
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
       lowercase_ : List[str]       =(UniPCMultistepScheduler,)
       lowercase_ : Tuple       =(('''num_inference_steps''', 25),)




       def 				A__   (       self     ,**A__):
            lowercase					=		{
                '''num_train_timesteps''': 1_0_0_0,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
                '''solver_order''': 2,
                '''solver_type''': '''bh2''',
            }

            config.update(**A__)
            return config




       def 				A__   (       self     ,A__=0     ,**A__):
            lowercase					=		dict(self.forward_default_kwargs)
            lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
            lowercase					=		self.dummy_sample
            lowercase					=		0.1 * sample
            lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                 lowercase			,		lowercase					=		sample, sample
                 for t in range(A__     ,time_step + scheduler.config.solver_order + 1):
                      lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
                      lowercase					=		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__=0     ,**A__):
            lowercase					=		dict(self.forward_default_kwargs)
            lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
            lowercase					=		self.dummy_sample
            lowercase					=		0.1 * sample
            lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 lowercase					=		self.get_scheduler_config()
                 lowercase					=		scheduler_class(**A__)
                 scheduler.set_timesteps(A__)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(A__)
                      lowercase					=		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)
                      lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
                 lowercase					=		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__):
            if scheduler is None:
                 lowercase					=		self.scheduler_classes[0]
                 lowercase					=		self.get_scheduler_config(**A__)
                 lowercase					=		scheduler_class(**A__)

            lowercase					=		self.scheduler_classes[0]
            lowercase					=		self.get_scheduler_config(**A__)
            lowercase					=		scheduler_class(**A__)

            lowercase					=		1_0
            lowercase					=		self.dummy_model()
            lowercase					=		self.dummy_sample_deter
            scheduler.set_timesteps(A__)

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

            return sample




       def 				A__   (       self):
            lowercase					=		dict(self.forward_default_kwargs)

            lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

            for scheduler_class in self.scheduler_classes:
                 lowercase					=		self.get_scheduler_config()
                 lowercase					=		scheduler_class(**A__)

                 lowercase					=		self.dummy_sample
                 lowercase					=		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'''):
                      lowercase					=		num_inference_steps

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

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

                 lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
                 lowercase					=		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):
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            lowercase					=		UniPCMultistepScheduler(**self.get_scheduler_config())
            lowercase					=		self.full_loop(scheduler=A__)
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.2464) < 1E-3

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

            lowercase					=		self.full_loop(scheduler=A__)
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.2464) < 1E-3




       def 				A__   (       self):
            for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__)




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




       def 				A__   (       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__     ,)
                           lowercase					=		self.full_loop(
                               solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
                           assert not torch.isnan(A__).any(), "Samples have nan numbers"




       def 				A__   (       self):
            self.check_over_configs(lower_order_final=A__)
            self.check_over_configs(lower_order_final=A__)




       def 				A__   (       self):
            for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=A__     ,time_step=0)




       def 				A__   (       self):
            lowercase					=		self.full_loop()
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.2464) < 1E-3




       def 				A__   (       self):
            lowercase					=		self.full_loop(prediction_type='''v_prediction''')
            lowercase					=		torch.mean(torch.abs(A__))

            assert abs(result_mean.item() - 0.1014) < 1E-3




       def 				A__   (       self):
            lowercase					=		self.scheduler_classes[0]
            lowercase					=		self.get_scheduler_config(thresholding=A__     ,dynamic_thresholding_ratio=0)
            lowercase					=		scheduler_class(**A__)

            lowercase					=		1_0
            lowercase					=		self.dummy_model()
            lowercase					=		self.dummy_sample_deter.half()
            scheduler.set_timesteps(A__)

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

            assert sample.dtype == torch.floataa





       def 				A__   (       self     ,**A__):
            for scheduler_class in self.scheduler_classes:
                 lowercase					=		self.get_scheduler_config(**A__)
                 lowercase					=		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_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




