




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class     A(		_UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		(DPMSolverSinglestepScheduler,)
 UpperCamelCase		  =		(('num_inference_steps', 25),)

 def 							a__					(							self   :    int		,				**A_   :    List[str]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       {
           """num_train_timesteps""": 1000,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
           """solver_order""": 2,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
           """sample_max_value""": 1.0,
           """algorithm_type""": """dpmsolver++""",
           """solver_type""": """midpoint""",
           """lambda_min_clipped""": -float('inf'    ),
           """variance_type""": None,
       }

       config.update(**A_    )
       return config

 def 							a__					(							self   :    List[str]		,				A_   :    Tuple=0		,				**A_   :    Optional[Any]    )  ->      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 + 0.10]

       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[: scheduler.config.solver_order]

             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[: new_scheduler.config.solver_order]

             lowerCamelCase_							=       sample, sample
             for t in range(A_		,				time_step + scheduler.config.solver_order + 1    ):
                   lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_		,				**A_    ).prev_sample
                   lowerCamelCase_							=       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   :    str    )  ->      Union[str, Any]:

       """simple docstring"""






       pass

 def 							a__					(							self   :    str		,				A_   :    Optional[Any]=0		,				**A_   :    Optional[Any]    )  ->      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 + 0.10]

       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[: scheduler.config.solver_order]

             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[: new_scheduler.config.solver_order]

             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_		,				**A_    ).prev_sample
             lowerCamelCase_							=       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   :    Optional[Any]		,				A_   :    Tuple=None		,				**A_   :    Tuple    )  ->      List[str]:

       """simple docstring"""






       if scheduler is None:
             lowerCamelCase_							=       self.scheduler_classes[0]
             lowerCamelCase_							=       self.get_scheduler_config(**A_    )
             lowerCamelCase_							=       scheduler_class(**A_    )

       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.timesteps    ):
             lowerCamelCase_							=       model(A_		,				A_    )
             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_    ).prev_sample

       return sample

 def 							a__					(							self   :    Dict    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
       lowerCamelCase_							=       50
       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter
       scheduler.set_timesteps(A_    )

       # make sure that the first t is uneven
       for i, t in enumerate(scheduler.timesteps[3:]    ):
             lowerCamelCase_							=       model(A_		,				A_    )
             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_    ).prev_sample

       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 0.2574    ) < 1E-3

 def 							a__					(							self   :    Optional[Any]    )  ->      Any:

       """simple docstring"""






       for timesteps in [25, 50, 100, 999, 1000]:
             self.check_over_configs(num_train_timesteps=A_    )

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

       """simple docstring"""






       lowerCamelCase_							=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
       lowerCamelCase_							=       self.full_loop(scheduler=A_    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 0.2791    ) < 1E-3

       lowerCamelCase_							=       DEISMultistepScheduler.from_config(scheduler.config    )
       lowerCamelCase_							=       DPMSolverMultistepScheduler.from_config(scheduler.config    )
       lowerCamelCase_							=       UniPCMultistepScheduler.from_config(scheduler.config    )
       lowerCamelCase_							=       DPMSolverSinglestepScheduler.from_config(scheduler.config    )

       lowerCamelCase_							=       self.full_loop(scheduler=A_    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 0.2791    ) < 1E-3

 def 							a__					(							self   :    int    )  ->      List[Any]:

       """simple docstring"""






       self.check_over_configs(thresholding=A_    )
       for order in [1, 2, 3]:
             for solver_type in ["midpoint", "heun"]:
                   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='dpmsolver++'		,				solver_order=A_		,				solver_type=A_		,				)

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

       """simple docstring"""






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

 def 							a__					(							self   :    int    )  ->      int:

       """simple docstring"""






       for algorithm_type in ["dpmsolver", "dpmsolver++"]:
             for solver_type in ["midpoint", "heun"]:
                   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_		,				)
                               lowerCamelCase_							=       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   :    Tuple    )  ->      Dict:

       """simple docstring"""






       self.check_over_configs(lower_order_final=A_    )
       self.check_over_configs(lower_order_final=A_    )

 def 							a__					(							self   :    Tuple    )  ->      Tuple:

       """simple docstring"""






       self.check_over_configs(lambda_min_clipped=-float('inf'    )    )
       self.check_over_configs(lambda_min_clipped=-5.1    )

 def 							a__					(							self   :    Tuple    )  ->      List[Any]:

       """simple docstring"""






       self.check_over_configs(variance_type=A_    )
       self.check_over_configs(variance_type='learned_range'    )

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

       """simple docstring"""






       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
             self.check_over_forward(num_inference_steps=A_		,				time_step=0    )

 def 							a__					(							self   :    Tuple    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop()
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 0.2791    ) < 1E-3

 def 							a__					(							self   :    Optional[Any]    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop(use_karras_sigmas=A_    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 0.2248    ) < 1E-3

 def 							a__					(							self   :    Any    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop(prediction_type='v_prediction'    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 0.1453    ) < 1E-3

 def 							a__					(							self   :    str    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop(prediction_type='v_prediction'		,				use_karras_sigmas=A_    )
       lowerCamelCase_							=       torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 0.0649    ) < 1E-3





 def 							a__					(							self   :    Dict    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config(thresholding=A_		,				dynamic_thresholding_ratio=0    )
       lowerCamelCase_							=       scheduler_class(**A_    )

       lowerCamelCase_							=       10
       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter.half()
       scheduler.set_timesteps(A_    )

       for i, t in enumerate(scheduler.timesteps    ):
             lowerCamelCase_							=       model(A_		,				A_    )
             lowerCamelCase_							=       scheduler.step(A_		,				A_		,				A_    ).prev_sample

       assert sample.dtype == torch.floataa









from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      SMALL_MODEL_IDENTIFIER
						__UpperCamelCase					:	List[str]             =      """pt"""
						__UpperCamelCase					:	List[Any]             =      """tf"""






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						Any	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      TFAutoModel.from_pretrained(self.test_model					,     from_pt=lowerCAmelCase	)
						model_tf.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      """mock_framework"""

						# Framework provided - return whatever the user provides
						__UpperCamelCase					:	List[Any]             =      FeaturesManager.determine_framework(self.test_model					,     lowerCAmelCase	)
						self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Optional[Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						__UpperCamelCase					:	Optional[int]             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Both in environment -> use PyTorch
						__UpperCamelCase					:	Dict             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# Both not in environment -> raise error
						__UpperCamelCase					:	Union[str, Any]             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	str             =      FeaturesManager.determine_framework(self.test_model	)






