





"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  (DPMSolverSinglestepScheduler,)
       lowerCAmelCase__						 =  (("""num_inference_steps""", 25),)






       def 							__A (   self    ,  **a__     ):
              _UpperCAmelCase     =     {
                  'num_train_timesteps': 10_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
                  'solver_order': 2,
                  'prediction_type': 'epsilon',
                  'thresholding': False,
                  'sample_max_value': 1.0,
                  'algorithm_type': 'dpmsolver++',
                  'solver_type': 'midpoint',
                  'lambda_min_clipped': -float('inf'     ),
                  'variance_type': None,
              }

              config.update(**a__     )
              return config






       def 							__A (   self    ,  a__=0    ,  **a__     ):
              _UpperCAmelCase     =     dict(self.forward_default_kwargs     )
              _UpperCAmelCase     =     kwargs.pop('num_inference_steps'    ,  a__     )
              _UpperCAmelCase     =     self.dummy_sample
              _UpperCAmelCase     =     0.1 * sample
              _UpperCAmelCase     =     [residual + 0.2, residual + 0.15, residual + 0.10]

              for scheduler_class in self.scheduler_classes:
                     _UpperCAmelCase     =     self.get_scheduler_config(**a__     )
                     _UpperCAmelCase     =     scheduler_class(**a__     )
                     scheduler.set_timesteps(a__     )
                     # copy over dummy past residuals
                     _UpperCAmelCase     =     dummy_past_residuals[: scheduler.config.solver_order]

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

                     _UpperCAmelCase    ,							_UpperCAmelCase     =     sample, sample
                     for t in range(a__    ,  time_step + scheduler.config.solver_order + 1     ):
                            _UpperCAmelCase     =     scheduler.step(a__    ,  a__    ,  a__    ,  **a__     ).prev_sample
                            _UpperCAmelCase     =     new_scheduler.step(a__    ,  a__    ,  a__    ,  **a__     ).prev_sample

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






       def 							__A (   self     ):
              pass






       def 							__A (   self    ,  a__=0    ,  **a__     ):
              _UpperCAmelCase     =     dict(self.forward_default_kwargs     )
              _UpperCAmelCase     =     kwargs.pop('num_inference_steps'    ,  a__     )
              _UpperCAmelCase     =     self.dummy_sample
              _UpperCAmelCase     =     0.1 * sample
              _UpperCAmelCase     =     [residual + 0.2, residual + 0.15, residual + 0.10]

              for scheduler_class in self.scheduler_classes:
                     _UpperCAmelCase     =     self.get_scheduler_config()
                     _UpperCAmelCase     =     scheduler_class(**a__     )
                     scheduler.set_timesteps(a__     )

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

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(a__     )
                            _UpperCAmelCase     =     scheduler_class.from_pretrained(a__     )
                            # copy over dummy past residuals
                            new_scheduler.set_timesteps(a__     )

                            # copy over dummy past residual (must be after setting timesteps)
                            _UpperCAmelCase     =     dummy_past_residuals[: new_scheduler.config.solver_order]

                     _UpperCAmelCase     =     scheduler.step(a__    ,  a__    ,  a__    ,  **a__     ).prev_sample
                     _UpperCAmelCase     =     new_scheduler.step(a__    ,  a__    ,  a__    ,  **a__     ).prev_sample

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






       def 							__A (   self    ,  a__=None    ,  **a__     ):
              if scheduler is None:
                     _UpperCAmelCase     =     self.scheduler_classes[0]
                     _UpperCAmelCase     =     self.get_scheduler_config(**a__     )
                     _UpperCAmelCase     =     scheduler_class(**a__     )

              _UpperCAmelCase     =     self.scheduler_classes[0]
              _UpperCAmelCase     =     self.get_scheduler_config(**a__     )
              _UpperCAmelCase     =     scheduler_class(**a__     )

              _UpperCAmelCase     =     10
              _UpperCAmelCase     =     self.dummy_model()
              _UpperCAmelCase     =     self.dummy_sample_deter
              scheduler.set_timesteps(a__     )

              for i, t in enumerate(scheduler.timesteps     ):
                     _UpperCAmelCase     =     model(a__    ,  a__     )
                     _UpperCAmelCase     =     scheduler.step(a__    ,  a__    ,  a__     ).prev_sample

              return sample






       def 							__A (   self     ):
              _UpperCAmelCase     =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
              _UpperCAmelCase     =     50
              _UpperCAmelCase     =     self.dummy_model()
              _UpperCAmelCase     =     self.dummy_sample_deter
              scheduler.set_timesteps(a__     )

              # make sure that the first t is uneven
              for i, t in enumerate(scheduler.timesteps[3:]     ):
                     _UpperCAmelCase     =     model(a__    ,  a__     )
                     _UpperCAmelCase     =     scheduler.step(a__    ,  a__    ,  a__     ).prev_sample

              _UpperCAmelCase     =     torch.mean(torch.abs(a__     )     )

              assert abs(result_mean.item() - 0.2_574     ) < 1E-3






       def 							__A (   self     ):
              for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                     self.check_over_configs(num_train_timesteps=a__     )






       def 							__A (   self     ):
              # make sure that iterating over schedulers with same config names gives same results
              # for defaults
              _UpperCAmelCase     =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
              _UpperCAmelCase     =     self.full_loop(scheduler=a__     )
              _UpperCAmelCase     =     torch.mean(torch.abs(a__     )     )

              assert abs(result_mean.item() - 0.2_791     ) < 1E-3

              _UpperCAmelCase     =     DEISMultistepScheduler.from_config(scheduler.config     )
              _UpperCAmelCase     =     DPMSolverMultistepScheduler.from_config(scheduler.config     )
              _UpperCAmelCase     =     UniPCMultistepScheduler.from_config(scheduler.config     )
              _UpperCAmelCase     =     DPMSolverSinglestepScheduler.from_config(scheduler.config     )

              _UpperCAmelCase     =     self.full_loop(scheduler=a__     )
              _UpperCAmelCase     =     torch.mean(torch.abs(a__     )     )

              assert abs(result_mean.item() - 0.2_791     ) < 1E-3






       def 							__A (   self     ):
              self.check_over_configs(thresholding=a__     )
              for order in [1, 2, 3]:
                     for solver_type in ["midpoint", "heun"]:
                            for threshold in [0.5, 1.0, 2.0]:
                                   for prediction_type in ["epsilon", "sample"]:
                                          self.check_over_configs(
                                              thresholding=a__    ,  prediction_type=a__    ,  sample_max_value=a__    ,  algorithm_type='dpmsolver++'    ,  solver_order=a__    ,  solver_type=a__    ,  )






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






       def 							__A (   self     ):
              for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                     for solver_type in ["midpoint", "heun"]:
                            for order in [1, 2, 3]:
                                   for prediction_type in ["epsilon", "sample"]:
                                          self.check_over_configs(
                                              solver_order=a__    ,  solver_type=a__    ,  prediction_type=a__    ,  algorithm_type=a__    ,  )
                                          _UpperCAmelCase     =     self.full_loop(
                                              solver_order=a__    ,  solver_type=a__    ,  prediction_type=a__    ,  algorithm_type=a__    ,  )
                                          assert not torch.isnan(a__     ).any(), "Samples have nan numbers"






       def 							__A (   self     ):
              self.check_over_configs(lower_order_final=a__     )
              self.check_over_configs(lower_order_final=a__     )






       def 							__A (   self     ):
              self.check_over_configs(lambda_min_clipped=-float('inf'     )     )
              self.check_over_configs(lambda_min_clipped=-5.1     )






       def 							__A (   self     ):
              self.check_over_configs(variance_type=a__     )
              self.check_over_configs(variance_type='learned_range'     )






       def 							__A (   self     ):
              for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                     self.check_over_forward(num_inference_steps=a__    ,  time_step=0     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.full_loop()
              _UpperCAmelCase     =     torch.mean(torch.abs(a__     )     )

              assert abs(result_mean.item() - 0.2_791     ) < 1E-3






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.full_loop(use_karras_sigmas=a__     )
              _UpperCAmelCase     =     torch.mean(torch.abs(a__     )     )

              assert abs(result_mean.item() - 0.2_248     ) < 1E-3






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.full_loop(prediction_type='v_prediction'     )
              _UpperCAmelCase     =     torch.mean(torch.abs(a__     )     )

              assert abs(result_mean.item() - 0.1_453     ) < 1E-3






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.full_loop(prediction_type='v_prediction'    ,  use_karras_sigmas=a__     )
              _UpperCAmelCase     =     torch.mean(torch.abs(a__     )     )

              assert abs(result_mean.item() - 0.0_649     ) < 1E-3






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.scheduler_classes[0]
              _UpperCAmelCase     =     self.get_scheduler_config(thresholding=a__    ,  dynamic_thresholding_ratio=0     )
              _UpperCAmelCase     =     scheduler_class(**a__     )

              _UpperCAmelCase     =     10
              _UpperCAmelCase     =     self.dummy_model()
              _UpperCAmelCase     =     self.dummy_sample_deter.half()
              scheduler.set_timesteps(a__     )

              for i, t in enumerate(scheduler.timesteps     ):
                     _UpperCAmelCase     =     model(a__    ,  a__     )
                     _UpperCAmelCase     =     scheduler.step(a__    ,  a__    ,  a__     ).prev_sample

              assert sample.dtype == torch.floataa













"""simple docstring"""






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


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''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 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """deberta-v2"""






       def __init__(   self    ,  a__=12_81_00    ,  a__=15_36    ,  a__=24    ,  a__=24    ,  a__=61_44    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=5_12    ,  a__=0    ,  a__=0.02    ,  a__=1E-7    ,  a__=False    ,  a__=-1    ,  a__=0    ,  a__=True    ,  a__=None    ,  a__=0    ,  a__="gelu"    ,  **a__    ,  ):
              super().__init__(**a__     )

              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     type_vocab_size
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     relative_attention
              _UpperCAmelCase     =     max_relative_positions
              _UpperCAmelCase     =     pad_token_id
              _UpperCAmelCase     =     position_biased_input

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

              _UpperCAmelCase     =     pos_att_type
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     layer_norm_eps

              _UpperCAmelCase     =     kwargs.get('pooler_hidden_size'    ,  a__     )
              _UpperCAmelCase     =     pooler_dropout
              _UpperCAmelCase     =     pooler_hidden_act



class 	lowerCAmelCase					(      snake_case							):






       @property
       def 							__A (   self     ):
              if self.task == "multiple-choice":
                     _UpperCAmelCase     =     {0: 'batch', 1: 'choice', 2: 'sequence'}
              else:
                     _UpperCAmelCase     =     {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     ):
              return 12






       def 							__A (   self    ,  a__    ,  a__ = -1    ,  a__ = -1    ,  a__ = -1    ,  a__ = False    ,  a__ = None    ,  a__ = 3    ,  a__ = 40    ,  a__ = 40    ,  a__ = None    ,  ):
              _UpperCAmelCase     =     super().generate_dummy_inputs(preprocessor=a__    ,  framework=a__     )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs






