



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    (KDPMaDiscreteScheduler,)
					A__				:		Dict			       =    10



					def     _a							(     self					:       Union[str, Any]				,					**_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    {
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**_snake_case					)
								return config



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=_snake_case					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]				,					[0.0002, 0.002, 0.02]					):
											self.check_over_configs(beta_start=_snake_case				,					beta_end=_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config(prediction_type='v_prediction'					)
								A__       =    scheduler_class(**_snake_case					)

								scheduler.set_timesteps(self.num_inference_steps					)

								A__       =    self.dummy_model()
								A__       =    self.dummy_sample_deter * scheduler.init_noise_sigma
								A__       =    sample.to(_snake_case					)

								for i, t in enumerate(scheduler.timesteps					):
											A__       =    scheduler.scale_model_input(_snake_case				,					_snake_case					)

											A__       =    model(_snake_case				,					_snake_case					)

											A__       =    scheduler.step(_snake_case				,					_snake_case				,					_snake_case					)
											A__       =    output.prev_sample

								A__       =    torch.sum(torch.abs(_snake_case					)					)
								A__       =    torch.mean(torch.abs(_snake_case					)					)

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.69_34E-07					) < 1E-2
											assert abs(result_mean.item() - 6.11_12E-10					) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07					) < 1E-2
											assert abs(result_mean.item() - 0.0002					) < 1E-3



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								if torch_device == "mps":
											return
								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								scheduler.set_timesteps(self.num_inference_steps					)

								A__       =    self.dummy_model()
								A__       =    self.dummy_sample_deter * scheduler.init_noise_sigma
								A__       =    sample.to(_snake_case					)

								for i, t in enumerate(scheduler.timesteps					):
											A__       =    scheduler.scale_model_input(_snake_case				,					_snake_case					)

											A__       =    model(_snake_case				,					_snake_case					)

											A__       =    scheduler.step(_snake_case				,					_snake_case				,					_snake_case					)
											A__       =    output.prev_sample

								A__       =    torch.sum(torch.abs(_snake_case					)					)
								A__       =    torch.mean(torch.abs(_snake_case					)					)

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125					) < 1E-2
											assert abs(result_mean.item() - 0.0266					) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 20.4125					) < 1E-2
											assert abs(result_mean.item() - 0.0266					) < 1E-3



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								if torch_device == "mps":
											return
								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								scheduler.set_timesteps(self.num_inference_steps				,					device=_snake_case					)

								A__       =    self.dummy_model()
								A__       =    self.dummy_sample_deter.to(_snake_case					) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											A__       =    scheduler.scale_model_input(_snake_case				,					_snake_case					)

											A__       =    model(_snake_case				,					_snake_case					)

											A__       =    scheduler.step(_snake_case				,					_snake_case				,					_snake_case					)
											A__       =    output.prev_sample

								A__       =    torch.sum(torch.abs(_snake_case					)					)
								A__       =    torch.mean(torch.abs(_snake_case					)					)

								if str(_snake_case					).startswith('cpu'					):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125					) < 1E-2
											assert abs(result_mean.item() - 0.0266					) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 20.4125					) < 1E-2
											assert abs(result_mean.item() - 0.0266					) < 1E-3









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       =      {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''XLMRobertaTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''XLMRobertaTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMRobertaForCausalLM''',
							    '''XLMRobertaForMaskedLM''',
							    '''XLMRobertaForMultipleChoice''',
							    '''XLMRobertaForQuestionAnswering''',
							    '''XLMRobertaForSequenceClassification''',
							    '''XLMRobertaForTokenClassification''',
							    '''XLMRobertaModel''',
							    '''XLMRobertaPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXLMRobertaForCausalLM''',
							    '''TFXLMRobertaForMaskedLM''',
							    '''TFXLMRobertaForMultipleChoice''',
							    '''TFXLMRobertaForQuestionAnswering''',
							    '''TFXLMRobertaForSequenceClassification''',
							    '''TFXLMRobertaForTokenClassification''',
							    '''TFXLMRobertaModel''',
							    '''TFXLMRobertaPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaxXLMRobertaForMaskedLM''',
							    '''FlaxXLMRobertaForCausalLM''',
							    '''FlaxXLMRobertaForMultipleChoice''',
							    '''FlaxXLMRobertaForQuestionAnswering''',
							    '''FlaxXLMRobertaForSequenceClassification''',
							    '''FlaxXLMRobertaForTokenClassification''',
							    '''FlaxXLMRobertaModel''',
							    '''FlaxXLMRobertaPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_xlm_roberta import (
							    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XLMRobertaConfig,
							    XLMRobertaOnnxConfig,
							)

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta import XLMRobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_roberta import (
														    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMRobertaForCausalLM,
														    XLMRobertaForMaskedLM,
														    XLMRobertaForMultipleChoice,
														    XLMRobertaForQuestionAnswering,
														    XLMRobertaForSequenceClassification,
														    XLMRobertaForTokenClassification,
														    XLMRobertaModel,
														    XLMRobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlm_roberta import (
														    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLMRobertaForCausalLM,
														    TFXLMRobertaForMaskedLM,
														    TFXLMRobertaForMultipleChoice,
														    TFXLMRobertaForQuestionAnswering,
														    TFXLMRobertaForSequenceClassification,
														    TFXLMRobertaForTokenClassification,
														    TFXLMRobertaModel,
														    TFXLMRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xlm_roberta import (
														    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxXLMRobertaForCausalLM,
														    FlaxXLMRobertaForMaskedLM,
														    FlaxXLMRobertaForMultipleChoice,
														    FlaxXLMRobertaForQuestionAnswering,
														    FlaxXLMRobertaForSequenceClassification,
														    FlaxXLMRobertaForTokenClassification,
														    FlaxXLMRobertaModel,
														    FlaxXLMRobertaPreTrainedModel,
														)

else:
							import sys

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




