import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class       lowercase__		(     UpperCamelCase_):
      UpperCamelCase_  =	(UniPCMultistepScheduler,)
      UpperCamelCase_  =	(("""num_inference_steps""", 25),)


      def  __A ( self			:       Any					,	**UpperCamelCase__			:       Tuple		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Any           =							{
                '''num_train_timesteps''': 1000,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
                '''solver_order''': 2,
                '''solver_type''': '''bh2''',
            }

            config.update(**UpperCamelCase__		)
            return config


      def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any=0					,	**UpperCamelCase__			:       Any		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(self.forward_default_kwargs		)
            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							kwargs.pop('''num_inference_steps'''					,	UpperCamelCase__		)
            SCREAMING_SNAKE_CASE							:					Any           =							self.dummy_sample
            SCREAMING_SNAKE_CASE							:					Any           =							0.1 * sample
            SCREAMING_SNAKE_CASE							:					str           =							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                  SCREAMING_SNAKE_CASE							:					str           =							self.get_scheduler_config(**UpperCamelCase__		)
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler_class(**UpperCamelCase__		)
                  scheduler.set_timesteps(UpperCamelCase__		)
                  # copy over dummy past residuals
                  SCREAMING_SNAKE_CASE							:					int           =							dummy_past_residuals[: scheduler.config.solver_order]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(UpperCamelCase__		)
                        SCREAMING_SNAKE_CASE							:					Optional[int]           =							scheduler_class.from_pretrained(UpperCamelCase__		)
                        new_scheduler.set_timesteps(UpperCamelCase__		)
                        # copy over dummy past residuals
                        SCREAMING_SNAKE_CASE							:					Optional[int]           =							dummy_past_residuals[: new_scheduler.config.solver_order]

                  SCREAMING_SNAKE_CASE							:					Optional[Any]           =							sample, sample
                  for t in range(UpperCamelCase__					,	time_step + scheduler.config.solver_order + 1		):
                        SCREAMING_SNAKE_CASE							:					List[str]           =							scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__		).prev_sample
                        SCREAMING_SNAKE_CASE							:					Any           =							new_scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__		).prev_sample

                        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


      def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]=0					,	**UpperCamelCase__			:       Optional[int]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Any           =							dict(self.forward_default_kwargs		)
            SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.pop('''num_inference_steps'''					,	UpperCamelCase__		)
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.dummy_sample
            SCREAMING_SNAKE_CASE							:					str           =							0.1 * sample
            SCREAMING_SNAKE_CASE							:					Dict           =							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_scheduler_config()
                  SCREAMING_SNAKE_CASE							:					int           =							scheduler_class(**UpperCamelCase__		)
                  scheduler.set_timesteps(UpperCamelCase__		)

                  # copy over dummy past residuals (must be after setting timesteps)
                  SCREAMING_SNAKE_CASE							:					Dict           =							dummy_past_residuals[: scheduler.config.solver_order]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(UpperCamelCase__		)
                        SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler_class.from_pretrained(UpperCamelCase__		)
                        # copy over dummy past residuals
                        new_scheduler.set_timesteps(UpperCamelCase__		)

                        # copy over dummy past residual (must be after setting timesteps)
                        SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							dummy_past_residuals[: new_scheduler.config.solver_order]

                  SCREAMING_SNAKE_CASE							:					Optional[int]           =							scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__		).prev_sample
                  SCREAMING_SNAKE_CASE							:					str           =							new_scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


      def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]=None					,	**UpperCamelCase__			:       Optional[int]		):





            '''simple docstring'''





            if scheduler is None:
                  SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.scheduler_classes[0]
                  SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.get_scheduler_config(**UpperCamelCase__		)
                  SCREAMING_SNAKE_CASE							:					str           =							scheduler_class(**UpperCamelCase__		)

            SCREAMING_SNAKE_CASE							:					int           =							self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE							:					int           =							self.get_scheduler_config(**UpperCamelCase__		)
            SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler_class(**UpperCamelCase__		)

            SCREAMING_SNAKE_CASE							:					Any           =							10
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.dummy_model()
            SCREAMING_SNAKE_CASE							:					int           =							self.dummy_sample_deter
            scheduler.set_timesteps(UpperCamelCase__		)

            for i, t in enumerate(scheduler.timesteps		):
                  SCREAMING_SNAKE_CASE							:					Optional[int]           =							model(UpperCamelCase__					,	UpperCamelCase__		)
                  SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		).prev_sample

            return sample


      def  __A ( self			:       str		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							dict(self.forward_default_kwargs		)

            SCREAMING_SNAKE_CASE							:					List[str]           =							kwargs.pop('''num_inference_steps'''					,	UpperCamelCase__		)

            for scheduler_class in self.scheduler_classes:
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_scheduler_config()
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler_class(**UpperCamelCase__		)

                  SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.dummy_sample
                  SCREAMING_SNAKE_CASE							:					int           =							0.1 * sample

                  if num_inference_steps is not None and hasattr(UpperCamelCase__					,	'''set_timesteps'''		):
                        scheduler.set_timesteps(UpperCamelCase__		)
                  elif num_inference_steps is not None and not hasattr(UpperCamelCase__					,	'''set_timesteps'''		):
                        SCREAMING_SNAKE_CASE							:					List[str]           =							num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  SCREAMING_SNAKE_CASE							:					Any           =							[residual + 0.2, residual + 0.15, residual + 0.10]
                  SCREAMING_SNAKE_CASE							:					int           =							dummy_past_residuals[: scheduler.config.solver_order]

                  SCREAMING_SNAKE_CASE							:					List[str]           =							scheduler.timesteps[5]
                  SCREAMING_SNAKE_CASE							:					Dict           =							scheduler.timesteps[6]

                  SCREAMING_SNAKE_CASE							:					Optional[Any]           =							scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__		).prev_sample
                  SCREAMING_SNAKE_CASE							:					List[str]           =							scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__		).prev_sample

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


      def  __A ( self			:       int		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							UniPCMultistepScheduler(**self.get_scheduler_config()		)
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.full_loop(scheduler=UpperCamelCase__		)
            SCREAMING_SNAKE_CASE							:					Any           =							torch.mean(torch.abs(UpperCamelCase__		)		)

            assert abs(result_mean.item() - 0.2464		) < 1E-3

            SCREAMING_SNAKE_CASE							:					Dict           =							DPMSolverSinglestepScheduler.from_config(scheduler.config		)
            SCREAMING_SNAKE_CASE							:					Any           =							DEISMultistepScheduler.from_config(scheduler.config		)
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							DPMSolverMultistepScheduler.from_config(scheduler.config		)
            SCREAMING_SNAKE_CASE							:					Any           =							UniPCMultistepScheduler.from_config(scheduler.config		)

            SCREAMING_SNAKE_CASE							:					str           =							self.full_loop(scheduler=UpperCamelCase__		)
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.mean(torch.abs(UpperCamelCase__		)		)

            assert abs(result_mean.item() - 0.2464		) < 1E-3


      def  __A ( self			:       List[str]		):





            '''simple docstring'''





            for timesteps in [25, 50, 100, 999, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__		)


      def  __A ( self			:       Any		):





            '''simple docstring'''





            self.check_over_configs(thresholding=UpperCamelCase__		)
            for order in [1, 2, 3]:
                  for solver_type in ["bh1", "bh2"]:
                        for threshold in [0.5, 1.0, 2.0]:
                              for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=UpperCamelCase__					,	prediction_type=UpperCamelCase__					,	sample_max_value=UpperCamelCase__					,	solver_order=UpperCamelCase__					,	solver_type=UpperCamelCase__					,	)


      def  __A ( self			:       Optional[Any]		):





            '''simple docstring'''





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


      def  __A ( self			:       Dict		):





            '''simple docstring'''





            for solver_type in ["bh1", "bh2"]:
                  for order in [1, 2, 3]:
                        for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=UpperCamelCase__					,	solver_type=UpperCamelCase__					,	prediction_type=UpperCamelCase__					,	)
                              SCREAMING_SNAKE_CASE							:					str           =							self.full_loop(
                                  solver_order=UpperCamelCase__					,	solver_type=UpperCamelCase__					,	prediction_type=UpperCamelCase__					,	)
                              assert not torch.isnan(UpperCamelCase__		).any(), "Samples have nan numbers"


      def  __A ( self			:       List[Any]		):





            '''simple docstring'''





            self.check_over_configs(lower_order_final=UpperCamelCase__		)
            self.check_over_configs(lower_order_final=UpperCamelCase__		)


      def  __A ( self			:       Optional[Any]		):





            '''simple docstring'''





            for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                  self.check_over_forward(num_inference_steps=UpperCamelCase__					,	time_step=0		)


      def  __A ( self			:       Union[str, Any]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Dict           =							self.full_loop()
            SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.mean(torch.abs(UpperCamelCase__		)		)

            assert abs(result_mean.item() - 0.2464		) < 1E-3


      def  __A ( self			:       Dict		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Dict           =							self.full_loop(prediction_type='''v_prediction'''		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							torch.mean(torch.abs(UpperCamelCase__		)		)

            assert abs(result_mean.item() - 0.1014		) < 1E-3


      def  __A ( self			:       Tuple		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					List[Any]           =							self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.get_scheduler_config(thresholding=UpperCamelCase__					,	dynamic_thresholding_ratio=0		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							scheduler_class(**UpperCamelCase__		)

            SCREAMING_SNAKE_CASE							:					List[str]           =							10
            SCREAMING_SNAKE_CASE							:					List[str]           =							self.dummy_model()
            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.dummy_sample_deter.half()
            scheduler.set_timesteps(UpperCamelCase__		)

            for i, t in enumerate(scheduler.timesteps		):
                  SCREAMING_SNAKE_CASE							:					List[str]           =							model(UpperCamelCase__					,	UpperCamelCase__		)
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		).prev_sample

            assert sample.dtype == torch.floataa



      def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





            '''simple docstring'''





            for scheduler_class in self.scheduler_classes:
                  SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_scheduler_config(**UpperCamelCase__		)
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler_class(**UpperCamelCase__		)

                  scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
                  assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps






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	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

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

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{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  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





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





