


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      __snake_case				(   _lowerCamelCase):
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			(HeunDiscreteScheduler,)
					SCREAMING_SNAKE_CASE_		:       List[Any]								=			10
					def 	a			(						self       :						Union[str, Any]	,      **_lowerCAmelCase       :						Optional[Any]  )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      {
											    'num_train_timesteps': 1_1_0_0,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_UpperCamelCase  )
											return config
					def 	a			(						self       :						Dict  )							->					Dict:



											'''simple docstring'''





											for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase  )
					def 	a			(						self       :						int  )							->					Tuple:



											'''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=_UpperCamelCase	,      beta_end=_UpperCamelCase  )
					def 	a			(						self       :						Optional[int]  )							->					Optional[int]:



											'''simple docstring'''





											for schedule in ["linear", "scaled_linear", "exp"]:
																	self.check_over_configs(beta_schedule=_UpperCamelCase  )
					def 	a			(						self       :						Optional[int]  )							->					Any:



											'''simple docstring'''





											for clip_sample_range in [1.0, 2.0, 3.0]:
																	self.check_over_configs(clip_sample_range=_UpperCamelCase	,      clip_sample=_UpperCamelCase  )
					def 	a			(						self       :						Optional[Any]  )							->					Union[str, Any]:



											'''simple docstring'''





											for prediction_type in ["epsilon", "v_prediction", "sample"]:
																	self.check_over_configs(prediction_type=_UpperCamelCase  )
					def 	a			(						self       :						Optional[int]  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config()
											__magic_name__					=      scheduler_class(**_UpperCamelCase  )

											scheduler.set_timesteps(self.num_inference_steps  )

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter * scheduler.init_noise_sigma
											__magic_name__					=      sample.to(_UpperCamelCase  )

											for i, t in enumerate(scheduler.timesteps  ):
																	__magic_name__					=      scheduler.scale_model_input(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      model(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      scheduler.step(_UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_UpperCamelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_UpperCamelCase  )  )

											if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 0.1233  ) < 1E-2
																	assert abs(result_mean.item() - 0.0002  ) < 1E-3
											else:
																	# CUDA
																	assert abs(result_sum.item() - 0.1233  ) < 1E-2
																	assert abs(result_mean.item() - 0.0002  ) < 1E-3
					def 	a			(						self       :						Optional[Any]  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config(prediction_type='v_prediction'  )
											__magic_name__					=      scheduler_class(**_UpperCamelCase  )

											scheduler.set_timesteps(self.num_inference_steps  )

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter * scheduler.init_noise_sigma
											__magic_name__					=      sample.to(_UpperCamelCase  )

											for i, t in enumerate(scheduler.timesteps  ):
																	__magic_name__					=      scheduler.scale_model_input(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      model(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      scheduler.step(_UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_UpperCamelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_UpperCamelCase  )  )

											if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 4.6_9_3_4E-0_7  ) < 1E-2
																	assert abs(result_mean.item() - 6.1_1_1_2E-1_0  ) < 1E-3
											else:
																	# CUDA
																	assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7  ) < 1E-2
																	assert abs(result_mean.item() - 0.0002  ) < 1E-3
					def 	a			(						self       :						Optional[int]  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config()
											__magic_name__					=      scheduler_class(**_UpperCamelCase  )

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

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter.to(_UpperCamelCase  ) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																	__magic_name__					=      scheduler.scale_model_input(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      model(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      scheduler.step(_UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_UpperCamelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_UpperCamelCase  )  )

											if str(_UpperCamelCase  ).startswith('cpu'  ):
																	# The following sum varies between 148 and 156 on mps. Why?
																	assert abs(result_sum.item() - 0.1233  ) < 1E-2
																	assert abs(result_mean.item() - 0.0002  ) < 1E-3
											elif str(_UpperCamelCase  ).startswith('mps'  ):
																	# Larger tolerance on mps
																	assert abs(result_mean.item() - 0.0002  ) < 1E-2
											else:
																	# CUDA
																	assert abs(result_sum.item() - 0.1233  ) < 1E-2
																	assert abs(result_mean.item() - 0.0002  ) < 1E-3







					def 	a			(						self       :						str  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.scheduler_classes[0]
											__magic_name__					=      self.get_scheduler_config()
											__magic_name__					=      scheduler_class(**_UpperCamelCase	,      use_karras_sigmas=_UpperCamelCase  )

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

											__magic_name__					=      self.dummy_model()
											__magic_name__					=      self.dummy_sample_deter.to(_UpperCamelCase  ) * scheduler.init_noise_sigma
											__magic_name__					=      sample.to(_UpperCamelCase  )

											for t in scheduler.timesteps:
																	__magic_name__					=      scheduler.scale_model_input(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      model(_UpperCamelCase	,      _UpperCamelCase  )

																	__magic_name__					=      scheduler.step(_UpperCamelCase	,      _UpperCamelCase	,      _UpperCamelCase  )
																	__magic_name__					=      output.prev_sample

											__magic_name__					=      torch.sum(torch.abs(_UpperCamelCase  )  )
											__magic_name__					=      torch.mean(torch.abs(_UpperCamelCase  )  )

											assert abs(result_sum.item() - 0.0_0015  ) < 1E-2
											assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7  ) < 1E-2








from typing import TYPE_CHECKING

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


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





