import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class        lowercase_   (					A__		):
							_lowerCamelCase						=		(UniPCMultistepScheduler,)
							_lowerCamelCase						=		(("num_inference_steps", 25),)



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													_snake_case						:					int    =   {
													    """num_train_timesteps""": 1_000,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													    """solver_order""": 2,
													    """solver_type""": """bh2""",
													}

													config.update(**__A    )
													return config



							def 	UpperCamelCase	(						self				,    lowercase_=0				,    **lowercase_    ):
													_snake_case						:					str    =   dict(self.forward_default_kwargs    )
													_snake_case						:					Any    =   kwargs.pop("num_inference_steps"				,    __A    )
													_snake_case						:					List[str]    =   self.dummy_sample
													_snake_case						:					int    =   0.1 * sample
													_snake_case						:					Optional[int]    =   [residual + 0.2, residual + 0.15, residual + 0.10]

													for scheduler_class in self.scheduler_classes:
																			_snake_case						:					int    =   self.get_scheduler_config(**__A    )
																			_snake_case						:					List[str]    =   scheduler_class(**__A    )
																			scheduler.set_timesteps(__A    )
																			# copy over dummy past residuals
																			_snake_case						:					List[Any]    =   dummy_past_residuals[: scheduler.config.solver_order]

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__A    )
																									_snake_case						:					Optional[int]    =   scheduler_class.from_pretrained(__A    )
																									new_scheduler.set_timesteps(__A    )
																									# copy over dummy past residuals
																									_snake_case						:					int    =   dummy_past_residuals[: new_scheduler.config.solver_order]

																			_snake_case						:					Union[str, Any]    =   sample, sample
																			for t in range(__A				,    time_step + scheduler.config.solver_order + 1    ):
																									_snake_case						:					List[Any]    =   scheduler.step(__A				,    __A				,    __A				,    **__A    ).prev_sample
																									_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 	UpperCamelCase	(						self				,    lowercase_=0				,    **lowercase_    ):
													_snake_case						:					Optional[Any]    =   dict(self.forward_default_kwargs    )
													_snake_case						:					Optional[Any]    =   kwargs.pop("num_inference_steps"				,    __A    )
													_snake_case						:					str    =   self.dummy_sample
													_snake_case						:					Union[str, Any]    =   0.1 * sample
													_snake_case						:					Optional[int]    =   [residual + 0.2, residual + 0.15, residual + 0.10]

													for scheduler_class in self.scheduler_classes:
																			_snake_case						:					Tuple    =   self.get_scheduler_config()
																			_snake_case						:					Any    =   scheduler_class(**__A    )
																			scheduler.set_timesteps(__A    )

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__A    )
																									_snake_case						:					str    =   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)
																									_snake_case						:					Optional[Any]    =   dummy_past_residuals[: new_scheduler.config.solver_order]

																			_snake_case						:					Union[str, Any]    =   scheduler.step(__A				,    __A				,    __A				,    **__A    ).prev_sample
																			_snake_case						:					Optional[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 	UpperCamelCase	(						self				,    lowercase_=None				,    **lowercase_    ):
													if scheduler is None:
																			_snake_case						:					List[str]    =   self.scheduler_classes[0]
																			_snake_case						:					str    =   self.get_scheduler_config(**__A    )
																			_snake_case						:					Optional[Any]    =   scheduler_class(**__A    )

													_snake_case						:					Optional[int]    =   self.scheduler_classes[0]
													_snake_case						:					List[Any]    =   self.get_scheduler_config(**__A    )
													_snake_case						:					List[Any]    =   scheduler_class(**__A    )

													_snake_case						:					Any    =   10
													_snake_case						:					List[str]    =   self.dummy_model()
													_snake_case						:					Optional[Any]    =   self.dummy_sample_deter
													scheduler.set_timesteps(__A    )

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

													return sample



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   dict(self.forward_default_kwargs    )

													_snake_case						:					List[str]    =   kwargs.pop("num_inference_steps"				,    __A    )

													for scheduler_class in self.scheduler_classes:
																			_snake_case						:					Any    =   self.get_scheduler_config()
																			_snake_case						:					str    =   scheduler_class(**__A    )

																			_snake_case						:					Any    =   self.dummy_sample
																			_snake_case						:					Union[str, Any]    =   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"    ):
																									_snake_case						:					List[Any]    =   num_inference_steps

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

																			_snake_case						:					Any    =   scheduler.timesteps[5]
																			_snake_case						:					List[str]    =   scheduler.timesteps[6]

																			_snake_case						:					Any    =   scheduler.step(__A				,    __A				,    __A				,    **__A    ).prev_sample
																			_snake_case						:					int    =   scheduler.step(__A				,    __A				,    __A				,    **__A    ).prev_sample

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



							def 	UpperCamelCase	(						self    ):
													# make sure that iterating over schedulers with same config names gives same results
													# for defaults
													_snake_case						:					List[str]    =   UniPCMultistepScheduler(**self.get_scheduler_config()    )
													_snake_case						:					Union[str, Any]    =   self.full_loop(scheduler=__A    )
													_snake_case						:					int    =   torch.mean(torch.abs(__A    )    )

													assert abs(result_mean.item() - 0.2_464    ) < 1e-3

													_snake_case						:					Dict    =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
													_snake_case						:					List[Any]    =   DEISMultistepScheduler.from_config(scheduler.config    )
													_snake_case						:					Union[str, Any]    =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
													_snake_case						:					List[Any]    =   UniPCMultistepScheduler.from_config(scheduler.config    )

													_snake_case						:					Union[str, Any]    =   self.full_loop(scheduler=__A    )
													_snake_case						:					List[Any]    =   torch.mean(torch.abs(__A    )    )

													assert abs(result_mean.item() - 0.2_464    ) < 1e-3



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



							def 	UpperCamelCase	(						self    ):
													self.check_over_configs(thresholding=__A    )
													for order in [1, 2, 3]:
																			for solver_type in ["bh1", "bh2"]:
																									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				,    solver_order=__A				,    solver_type=__A				,    )



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



							def 	UpperCamelCase	(						self    ):
													for solver_type in ["bh1", "bh2"]:
																			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				,    )
																															_snake_case						:					Dict    =   self.full_loop(
																															    solver_order=__A				,    solver_type=__A				,    prediction_type=__A				,    )
																															assert not torch.isnan(__A    ).any(), "Samples have nan numbers"



							def 	UpperCamelCase	(						self    ):
													self.check_over_configs(lower_order_final=__A    )
													self.check_over_configs(lower_order_final=__A    )



							def 	UpperCamelCase	(						self    ):
													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 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.full_loop()
													_snake_case						:					Any    =   torch.mean(torch.abs(__A    )    )

													assert abs(result_mean.item() - 0.2_464    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.full_loop(prediction_type="v_prediction"    )
													_snake_case						:					str    =   torch.mean(torch.abs(__A    )    )

													assert abs(result_mean.item() - 0.1_014    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.scheduler_classes[0]
													_snake_case						:					List[str]    =   self.get_scheduler_config(thresholding=__A				,    dynamic_thresholding_ratio=0    )
													_snake_case						:					int    =   scheduler_class(**__A    )

													_snake_case						:					List[Any]    =   10
													_snake_case						:					Tuple    =   self.dummy_model()
													_snake_case						:					Dict    =   self.dummy_sample_deter.half()
													scheduler.set_timesteps(__A    )

													for i, t in enumerate(scheduler.timesteps    ):
																			_snake_case						:					List[Any]    =   model(__A				,    __A    )
																			_snake_case						:					Tuple    =   scheduler.step(__A				,    __A				,    __A    ).prev_sample

													assert sample.dtype == torch.floataa



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													for scheduler_class in self.scheduler_classes:
																			_snake_case						:					Union[str, Any]    =   self.get_scheduler_config(**__A    )
																			_snake_case						:					int    =   scheduler_class(**__A    )

																			scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
																			assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			['DistilBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   int         =			[
				    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DistilBertForMaskedLM',
				    'DistilBertForMultipleChoice',
				    'DistilBertForQuestionAnswering',
				    'DistilBertForSequenceClassification',
				    'DistilBertForTokenClassification',
				    'DistilBertModel',
				    'DistilBertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Any         =			[
				    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDistilBertForMaskedLM',
				    'TFDistilBertForMultipleChoice',
				    'TFDistilBertForQuestionAnswering',
				    'TFDistilBertForSequenceClassification',
				    'TFDistilBertForTokenClassification',
				    'TFDistilBertMainLayer',
				    'TFDistilBertModel',
				    'TFDistilBertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'FlaxDistilBertForMaskedLM',
				    'FlaxDistilBertForMultipleChoice',
				    'FlaxDistilBertForQuestionAnswering',
				    'FlaxDistilBertForSequenceClassification',
				    'FlaxDistilBertForTokenClassification',
				    'FlaxDistilBertModel',
				    'FlaxDistilBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_distilbert import (
				    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DistilBertConfig,
				    DistilBertOnnxConfig,
				)
				from .tokenization_distilbert import DistilBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_distilbert_fast import DistilBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_distilbert import (
								    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DistilBertForMaskedLM,
								    DistilBertForMultipleChoice,
								    DistilBertForQuestionAnswering,
								    DistilBertForSequenceClassification,
								    DistilBertForTokenClassification,
								    DistilBertModel,
								    DistilBertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_distilbert import (
								    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDistilBertForMaskedLM,
								    TFDistilBertForMultipleChoice,
								    TFDistilBertForQuestionAnswering,
								    TFDistilBertForSequenceClassification,
								    TFDistilBertForTokenClassification,
								    TFDistilBertMainLayer,
								    TFDistilBertModel,
								    TFDistilBertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_distilbert import (
								    FlaxDistilBertForMaskedLM,
								    FlaxDistilBertForMultipleChoice,
								    FlaxDistilBertForQuestionAnswering,
								    FlaxDistilBertForSequenceClassification,
								    FlaxDistilBertForTokenClassification,
								    FlaxDistilBertModel,
								    FlaxDistilBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)