




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






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				(DDIMScheduler,)
					_lowerCamelCase							=				(("""eta""", 0.0), ("""num_inference_steps""", 50))

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							{
						    'num_train_timesteps': 1_000,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**__lowercase      )
						return config

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




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

						lowerCamelCase_     ,    lowerCamelCase_							:	Dict							     =							10, 0.0

						lowerCamelCase_							:	Optional[int]							     =							self.dummy_model()
						lowerCamelCase_							:	Union[str, Any]							     =							self.dummy_sample_deter

						scheduler.set_timesteps(__lowercase      )

						for t in scheduler.timesteps:
							lowerCamelCase_							:	Any							     =							model(__lowercase				, __lowercase      )
							lowerCamelCase_							:	Optional[Any]							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase				, __lowercase      ).prev_sample

						return sample

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timesteps in [100, 500, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=__lowercase      )

						lowerCamelCase_							:	Optional[int]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	List[str]							     =							self.get_scheduler_config(steps_offset=1      )
						lowerCamelCase_							:	int							     =							scheduler_class(**__lowercase      )
						scheduler.set_timesteps(5      )
						assert torch.equal(scheduler.timesteps				, torch.LongTensor([801, 601, 401, 201, 1]      )      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				, [0.0_02, 0.02, 0.2, 2]      ):
							self.check_over_configs(beta_start=__lowercase				, beta_end=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for schedule in ["linear", "squaredcos_cap_v2"]:
							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 clip_sample in [True, False]:
							self.check_over_configs(clip_sample=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self.check_over_configs(thresholding=__lowercase      )
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=__lowercase				, prediction_type=__lowercase				, sample_max_value=__lowercase				, )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t in [1, 10, 49]:
							self.check_over_forward(time_step=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, num_inference_steps in zip([1, 10, 50]				, [10, 50, 500]      ):
							self.check_over_forward(time_step=__lowercase				, num_inference_steps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, eta in zip([1, 10, 49]				, [0.0, 0.5, 1.0]      ):
							self.check_over_forward(time_step=__lowercase				, eta=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

						assert torch.sum(torch.abs(scheduler._get_variance(0				, 0      ) - 0.0      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420				, 400      ) - 0.1_47_71      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980				, 960      ) - 0.3_24_60      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0				, 0      ) - 0.0      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487				, 486      ) - 0.0_09_79      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999				, 998      ) - 0.02      )      ) < 1E-5

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							self.full_loop()

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

						assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
						assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.full_loop(prediction_type='v_prediction'      )

						lowerCamelCase_							:	Tuple							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	List[str]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 52.53_02      ) < 1E-2
						assert abs(result_mean.item() - 0.06_84      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[Any]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Dict							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
						assert abs(result_mean.item() - 0.19_51      ) < 1E-3





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[int]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Optional[int]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
						assert abs(result_mean.item() - 0.19_41      ) < 1E-3
