import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):
       # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
       A							:	Optional[int]          =	(ScoreSdeVeScheduler,)
       A							:	Tuple          =	()

       @property
       def 			snake_case__    (							self					:     str			):
           __snake_case       :				Any 			=    4
           __snake_case       :				Dict 			=    3
           __snake_case       :				str 			=    8
           __snake_case       :				str 			=    8

           __snake_case       :				List[str] 			=    torch.rand((batch_size, num_channels, height, width)			)

           return sample

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           __snake_case       :				Optional[Any] 			=    4
           __snake_case       :				Any 			=    3
           __snake_case       :				Dict 			=    8
           __snake_case       :				Union[str, Any] 			=    8

           __snake_case       :				List[str] 			=    batch_size * num_channels * height * width
           __snake_case       :				List[str] 			=    torch.arange(_lowerCAmelCase			)
           __snake_case       :				Dict 			=    sample.reshape(_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				List[str] 			=    sample / num_elems
           __snake_case       :				List[Any] 			=    sample.permute(3			,			0			,			1			,			2			)

           return sample

       def 			snake_case__    (							self					:     Any			):
           def model(_lowerCAmelCase					:     Tuple			,			_lowerCAmelCase					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			):
               return sample * t / (t + 1)

           return model

       def 			snake_case__    (							self					:     int			,			**_lowerCAmelCase					:     Optional[int]			):
           __snake_case       :				Optional[Any] 			=    {
               """num_train_timesteps""": 20_00,
               """snr""": 0.15,
               """sigma_min""": 0.01,
               """sigma_max""": 13_48,
               """sampling_eps""": 1e-5,
           }

           config.update(**_lowerCAmelCase			)
           return config

       def 			snake_case__    (							self					:     Dict			,			_lowerCAmelCase					:     Optional[Any]=0			,			**_lowerCAmelCase					:     Tuple			):
           __snake_case       :				Union[str, Any] 			=    dict(self.forward_default_kwargs			)

           for scheduler_class in self.scheduler_classes:
               __snake_case       :				Dict 			=    self.dummy_sample
               __snake_case       :				Optional[int] 			=    0.1 * sample

               __snake_case       :				Dict 			=    self.get_scheduler_config(**_lowerCAmelCase			)
               __snake_case       :				int 			=    scheduler_class(**_lowerCAmelCase			)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCAmelCase			)
                   __snake_case       :				Optional[Any] 			=    scheduler_class.from_pretrained(_lowerCAmelCase			)

               __snake_case       :				List[Any] 			=    scheduler.step_pred(
                   _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample
               __snake_case       :				Optional[int] 			=    new_scheduler.step_pred(
                   _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample

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

               __snake_case       :				str 			=    scheduler.step_correct(_lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample
               __snake_case       :				List[str] 			=    new_scheduler.step_correct(
                   _lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample

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

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     Optional[int]=0			,			**_lowerCAmelCase					:     Optional[int]			):
           __snake_case       :				int 			=    dict(self.forward_default_kwargs			)
           kwargs.update(_lowerCAmelCase			)

           for scheduler_class in self.scheduler_classes:
               __snake_case       :				str 			=    self.dummy_sample
               __snake_case       :				Optional[int] 			=    0.1 * sample

               __snake_case       :				Any 			=    self.get_scheduler_config()
               __snake_case       :				List[Any] 			=    scheduler_class(**_lowerCAmelCase			)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCAmelCase			)
                   __snake_case       :				Tuple 			=    scheduler_class.from_pretrained(_lowerCAmelCase			)

               __snake_case       :				Dict 			=    scheduler.step_pred(
                   _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample
               __snake_case       :				Optional[int] 			=    new_scheduler.step_pred(
                   _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample

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

               __snake_case       :				Optional[int] 			=    scheduler.step_correct(_lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample
               __snake_case       :				Optional[Any] 			=    new_scheduler.step_correct(
                   _lowerCAmelCase			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample

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

       def 			snake_case__    (							self					:     Union[str, Any]			):
           for timesteps in [10, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Any			):
           for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]			,			[1, 1_00, 10_00]			):
               self.check_over_configs(sigma_min=_lowerCAmelCase			,			sigma_max=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Any			):
           for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				Union[str, Any] 			=    dict(self.forward_default_kwargs			)

           __snake_case       :				List[Any] 			=    self.scheduler_classes[0]
           __snake_case       :				List[str] 			=    self.get_scheduler_config()
           __snake_case       :				Optional[int] 			=    scheduler_class(**_lowerCAmelCase			)

           __snake_case       :				Optional[Any] 			=    3

           __snake_case       :				List[str] 			=    self.dummy_model()
           __snake_case       :				List[Any] 			=    self.dummy_sample_deter

           scheduler.set_sigmas(_lowerCAmelCase			)
           scheduler.set_timesteps(_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    torch.manual_seed(0			)

           for i, t in enumerate(scheduler.timesteps			):
               __snake_case       :				List[str] 			=    scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps			):
                   with torch.no_grad():
                       __snake_case       :				List[str] 			=    model(_lowerCAmelCase			,			_lowerCAmelCase			)
                   __snake_case       :				Union[str, Any] 			=    scheduler.step_correct(_lowerCAmelCase			,			_lowerCAmelCase			,			generator=_lowerCAmelCase			,			**_lowerCAmelCase			).prev_sample

               with torch.no_grad():
                   __snake_case       :				str 			=    model(_lowerCAmelCase			,			_lowerCAmelCase			)

               __snake_case       :				Optional[int] 			=    scheduler.step_pred(_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			generator=_lowerCAmelCase			,			**_lowerCAmelCase			)
               __snake_case			, __snake_case       :				Any 			=    output.prev_sample, output.prev_sample_mean

           __snake_case       :				List[Any] 			=    torch.sum(torch.abs(_lowerCAmelCase			)			)
           __snake_case       :				Tuple 			=    torch.mean(torch.abs(_lowerCAmelCase			)			)

           assert np.isclose(result_sum.item()			,			14372758528.0			)
           assert np.isclose(result_mean.item()			,			18714530.0			)





       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				str 			=    dict(self.forward_default_kwargs			)

           __snake_case       :				Dict 			=    kwargs.pop("""num_inference_steps"""			,			_lowerCAmelCase			)

           for scheduler_class in self.scheduler_classes:
               __snake_case       :				Dict 			=    self.get_scheduler_config()
               __snake_case       :				Tuple 			=    scheduler_class(**_lowerCAmelCase			)

               __snake_case       :				str 			=    self.dummy_sample
               __snake_case       :				Tuple 			=    0.1 * sample

               if num_inference_steps is not None and hasattr(_lowerCAmelCase			,			"""set_timesteps"""			):
                   scheduler.set_timesteps(_lowerCAmelCase			)
               elif num_inference_steps is not None and not hasattr(_lowerCAmelCase			,			"""set_timesteps"""			):
                   __snake_case       :				Union[str, Any] 			=    num_inference_steps

               __snake_case       :				Tuple 			=    scheduler.step_pred(_lowerCAmelCase			,			0			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample
               __snake_case       :				List[str] 			=    scheduler.step_pred(_lowerCAmelCase			,			1			,			_lowerCAmelCase			,			generator=torch.manual_seed(0			)			,			**_lowerCAmelCase			).prev_sample

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




import warnings

from .generation import FlaxGenerationMixin







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       # warning at import time
       warnings.warn(
           "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
           "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."   ,							__UpperCamelCase   ,							)



