)




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



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

        lowerCAmelCase__					     = self.scheduler_classes[0]
        lowerCAmelCase__					     = self.get_scheduler_config(steps_offset=1)
        lowerCAmelCase__					     = scheduler_class(**_a)
        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 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        for beta_start, beta_end in zip([0.0_001, 0.001]      ,   [0.002, 0.02]):
               self.check_over_configs(beta_start=_a      ,   beta_end=_a)




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = 27

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

               lowerCAmelCase__					     = self.get_scheduler_config()
               lowerCAmelCase__					     = 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]):
                      lowerCAmelCase__					     = scheduler.step_prk(_a      ,   _a      ,   _a).prev_sample




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        with self.assertRaises(_a):
               lowerCAmelCase__					     = self.scheduler_classes[0]
               lowerCAmelCase__					     = self.get_scheduler_config()
               lowerCAmelCase__					     = scheduler_class(**_a)

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




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = self.full_loop()
        lowerCAmelCase__					     = torch.sum(torch.abs(_a))
        lowerCAmelCase__					     = torch.mean(torch.abs(_a))

        assert abs(result_sum.item() - 198.1_318) < 1e-2
        assert abs(result_mean.item() - 0.2_580) < 1e-3




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.full_loop(prediction_type='v_prediction')
        lowerCAmelCase__					     = torch.sum(torch.abs(_a))
        lowerCAmelCase__					     = torch.mean(torch.abs(_a))

        assert abs(result_sum.item() - 67.3_986) < 1e-2
        assert abs(result_mean.item() - 0.0_878) < 1e-3




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = self.full_loop(set_alpha_to_one=_a      ,   beta_start=0.01)
        lowerCAmelCase__					     = torch.sum(torch.abs(_a))
        lowerCAmelCase__					     = torch.mean(torch.abs(_a))

        assert abs(result_sum.item() - 230.0_399) < 1e-2
        assert abs(result_mean.item() - 0.2_995) < 1e-3




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.full_loop(set_alpha_to_one=_a      ,   beta_start=0.01)
        lowerCAmelCase__					     = torch.sum(torch.abs(_a))
        lowerCAmelCase__					     = torch.mean(torch.abs(_a))

        assert abs(result_sum.item() - 186.9_482) < 1e-2
        assert abs(result_mean.item() - 0.2_434) < 1e-3

import argparse
from collections import defaultdict

import yaml


lowerCAmelCase__     						=			'docs/source/en/_toctree.yml'






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = defaultdict(lowerCAmelCase__     )
       for doc in model_doc:
              counts[doc["local"]] += 1
       lowerCAmelCase__					     = [key for key, value in counts.items() if value > 1]

       lowerCAmelCase__					     = []
       for duplicate_key in duplicates:
              lowerCAmelCase__					     = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}     )
              if len(lowerCAmelCase__     ) > 1:
                     raise ValueError(
                         F"""{duplicate_key} is present several times in the documentation table of content at """
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'     )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}     )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]     )

       # Sort
       return sorted(lowerCAmelCase__     ,  key=lambda lowerCAmelCase__     : s["title"].lower()     )






def 				__lowerCamelCase		(						lowerCAmelCase__=False     ):
       with open(lowerCAmelCase__     ,  encoding='utf-8'     ) as f:
              lowerCAmelCase__					     = yaml.safe_load(f.read()     )

       # Get to the API doc
       lowerCAmelCase__					     = 0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       lowerCAmelCase__					     = content[api_idx]['sections']

       # Then to the model doc
       lowerCAmelCase__					     = 0
       while api_doc[model_idx]["title"] != "Models":
              model_idx += 1

       lowerCAmelCase__					     = api_doc[model_idx]['sections']

       lowerCAmelCase__					     = [(idx, section) for idx, section in enumerate(lowerCAmelCase__     ) if 'sections' in section]
       lowerCAmelCase__					     = False
       for idx, modality_doc in modalities_docs:
              lowerCAmelCase__					     = modality_doc['sections']
              lowerCAmelCase__					     = clean_model_doc_toc(lowerCAmelCase__     )

              if old_modality_doc != new_modality_doc:
                     lowerCAmelCase__					     = True
                     if overwrite:
                            lowerCAmelCase__					     = new_modality_doc

       if diff:
              if overwrite:
                     lowerCAmelCase__					     = model_doc
                     lowerCAmelCase__					     = api_doc
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
                            f.write(yaml.dump(lowerCAmelCase__     ,  allow_unicode=lowerCAmelCase__     )     )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    lowerCAmelCase__     						=			parser.parse_args()

    check_model_doc(args.fix_and_overwrite)
