




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				(LMSDiscreteScheduler,)
					_lowerCamelCase							=				10

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							{
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__lowercase      )
						return config

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				, [0.00_02, 0.0_02, 0.02]      ):
							self.check_over_configs(beta_start=__lowercase				, beta_end=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t in [0, 500, 800]:
							self.check_over_forward(time_step=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	str							     =							self.get_scheduler_config()
						lowerCamelCase_							:	int							     =							scheduler_class(**__lowercase      )

						scheduler.set_timesteps(self.num_inference_steps      )

						lowerCamelCase_							:	Any							     =							self.dummy_model()
						lowerCamelCase_							:	int							     =							self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(scheduler.timesteps      ):
							lowerCamelCase_							:	int							     =							scheduler.scale_model_input(__lowercase				, __lowercase      )

							lowerCamelCase_							:	Optional[int]							     =							model(__lowercase				, __lowercase      )

							lowerCamelCase_							:	Union[str, Any]							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase      )
							lowerCamelCase_							:	Optional[Any]							     =							output.prev_sample

						lowerCamelCase_							:	str							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Union[str, Any]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 10_06.3_88      ) < 1E-2
						assert abs(result_mean.item() - 1.31      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	Union[str, Any]							     =							self.get_scheduler_config(prediction_type='v_prediction'      )
						lowerCamelCase_							:	int							     =							scheduler_class(**__lowercase      )

						scheduler.set_timesteps(self.num_inference_steps      )

						lowerCamelCase_							:	List[str]							     =							self.dummy_model()
						lowerCamelCase_							:	Union[str, Any]							     =							self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(scheduler.timesteps      ):
							lowerCamelCase_							:	Dict							     =							scheduler.scale_model_input(__lowercase				, __lowercase      )

							lowerCamelCase_							:	List[str]							     =							model(__lowercase				, __lowercase      )

							lowerCamelCase_							:	Tuple							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase      )
							lowerCamelCase_							:	List[str]							     =							output.prev_sample

						lowerCamelCase_							:	str							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	str							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 0.00_17      ) < 1E-2
						assert abs(result_mean.item() - 2.2676E-06      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	Union[str, Any]							     =							self.get_scheduler_config()
						lowerCamelCase_							:	Any							     =							scheduler_class(**__lowercase      )

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

						lowerCamelCase_							:	Tuple							     =							self.dummy_model()
						lowerCamelCase_							:	Union[str, Any]							     =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						lowerCamelCase_							:	Optional[int]							     =							sample.to(__lowercase      )

						for i, t in enumerate(scheduler.timesteps      ):
							lowerCamelCase_							:	List[Any]							     =							scheduler.scale_model_input(__lowercase				, __lowercase      )

							lowerCamelCase_							:	Any							     =							model(__lowercase				, __lowercase      )

							lowerCamelCase_							:	List[str]							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase      )
							lowerCamelCase_							:	List[Any]							     =							output.prev_sample

						lowerCamelCase_							:	Optional[int]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Union[str, Any]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 10_06.3_88      ) < 1E-2
						assert abs(result_mean.item() - 1.31      ) < 1E-3





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	int							     =							self.get_scheduler_config()
						lowerCamelCase_							:	Any							     =							scheduler_class(**__lowercase				, use_karras_sigmas=__lowercase      )

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

						lowerCamelCase_							:	List[str]							     =							self.dummy_model()
						lowerCamelCase_							:	Union[str, Any]							     =							self.dummy_sample_deter.to(__lowercase      ) * scheduler.init_noise_sigma
						lowerCamelCase_							:	int							     =							sample.to(__lowercase      )

						for t in scheduler.timesteps:
							lowerCamelCase_							:	List[str]							     =							scheduler.scale_model_input(__lowercase				, __lowercase      )

							lowerCamelCase_							:	Dict							     =							model(__lowercase				, __lowercase      )

							lowerCamelCase_							:	Any							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase      )
							lowerCamelCase_							:	Union[str, Any]							     =							output.prev_sample

						lowerCamelCase_							:	List[Any]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Union[str, Any]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 38_12.99_27      ) < 2E-2
						assert abs(result_mean.item() - 4.96_48      ) < 1E-3






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
							from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCamelCase__  =		logging.get_logger(__name__)

UpperCamelCase__  =		{
    'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				"""deberta"""

					def __init__(       self				, __lowercase=50_265				, __lowercase=768				, __lowercase=12				, __lowercase=12				, __lowercase=3_072				, __lowercase="gelu"				, __lowercase=0.1				, __lowercase=0.1				, __lowercase=512				, __lowercase=0				, __lowercase=0.02				, __lowercase=1E-7				, __lowercase=False				, __lowercase=-1				, __lowercase=0				, __lowercase=True				, __lowercase=None				, __lowercase=0				, __lowercase="gelu"				, **__lowercase				, ):







						'''simple docstring'''




						super().__init__(**__lowercase      )

						lowerCamelCase_							:	Dict							     =							hidden_size
						lowerCamelCase_							:	Tuple							     =							num_hidden_layers
						lowerCamelCase_							:	Dict							     =							num_attention_heads
						lowerCamelCase_							:	Tuple							     =							intermediate_size
						lowerCamelCase_							:	Tuple							     =							hidden_act
						lowerCamelCase_							:	List[Any]							     =							hidden_dropout_prob
						lowerCamelCase_							:	Dict							     =							attention_probs_dropout_prob
						lowerCamelCase_							:	List[Any]							     =							max_position_embeddings
						lowerCamelCase_							:	List[Any]							     =							type_vocab_size
						lowerCamelCase_							:	Union[str, Any]							     =							initializer_range
						lowerCamelCase_							:	int							     =							relative_attention
						lowerCamelCase_							:	Tuple							     =							max_relative_positions
						lowerCamelCase_							:	Optional[Any]							     =							pad_token_id
						lowerCamelCase_							:	int							     =							position_biased_input

						# Backwards compatibility
						if type(__lowercase      ) == str:
							lowerCamelCase_							:	Tuple							     =							[x.strip() for x in pos_att_type.lower().split('|'      )]

						lowerCamelCase_							:	Any							     =							pos_att_type
						lowerCamelCase_							:	Any							     =							vocab_size
						lowerCamelCase_							:	List[str]							     =							layer_norm_eps

						lowerCamelCase_							:	str							     =							kwargs.get('pooler_hidden_size'				, __lowercase      )
						lowerCamelCase_							:	List[Any]							     =							pooler_dropout
						lowerCamelCase_							:	str							     =							pooler_hidden_act


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						if self.task == "multiple-choice":
							lowerCamelCase_							:	Tuple							     =							{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
							lowerCamelCase_							:	List[str]							     =							{0: 'batch', 1: 'sequence'}
						if self._config.type_vocab_size > 0:
							return OrderedDict(
							    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]      )
						else:
							return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]      )

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						return 12





					def 			lowercase_							(       self				, __lowercase				, __lowercase = -1				, __lowercase = -1				, __lowercase = -1				, __lowercase = False				, __lowercase = None				, __lowercase = 3				, __lowercase = 40				, __lowercase = 40				, __lowercase = None				, ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							super().generate_dummy_inputs(preprocessor=__lowercase				, framework=__lowercase      )
						if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
							del dummy_inputs["token_type_ids"]
						return dummy_inputs
