

"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DEISMultistepScheduler,)
     _SCREAMING_SNAKE_CASE						:List[Any]       = (("""num_inference_steps""", 25),)

     def 					_a			(						self	,    **_a  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """solver_order""": 2,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self	,    _a=0	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	dict(self.forward_default_kwargs  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	kwargs.pop("""num_inference_steps"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_sample
           SCREAMING_SNAKE_CASE__   :      str      		=	0.1 * sample
           SCREAMING_SNAKE_CASE__   :      str      		=	[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.get_scheduler_config(**_a  )
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )
                 scheduler.set_timesteps(_a  )
                 # copy over dummy past residuals
                 SCREAMING_SNAKE_CASE__   :      Any      		=	dummy_past_residuals[: scheduler.config.solver_order]

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

                 SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Any      		=	sample, sample
                 for t in range(_a	,    time_step + scheduler.config.solver_order + 1  ):
                       SCREAMING_SNAKE_CASE__   :      str      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample
                       SCREAMING_SNAKE_CASE__   :      Any      		=	new_scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample

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

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           pass

     def 					_a			(						self	,    _a=0	,    **_a  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dict(self.forward_default_kwargs  )
           SCREAMING_SNAKE_CASE__   :      int      		=	kwargs.pop("""num_inference_steps"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.dummy_sample
           SCREAMING_SNAKE_CASE__   :      Any      		=	0.1 * sample
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )
                 scheduler.set_timesteps(_a  )

                 # copy over dummy past residuals (must be after setting timesteps)
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(_a  )
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	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)
                       SCREAMING_SNAKE_CASE__   :      Any      		=	dummy_past_residuals[: new_scheduler.config.solver_order]

                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample
                 SCREAMING_SNAKE_CASE__   :      str      		=	new_scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample

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

     def 					_a			(						self	,    _a=None	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           if scheduler is None:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
                 SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_scheduler_config(**_a  )
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config(**_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	10
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_sample_deter
           scheduler.set_timesteps(_a  )

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      str      		=	model(_a	,    _a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler.step(_a	,    _a	,    _a  ).prev_sample

           return sample

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	dict(self.forward_default_kwargs  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	kwargs.pop("""num_inference_steps"""	,    _a  )

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_scheduler_config()
                 SCREAMING_SNAKE_CASE__   :      str      		=	scheduler_class(**_a  )

                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_sample
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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"""  ):
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[residual + 0.2, residual + 0.15, residual + 0.10]
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dummy_past_residuals[: scheduler.config.solver_order]

                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	scheduler.timesteps[5]
                 SCREAMING_SNAKE_CASE__   :      Any      		=	scheduler.timesteps[6]

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample
                 SCREAMING_SNAKE_CASE__   :      str      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample

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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	DEISMultistepScheduler(**self.get_scheduler_config()  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.full_loop(scheduler=_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.23_916  ) < 1E-3

           SCREAMING_SNAKE_CASE__   :      Dict      		=	DPMSolverSinglestepScheduler.from_config(scheduler.config  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	DPMSolverMultistepScheduler.from_config(scheduler.config  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	UniPCMultistepScheduler.from_config(scheduler.config  )
           SCREAMING_SNAKE_CASE__   :      str      		=	DEISMultistepScheduler.from_config(scheduler.config  )

           SCREAMING_SNAKE_CASE__   :      str      		=	self.full_loop(scheduler=_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.23_916  ) < 1E-3

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           for timesteps in [25, 50, 100, 999, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           self.check_over_configs(thresholding=_a  )
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    algorithm_type="""deis"""	,    solver_order=_a	,    solver_type=_a	,    )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


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

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


           """simple docstring"""


           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       solver_order=_a	,    solver_type=_a	,    prediction_type=_a	,    algorithm_type=_a	,    )
                                   SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.full_loop(
                                       solver_order=_a	,    solver_type=_a	,    prediction_type=_a	,    algorithm_type=_a	,    )
                                   assert not torch.isnan(_a  ).any(), "Samples have nan numbers"

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           self.check_over_configs(lower_order_final=_a  )
           self.check_over_configs(lower_order_final=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
                 self.check_over_forward(num_inference_steps=_a	,    time_step=0  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.full_loop()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.23_916  ) < 1E-3

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.full_loop(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.091  ) < 1E-3





     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config(thresholding=_a	,    dynamic_thresholding_ratio=0  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	10
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_sample_deter.half()
           scheduler.set_timesteps(_a  )

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model(_a	,    _a  )
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a  ).prev_sample

           assert sample.dtype == torch.floataa







"""simple docstring"""


import re
from pathlib import Path
from unittest import TestCase

import pytest

@pytest.mark.integration
class      __a							(UpperCamelCase_):






     '''simple docstring'''

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


           """simple docstring"""


           with open(_a	,    encoding="""utf-8"""  ) as input_file:
                 SCREAMING_SNAKE_CASE__   :      str      		=	re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	input_file.read()
                 SCREAMING_SNAKE_CASE__   :      str      		=	regexp.search(_a  )

           return match

     def 					_a			(						self	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           with open(_a	,    encoding="""utf-8"""  ) as input_file:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""	,    re.DOTALL  )
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	input_file.read()
                 # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	regexp.finditer(_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[match for match in matches if match is not None and match.group(1  ) is not None]
           return matches[0] if matches else None

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	Path("""./datasets"""  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	list(dataset_paths.absolute().glob("""**/*.py"""  )  )

           for dataset in dataset_files:
                 if self._no_encoding_on_file_open(str(_a  )  ):
                       raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''  )





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Path("""./datasets"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	list(dataset_paths.absolute().glob("""**/*.py"""  )  )

           for dataset in dataset_files:
                 if self._no_print_statements(str(_a  )  ):
                       raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''  )




