"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  (DDIMParallelScheduler,)
				_lowerCAmelCase       :      Optional[int]     					=  (("""eta""", 0.0), ("""num_inference_steps""", 50))

				def 						snake_case	(			self						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     {
										    'num_train_timesteps': 10_00,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'clip_sample': True,
										}

										config.update(**lowerCAmelCase      )
										return config

				def 						snake_case	(			self						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.scheduler_classes[0]
										snake_case        =     self.get_scheduler_config(**lowerCAmelCase      )
										snake_case        =     scheduler_class(**lowerCAmelCase      )

										snake_case   ,snake_case        =     10, 0.0

										snake_case        =     self.dummy_model()
										snake_case        =     self.dummy_sample_deter

										scheduler.set_timesteps(lowerCAmelCase      )

										for t in scheduler.timesteps:
																snake_case        =     model(lowerCAmelCase						,      lowerCAmelCase      )
																snake_case        =     scheduler.step(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ).prev_sample

										return sample

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for timesteps in [1_00, 5_00, 10_00]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=lowerCAmelCase      )

										snake_case        =     self.scheduler_classes[0]
										snake_case        =     self.get_scheduler_config(steps_offset=1      )
										snake_case        =     scheduler_class(**lowerCAmelCase      )
										scheduler.set_timesteps(5      )
										assert torch.equal(scheduler.timesteps						,      torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]						,      [0.0_02, 0.02, 0.2, 2]      ):
																self.check_over_configs(beta_start=lowerCAmelCase						,      beta_end=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for timestep_spacing in ["trailing", "leading"]:
																self.check_over_configs(timestep_spacing=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.check_over_configs(thresholding=lowerCAmelCase      )
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=lowerCAmelCase						,      prediction_type=lowerCAmelCase						,      sample_max_value=lowerCAmelCase						,      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for t in [1, 10, 49]:
																self.check_over_forward(time_step=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for t, num_inference_steps in zip([1, 10, 50]						,      [10, 50, 5_00]      ):
																self.check_over_forward(time_step=lowerCAmelCase						,      num_inference_steps=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for t, eta in zip([1, 10, 49]						,      [0.0, 0.5, 1.0]      ):
																self.check_over_forward(time_step=lowerCAmelCase						,      eta=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.scheduler_classes[0]
										snake_case        =     self.get_scheduler_config()
										snake_case        =     scheduler_class(**lowerCAmelCase      )

										assert torch.sum(torch.abs(scheduler._get_variance(0						,      0      ) - 0.0      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_20						,      4_00      ) - 0.1_47_71      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_80						,      9_60      ) - 0.3_24_60      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0						,      0      ) - 0.0      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87						,      4_86      ) - 0.0_09_79      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99						,      9_98      ) - 0.02      )      ) < 1E-5

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.scheduler_classes[0]
										snake_case        =     self.get_scheduler_config()
										snake_case        =     scheduler_class(**lowerCAmelCase      )

										snake_case   ,snake_case        =     10, 0.0
										scheduler.set_timesteps(lowerCAmelCase      )

										snake_case        =     self.dummy_model()
										snake_case        =     self.dummy_sample_deter
										snake_case        =     self.dummy_sample_deter + 0.1
										snake_case        =     self.dummy_sample_deter - 0.1

										snake_case        =     samplea.shape[0]
										snake_case        =     torch.stack([samplea, samplea, samplea]						,      dim=0      )
										snake_case        =     torch.arange(lowerCAmelCase      )[0:3, None].repeat(1						,      lowerCAmelCase      )

										snake_case        =     model(samples.flatten(0						,      1      )						,      timesteps.flatten(0						,      1      )      )
										snake_case        =     scheduler.batch_step_no_noise(lowerCAmelCase						,      timesteps.flatten(0						,      1      )						,      samples.flatten(0						,      1      )						,      lowerCAmelCase      )

										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

										assert abs(result_sum.item() - 11_47.79_04      ) < 1E-2
										assert abs(result_mean.item() - 0.49_82      ) < 1E-3

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop()

										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

										assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
										assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop(prediction_type='v_prediction'      )

										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

										assert abs(result_sum.item() - 52.53_02      ) < 1E-2
										assert abs(result_mean.item() - 0.06_84      ) < 1E-3

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop(set_alpha_to_one=lowerCAmelCase						,      beta_start=0.01      )
										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

										assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
										assert abs(result_mean.item() - 0.19_51      ) < 1E-3

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.full_loop(set_alpha_to_one=lowerCAmelCase						,      beta_start=0.01      )
										snake_case        =     torch.sum(torch.abs(lowerCAmelCase      )      )
										snake_case        =     torch.mean(torch.abs(lowerCAmelCase      )      )

										assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
										assert abs(result_mean.item() - 0.19_41      ) < 1E-3



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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

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


