



"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				(HeunDiscreteScheduler,)
   _lowerCamelCase												=				10


   def        lowercase				(							self      :							List[Any],     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase			)
         return config


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],     [0.0002, 0.002, 0.02]			):
               self.check_over_configs(beta_start=lowerCAmelCase,     beta_end=lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Any:
         for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase,     clip_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Tuple:
         lowercase  :      Tuple					=   self.scheduler_classes[0]
         lowercase  :      int					=   self.get_scheduler_config()
         lowercase  :      str					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      Tuple					=   self.dummy_model()
         lowercase  :      Dict					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      Optional[Any]					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      Any					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Union[str, Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Tuple					=   output.prev_sample

         lowercase  :      Tuple					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Tuple			)    ->       List[str]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      Any					=   self.get_scheduler_config(prediction_type='v_prediction'			)
         lowercase  :      List[str]					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      List[Any]					=   self.dummy_model()
         lowercase  :      List[Any]					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      List[str]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Any					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      List[str]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      Optional[int]					=   self.scheduler_classes[0]
         lowercase  :      Optional[int]					=   self.get_scheduler_config()
         lowercase  :      Dict					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      int					=   self.dummy_model()
         lowercase  :      Tuple					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               lowercase  :      Optional[Any]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Optional[int]					=   output.prev_sample

         lowercase  :      str					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if str(lowerCAmelCase			).startswith('cpu'			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         elif str(lowerCAmelCase			).startswith('mps'			):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002			) < 1e-2
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      str					=   self.get_scheduler_config()
         lowercase  :      Tuple					=   scheduler_class(**lowerCAmelCase,     use_karras_sigmas=lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      Dict					=   self.dummy_model()
         lowercase  :      Any					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for t in scheduler.timesteps:
               lowercase  :      int					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      int					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      str					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Union[str, Any]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         assert abs(result_sum.item() - 0.0_0015			) < 1e-2
         assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07			) < 1e-2





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      Any					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '			)
         with self.assertRaises(lowerCAmelCase			):
               DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      int					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)
