code: 
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  __A   :     Optional[int]      =			logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		Tuple						=							"mt5"
      lowerCAmelCase_							:		Optional[Any]						=							["past_key_values"]
  
  
  
  
  
  
      def __init__(	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[int]=250112    ,				UpperCAmelCase_			:    int=512    ,				UpperCAmelCase_			:    int=64    ,				UpperCAmelCase_			:    Dict=1024    ,				UpperCAmelCase_			:    str=8    ,				UpperCAmelCase_			:    Dict=None    ,				UpperCAmelCase_			:    Tuple=6    ,				UpperCAmelCase_			:    List[str]=32    ,				UpperCAmelCase_			:    Optional[int]=128    ,				UpperCAmelCase_			:    str=0.1    ,				UpperCAmelCase_			:    Optional[Any]=1E-6    ,				UpperCAmelCase_			:    Tuple=1.0    ,				UpperCAmelCase_			:    Any="gated-gelu"    ,				UpperCAmelCase_			:    Tuple=True    ,				UpperCAmelCase_			:    str=True    ,				UpperCAmelCase_			:    List[Any]="T5Tokenizer"    ,				UpperCAmelCase_			:    str=False    ,				UpperCAmelCase_			:    Tuple=0    ,				UpperCAmelCase_			:    Dict=1    ,				UpperCAmelCase_			:    List[Any]=0    ,				**UpperCAmelCase_			:    Union[str, Any]    ,				):
          super().__init__(
              is_encoder_decoder=UpperCAmelCase_    ,				tokenizer_class=UpperCAmelCase_    ,				tie_word_embeddings=UpperCAmelCase_    ,				pad_token_id=UpperCAmelCase_    ,				eos_token_id=UpperCAmelCase_    ,				decoder_start_token_id=UpperCAmelCase_    ,				**UpperCAmelCase_    ,				)
          lowerCAmelCase				:			Optional[Any]    			=      vocab_size
          lowerCAmelCase				:			List[Any]    			=      d_model
          lowerCAmelCase				:			Tuple    			=      d_kv
          lowerCAmelCase				:			str    			=      d_ff
          lowerCAmelCase				:			Dict    			=      num_layers
          lowerCAmelCase				:			Any    			=      (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          lowerCAmelCase				:			Any    			=      num_heads
          lowerCAmelCase				:			int    			=      relative_attention_num_buckets
          lowerCAmelCase				:			List[Any]    			=      relative_attention_max_distance
          lowerCAmelCase				:			List[str]    			=      dropout_rate
          lowerCAmelCase				:			List[str]    			=      layer_norm_epsilon
          lowerCAmelCase				:			Tuple    			=      initializer_factor
          lowerCAmelCase				:			Union[str, Any]    			=      feed_forward_proj
          lowerCAmelCase				:			Optional[Any]    			=      use_cache
  
          lowerCAmelCase				:			Tuple    			=      self.feed_forward_proj.split('-'    )
          lowerCAmelCase				:			Dict    			=      act_info[-1]
          lowerCAmelCase				:			Dict    			=      act_info[0] == 'gated'
  
          if len(UpperCAmelCase_    ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_    ) > 2:
              raise ValueError(
                  f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                  'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                  '\'gated-gelu\' or \'relu\''    )
  
          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
              lowerCAmelCase				:			Optional[int]    			=      'gelu_new'
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    List[str]    ):
          return self.d_model
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    List[str]    ):
          return self.num_heads
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Any    ):
          return self.num_layers
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			str    			=      {
              'input_ids': {0: 'batch', 1: 'encoder_sequence'},
              'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
          }
          if self.use_past:
              lowerCAmelCase				:			Optional[Any]    			=      'past_encoder_sequence + sequence'
              lowerCAmelCase				:			List[str]    			=      {0: 'batch'}
              lowerCAmelCase				:			Optional[int]    			=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
              lowerCAmelCase				:			str    			=      {0: 'batch', 1: 'decoder_sequence'}
              lowerCAmelCase				:			int    			=      {0: 'batch', 1: 'decoder_sequence'}
  
          if self.use_past:
              self.fill_with_past_key_values_(UpperCAmelCase_    ,				direction='inputs'    )
  
          return common_inputs
  
  
  
  
  
  
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def 	lowercase__ (	self			:    Optional[int]    ):
          return 13
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    int    ):
          return 5E-4
  
  
  
  
  
  
style_context: 
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		List[str]						=							(KDPMaAncestralDiscreteScheduler,)
      lowerCAmelCase_							:		Dict						=							10
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ,				**UpperCAmelCase_			:    List[Any]    ):
          lowerCAmelCase				:			int    			=      {
              'num_train_timesteps': 1100,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
          }
  
          config.update(**UpperCAmelCase_    )
          return config
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          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=UpperCAmelCase_    ,				beta_end=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			Any    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			Optional[int]    			=      self.get_scheduler_config()
          lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    )
  
          lowerCAmelCase				:			Any    			=      torch.manual_seed(0    )
  
          lowerCAmelCase				:			List[Any]    			=      self.dummy_model()
          lowerCAmelCase				:			List[Any]    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )
  
          for i, t in enumerate(scheduler.timesteps    ):
              lowerCAmelCase				:			str    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Any    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[Any]    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			Union[str, Any]    			=      output.prev_sample
  
          lowerCAmelCase				:			List[Any]    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Any    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 1_38_49.38_77    ) < 1E-2
          assert abs(result_mean.item() - 18.03_31    ) < 5E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			Optional[Any]    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			List[Any]    			=      self.get_scheduler_config(prediction_type='v_prediction'    )
          lowerCAmelCase				:			Any    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    )
  
          lowerCAmelCase				:			Optional[int]    			=      self.dummy_model()
          lowerCAmelCase				:			Any    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCAmelCase				:			Optional[int]    			=      sample.to(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[int]    			=      torch.manual_seed(0    )
  
          for i, t in enumerate(scheduler.timesteps    ):
              lowerCAmelCase				:			Dict    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Optional[int]    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			str    			=      output.prev_sample
  
          lowerCAmelCase				:			str    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Tuple    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 3_28.99_70    ) < 1E-2
          assert abs(result_mean.item() - 0.42_84    ) < 1E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          if torch_device == "mps":
              return
          lowerCAmelCase				:			str    			=      self.scheduler_classes[0]
          lowerCAmelCase				:			Dict    			=      self.get_scheduler_config()
          lowerCAmelCase				:			List[str]    			=      scheduler_class(**UpperCAmelCase_    )
  
          scheduler.set_timesteps(self.num_inference_steps    ,				device=UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      torch.manual_seed(0    )
  
          lowerCAmelCase				:			Tuple    			=      self.dummy_model()
          lowerCAmelCase				:			Optional[Any]    			=      self.dummy_sample_deter.to(UpperCAmelCase_    ) * scheduler.init_noise_sigma
  
          for t in scheduler.timesteps:
              lowerCAmelCase				:			str    			=      scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			List[Any]    			=      model(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			Dict    			=      scheduler.step(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				generator=UpperCAmelCase_    )
              lowerCAmelCase				:			Dict    			=      output.prev_sample
  
          lowerCAmelCase				:			Union[str, Any]    			=      torch.sum(torch.abs(UpperCAmelCase_    )    )
          lowerCAmelCase				:			str    			=      torch.mean(torch.abs(UpperCAmelCase_    )    )
  
          assert abs(result_sum.item() - 1_38_49.38_18    ) < 1E-1
          assert abs(result_mean.item() - 18.03_31    ) < 1E-3
  
  
  
  
  
  