



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 			__lowerCAmelCase    (			A						):
			UpperCamelCase   					=     (CMStochasticIterativeScheduler,)
			UpperCamelCase   					=     1_0



			def     _lowerCamelCase       (						self   :  int ,		**A   :  int)   ->    List[str]:



						"""simple docstring"""






						_UpperCAmelCase		       =   {
						    'num_train_timesteps': 2_01,
						    'sigma_min': 0.0_0_2,
						    'sigma_max': 8_0.0,
						}

						config.update(**_snake_case)
						return config



			def     _lowerCamelCase       (						self   :  Union[str, Any])   ->    str:



						"""simple docstring"""






						_UpperCAmelCase		       =   10

						_UpperCAmelCase		       =   self.get_scheduler_config()
						_UpperCAmelCase		       =   self.scheduler_classes[0](**_snake_case)

						scheduler.set_timesteps(_snake_case)

						_UpperCAmelCase		       =   scheduler.timesteps[0]
						_UpperCAmelCase		       =   scheduler.timesteps[1]

						_UpperCAmelCase		       =   self.dummy_sample
						_UpperCAmelCase		       =   0.1 * sample

						_UpperCAmelCase		       =   scheduler.step(_snake_case ,		_snake_case ,		_snake_case).prev_sample
						_UpperCAmelCase		       =   scheduler.step(_snake_case ,		_snake_case ,		_snake_case).prev_sample

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



			def     _lowerCamelCase       (						self   :  int)   ->    Union[str, Any]:



						"""simple docstring"""






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



			def     _lowerCamelCase       (						self   :  Optional[int])   ->    Tuple:



						"""simple docstring"""






						for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=_snake_case)



			def     _lowerCamelCase       (						self   :  Dict)   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.scheduler_classes[0]
						_UpperCAmelCase		       =   self.get_scheduler_config()
						_UpperCAmelCase		       =   scheduler_class(**_snake_case)

						_UpperCAmelCase		       =   1
						scheduler.set_timesteps(_snake_case)
						_UpperCAmelCase		       =   scheduler.timesteps

						_UpperCAmelCase		       =   torch.manual_seed(0)

						_UpperCAmelCase		       =   self.dummy_model()
						_UpperCAmelCase		       =   self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(_snake_case):
									# 1. scale model input
									_UpperCAmelCase		       =   scheduler.scale_model_input(_snake_case ,		_snake_case)

									# 2. predict noise residual
									_UpperCAmelCase		       =   model(_snake_case ,		_snake_case)

									# 3. predict previous sample x_t-1
									_UpperCAmelCase		       =   scheduler.step(_snake_case ,		_snake_case ,		_snake_case ,		generator=_snake_case).prev_sample

									_UpperCAmelCase		       =   pred_prev_sample

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

						assert abs(result_sum.item() - 1_9_2.7_6_1_4) < 1E-2
						assert abs(result_mean.item() - 0.2_5_1_0) < 1E-3



			def     _lowerCamelCase       (						self   :  str)   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.scheduler_classes[0]
						_UpperCAmelCase		       =   self.get_scheduler_config()
						_UpperCAmelCase		       =   scheduler_class(**_snake_case)

						_UpperCAmelCase		       =   [1_06, 0]
						scheduler.set_timesteps(timesteps=_snake_case)
						_UpperCAmelCase		       =   scheduler.timesteps

						_UpperCAmelCase		       =   torch.manual_seed(0)

						_UpperCAmelCase		       =   self.dummy_model()
						_UpperCAmelCase		       =   self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
									# 1. scale model input
									_UpperCAmelCase		       =   scheduler.scale_model_input(_snake_case ,		_snake_case)

									# 2. predict noise residual
									_UpperCAmelCase		       =   model(_snake_case ,		_snake_case)

									# 3. predict previous sample x_t-1
									_UpperCAmelCase		       =   scheduler.step(_snake_case ,		_snake_case ,		_snake_case ,		generator=_snake_case).prev_sample

									_UpperCAmelCase		       =   pred_prev_sample

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

						assert abs(result_sum.item() - 3_4_7.6_3_5_7) < 1E-2
						assert abs(result_mean.item() - 0.4_5_2_7) < 1E-3



			def     _lowerCamelCase       (						self   :  Any)   ->    str:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.scheduler_classes[0]
						_UpperCAmelCase		       =   self.get_scheduler_config()
						_UpperCAmelCase		       =   scheduler_class(**_snake_case)

						_UpperCAmelCase		       =   [39, 30, 12, 15, 0]

						with self.assertRaises(_snake_case ,		msg='`timesteps` must be in descending order.'):
									scheduler.set_timesteps(timesteps=_snake_case)



			def     _lowerCamelCase       (						self   :  Optional[int])   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.scheduler_classes[0]
						_UpperCAmelCase		       =   self.get_scheduler_config()
						_UpperCAmelCase		       =   scheduler_class(**_snake_case)

						_UpperCAmelCase		       =   [39, 30, 12, 1, 0]
						_UpperCAmelCase		       =   len(_snake_case)

						with self.assertRaises(_snake_case ,		msg='Can only pass one of `num_inference_steps` or `timesteps`.'):
									scheduler.set_timesteps(num_inference_steps=_snake_case ,		timesteps=_snake_case)



			def     _lowerCamelCase       (						self   :  Optional[Any])   ->    List[str]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.scheduler_classes[0]
						_UpperCAmelCase		       =   self.get_scheduler_config()
						_UpperCAmelCase		       =   scheduler_class(**_snake_case)

						_UpperCAmelCase		       =   [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    _snake_case ,		msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' ,		):
									scheduler.set_timesteps(timesteps=_snake_case)


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,
)


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

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    '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:
					A     :	int    = [
					    '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:
					A     :	Tuple    = [
					    '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

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)