from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase					:   Optional[int]							=		{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Optional[int]							=		['''LayoutLMv2TokenizerFast''']

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Optional[int]							=		['''LayoutLMv2FeatureExtractor''']
							_lowerCamelCase					:   Tuple							=		['''LayoutLMv2ImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   int							=		[
							    '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LayoutLMv2ForQuestionAnswering''',
							    '''LayoutLMv2ForSequenceClassification''',
							    '''LayoutLMv2ForTokenClassification''',
							    '''LayoutLMv2Layer''',
							    '''LayoutLMv2Model''',
							    '''LayoutLMv2PreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
							from .processing_layoutlmva import LayoutLMvaProcessor
							from .tokenization_layoutlmva import LayoutLMvaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlmva import (
														    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMvaForQuestionAnswering,
														    LayoutLMvaForSequenceClassification,
														    LayoutLMvaForTokenClassification,
														    LayoutLMvaLayer,
														    LayoutLMvaModel,
														    LayoutLMvaPreTrainedModel,
														)
else:
							import sys

							_lowerCamelCase					:   Optional[int]							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							(KDPMaDiscreteScheduler,)
							lowercase__						:       Optional[int]								=							10




							def 	__snake_case(						self   :					Optional[Any]	,							**_UpperCamelCase   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "num_train_timesteps": 1_100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**_UpperCamelCase      )
												return config




							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase      )




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



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																	self.check_over_configs(beta_start=_UpperCamelCase	,							beta_end=_UpperCamelCase      )




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



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Optional[int]      )					->				int:



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config(prediction_type="v_prediction"      )
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

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

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

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

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

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

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 4.6_934e-07      ) < 1e-2
																	assert abs(result_mean.item() - 6.1_112e-10      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 4.693_428_650_170_972e-07      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

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

												scheduler.set_timesteps(self.num_inference_steps      )

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

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

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

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

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

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3





							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

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

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

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

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

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       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() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




