





'''simple docstring'''

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class __a (   _snake_case			):
   __UpperCamelCase   : Dict	      =	(DDPMScheduler,)





   def      UpperCAmelCase__     (					self				:			int				,**lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       {
          "num_train_timesteps": 1000,
          "beta_start": 0.0_001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
          "variance_type": "fixed_small",
          "clip_sample": True,
      }

      config.update(**snake_case_    )
      return config





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=snake_case_    )





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,[0.002, 0.02, 0.2, 2]    ):
         self.check_over_configs(beta_start=snake_case_				,beta_end=snake_case_    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=snake_case_    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=snake_case_    )





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      self.check_over_configs(thresholding=snake_case_    )
      for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
            self.check_over_configs(
                thresholding=snake_case_				,prediction_type=snake_case_				,sample_max_value=snake_case_				,)





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      for t in [0, 500, 999]:
         self.check_over_forward(time_step=snake_case_    )





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**snake_case_    )

      assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487    ) - 0.00_979    )    ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999    ) - 0.02    )    ) < 1E-5





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       len(snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       self.dummy_model()
      __SCREAMING_SNAKE_CASE      							=       self.dummy_sample_deter
      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )

      for t in reversed(range(snake_case_    )    ):
         # 1. predict noise residual
         __SCREAMING_SNAKE_CASE      							=       model(snake_case_				,snake_case_    )

         # 2. predict previous mean of sample x_t-1
         __SCREAMING_SNAKE_CASE      							=       scheduler.step(snake_case_				,snake_case_				,snake_case_				,generator=snake_case_    ).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         __SCREAMING_SNAKE_CASE      							=       pred_prev_sample

      __SCREAMING_SNAKE_CASE      							=       torch.sum(torch.abs(snake_case_    )    )
      __SCREAMING_SNAKE_CASE      							=       torch.mean(torch.abs(snake_case_    )    )

      assert abs(result_sum.item() - 258.9_606    ) < 1E-2
      assert abs(result_mean.item() - 0.3_372    ) < 1E-3





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config(prediction_type="""v_prediction"""    )
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       len(snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       self.dummy_model()
      __SCREAMING_SNAKE_CASE      							=       self.dummy_sample_deter
      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )

      for t in reversed(range(snake_case_    )    ):
         # 1. predict noise residual
         __SCREAMING_SNAKE_CASE      							=       model(snake_case_				,snake_case_    )

         # 2. predict previous mean of sample x_t-1
         __SCREAMING_SNAKE_CASE      							=       scheduler.step(snake_case_				,snake_case_				,snake_case_				,generator=snake_case_    ).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         __SCREAMING_SNAKE_CASE      							=       pred_prev_sample

      __SCREAMING_SNAKE_CASE      							=       torch.sum(torch.abs(snake_case_    )    )
      __SCREAMING_SNAKE_CASE      							=       torch.mean(torch.abs(snake_case_    )    )

      assert abs(result_sum.item() - 202.0_296    ) < 1E-2
      assert abs(result_mean.item() - 0.2_631    ) < 1E-3





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       [100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       scheduler.timesteps

      for i, timestep in enumerate(snake_case_    ):
         if i == len(snake_case_    ) - 1:
            __SCREAMING_SNAKE_CASE      							=       -1
         else:
            __SCREAMING_SNAKE_CASE      							=       timesteps[i + 1]

         __SCREAMING_SNAKE_CASE      							=       scheduler.previous_timestep(snake_case_    )
         __SCREAMING_SNAKE_CASE      							=       prev_t.item()

         self.assertEqual(snake_case_				,snake_case_    )





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       [100, 87, 50, 51, 0]

      with self.assertRaises(snake_case_				,msg="""`custom_timesteps` must be in descending order."""    ):
         scheduler.set_timesteps(timesteps=snake_case_    )





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       [100, 87, 50, 1, 0]
      __SCREAMING_SNAKE_CASE      							=       len(snake_case_    )

      with self.assertRaises(snake_case_				,msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""    ):
         scheduler.set_timesteps(num_inference_steps=snake_case_				,timesteps=snake_case_    )





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE      							=       self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE      							=       scheduler_class(**snake_case_    )

      __SCREAMING_SNAKE_CASE      							=       [scheduler.config.num_train_timesteps]

      with self.assertRaises(
          snake_case_				,msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""				,):
         scheduler.set_timesteps(timesteps=snake_case_    )












'''simple docstring'''

import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a        =						"__DUMMY_TRANSFORMERS_USER__"
a        =						"Dummy User"
a        =						"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"

a        =						"https://hub-ci.huggingface.co"
a        =						CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
a        =						CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
a        =						Path("~/.huggingface/hub_ci_token").expanduser()

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   monkeypatch.setattr(
       """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""							,	__UpperCAmelCase      )
   monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   HfFolder.save_token(__UpperCAmelCase      )
   yield
   HfFolder.delete_token()

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							)						->					Optional[Any]:

   '''simple docstring'''







   return HfApi(endpoint=__UpperCAmelCase      )

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       HfFolder.get_token()
   HfFolder.save_token(__UpperCAmelCase      )
   yield CI_HUB_USER_TOKEN
   if previous_token is not None:
      HfFolder.save_token(__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   def _cleanup_repo(__UpperCAmelCase      ):
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )

   return _cleanup_repo

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   @contextmanager
   def _temporary_repo(__UpperCAmelCase      ):
      try:
         yield repo_id
      finally:
         cleanup_repo(__UpperCAmelCase      )

   return _temporary_repo

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data/text_data.txt"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass

@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   return hf_private_dataset_repo_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data.zip"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass

@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_img_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data.zip"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass







@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_img_data_





