

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class    _a (       _lowerCAmelCase							):




      """simple docstring"""

      _lowerCamelCase    :       Tuple							=    (UniPCMultistepScheduler,)
      _lowerCamelCase    :       Any							=    (("""num_inference_steps""", 2_5),)





      def 						__A     (					self				:							Any		,       **UpperCAmelCase				:							Optional[Any]     ):
            A_		   =							{
                """num_train_timesteps""": 1000,
                """beta_start""": 0.0_001,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
                """solver_order""": 2,
                """solver_type""": """bh2""",
            }

            config.update(**UpperCAmelCase     )
            return config





      def 						__A     (					self				:							List[Any]		,       UpperCAmelCase				:							Dict=0		,       **UpperCAmelCase				:							List[str]     ):
            A_		   =							dict(self.forward_default_kwargs     )
            A_		   =							kwargs.pop("num_inference_steps"		,       UpperCAmelCase     )
            A_		   =							self.dummy_sample
            A_		   =							0.1 * sample
            A_		   =							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                  A_		   =							self.get_scheduler_config(**UpperCAmelCase     )
                  A_		   =							scheduler_class(**UpperCAmelCase     )
                  scheduler.set_timesteps(UpperCAmelCase     )
                  # copy over dummy past residuals
                  A_		   =							dummy_past_residuals[: scheduler.config.solver_order]

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

                  A_		   =							sample, sample
                  for t in range(UpperCAmelCase		,       time_step + scheduler.config.solver_order + 1     ):
                        A_		   =							scheduler.step(UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase		,       **UpperCAmelCase     ).prev_sample
                        A_		   =							new_scheduler.step(UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase		,       **UpperCAmelCase     ).prev_sample

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





      def 						__A     (					self				:							Tuple		,       UpperCAmelCase				:							List[Any]=0		,       **UpperCAmelCase				:							Union[str, Any]     ):
            A_		   =							dict(self.forward_default_kwargs     )
            A_		   =							kwargs.pop("num_inference_steps"		,       UpperCAmelCase     )
            A_		   =							self.dummy_sample
            A_		   =							0.1 * sample
            A_		   =							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                  A_		   =							self.get_scheduler_config()
                  A_		   =							scheduler_class(**UpperCAmelCase     )
                  scheduler.set_timesteps(UpperCAmelCase     )

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(UpperCAmelCase     )
                        A_		   =							scheduler_class.from_pretrained(UpperCAmelCase     )
                        # copy over dummy past residuals
                        new_scheduler.set_timesteps(UpperCAmelCase     )

                        # copy over dummy past residual (must be after setting timesteps)
                        A_		   =							dummy_past_residuals[: new_scheduler.config.solver_order]

                  A_		   =							scheduler.step(UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase		,       **UpperCAmelCase     ).prev_sample
                  A_		   =							new_scheduler.step(UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase		,       **UpperCAmelCase     ).prev_sample

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





      def 						__A     (					self				:							Any		,       UpperCAmelCase				:							List[str]=None		,       **UpperCAmelCase				:							int     ):
            if scheduler is None:
                  A_		   =							self.scheduler_classes[0]
                  A_		   =							self.get_scheduler_config(**UpperCAmelCase     )
                  A_		   =							scheduler_class(**UpperCAmelCase     )

            A_		   =							self.scheduler_classes[0]
            A_		   =							self.get_scheduler_config(**UpperCAmelCase     )
            A_		   =							scheduler_class(**UpperCAmelCase     )

            A_		   =							10
            A_		   =							self.dummy_model()
            A_		   =							self.dummy_sample_deter
            scheduler.set_timesteps(UpperCAmelCase     )

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

            return sample





      def 						__A     (					self				:							Optional[Any]     ):
            A_		   =							dict(self.forward_default_kwargs     )

            A_		   =							kwargs.pop("num_inference_steps"		,       UpperCAmelCase     )

            for scheduler_class in self.scheduler_classes:
                  A_		   =							self.get_scheduler_config()
                  A_		   =							scheduler_class(**UpperCAmelCase     )

                  A_		   =							self.dummy_sample
                  A_		   =							0.1 * sample

                  if num_inference_steps is not None and hasattr(UpperCAmelCase		,       "set_timesteps"     ):
                        scheduler.set_timesteps(UpperCAmelCase     )
                  elif num_inference_steps is not None and not hasattr(UpperCAmelCase		,       "set_timesteps"     ):
                        A_		   =							num_inference_steps

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

                  A_		   =							scheduler.timesteps[5]
                  A_		   =							scheduler.timesteps[6]

                  A_		   =							scheduler.step(UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase		,       **UpperCAmelCase     ).prev_sample
                  A_		   =							scheduler.step(UpperCAmelCase		,       UpperCAmelCase		,       UpperCAmelCase		,       **UpperCAmelCase     ).prev_sample

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





      def 						__A     (					self				:							int     ):
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            A_		   =							UniPCMultistepScheduler(**self.get_scheduler_config()     )
            A_		   =							self.full_loop(scheduler=UpperCAmelCase     )
            A_		   =							torch.mean(torch.abs(UpperCAmelCase     )     )

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

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

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

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





      def 						__A     (					self				:							Any     ):
            for timesteps in [25, 50, 100, 999, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase     )





      def 						__A     (					self				:							Tuple     ):
            self.check_over_configs(thresholding=UpperCAmelCase     )
            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=UpperCAmelCase		,       prediction_type=UpperCAmelCase		,       sample_max_value=UpperCAmelCase		,       solver_order=UpperCAmelCase		,       solver_type=UpperCAmelCase		,       )





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





      def 						__A     (					self				:							str     ):
            for solver_type in ["bh1", "bh2"]:
                  for order in [1, 2, 3]:
                        for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=UpperCAmelCase		,       solver_type=UpperCAmelCase		,       prediction_type=UpperCAmelCase		,       )
                              A_		   =							self.full_loop(
                                  solver_order=UpperCAmelCase		,       solver_type=UpperCAmelCase		,       prediction_type=UpperCAmelCase		,       )
                              assert not torch.isnan(UpperCAmelCase     ).any(), "Samples have nan numbers"





      def 						__A     (					self				:							List[Any]     ):
            self.check_over_configs(lower_order_final=UpperCAmelCase     )
            self.check_over_configs(lower_order_final=UpperCAmelCase     )





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





      def 						__A     (					self				:							str     ):
            A_		   =							self.full_loop()
            A_		   =							torch.mean(torch.abs(UpperCAmelCase     )     )

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





      def 						__A     (					self				:							Tuple     ):
            A_		   =							self.full_loop(prediction_type="v_prediction"     )
            A_		   =							torch.mean(torch.abs(UpperCAmelCase     )     )

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





      def 						__A     (					self				:							Any     ):
            A_		   =							self.scheduler_classes[0]
            A_		   =							self.get_scheduler_config(thresholding=UpperCAmelCase		,       dynamic_thresholding_ratio=0     )
            A_		   =							scheduler_class(**UpperCAmelCase     )

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

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

            assert sample.dtype == torch.floataa







      def 						__A     (					self				:							Tuple		,       **UpperCAmelCase				:							List[Any]     ):
            for scheduler_class in self.scheduler_classes:
                  A_		   =							self.get_scheduler_config(**UpperCAmelCase     )
                  A_		   =							scheduler_class(**UpperCAmelCase     )

                  scheduler.set_timesteps(scheduler.config.num_train_timesteps     )
                  assert len(scheduler.timesteps.unique()     ) == scheduler.num_inference_steps




"""simple docstring"""
from typing import TYPE_CHECKING

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


__a      =				{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["MobileBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MobileBertForMaskedLM",
						    "MobileBertForMultipleChoice",
						    "MobileBertForNextSentencePrediction",
						    "MobileBertForPreTraining",
						    "MobileBertForQuestionAnswering",
						    "MobileBertForSequenceClassification",
						    "MobileBertForTokenClassification",
						    "MobileBertLayer",
						    "MobileBertModel",
						    "MobileBertPreTrainedModel",
						    "load_tf_weights_in_mobilebert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMobileBertForMaskedLM",
						    "TFMobileBertForMultipleChoice",
						    "TFMobileBertForNextSentencePrediction",
						    "TFMobileBertForPreTraining",
						    "TFMobileBertForQuestionAnswering",
						    "TFMobileBertForSequenceClassification",
						    "TFMobileBertForTokenClassification",
						    "TFMobileBertMainLayer",
						    "TFMobileBertModel",
						    "TFMobileBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mobilebert import (
						    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileBertConfig,
						    MobileBertOnnxConfig,
						)
						from .tokenization_mobilebert import MobileBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mobilebert_fast import MobileBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilebert import (
												    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileBertForMaskedLM,
												    MobileBertForMultipleChoice,
												    MobileBertForNextSentencePrediction,
												    MobileBertForPreTraining,
												    MobileBertForQuestionAnswering,
												    MobileBertForSequenceClassification,
												    MobileBertForTokenClassification,
												    MobileBertLayer,
												    MobileBertModel,
												    MobileBertPreTrainedModel,
												    load_tf_weights_in_mobilebert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilebert import (
												    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileBertForMaskedLM,
												    TFMobileBertForMultipleChoice,
												    TFMobileBertForNextSentencePrediction,
												    TFMobileBertForPreTraining,
												    TFMobileBertForQuestionAnswering,
												    TFMobileBertForSequenceClassification,
												    TFMobileBertForTokenClassification,
												    TFMobileBertMainLayer,
												    TFMobileBertModel,
												    TFMobileBertPreTrainedModel,
												)

else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
