

"""simple docstring"""



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(KDPMaDiscreteScheduler,)
    UpperCAmelCase						 =					1_0

    def 				UpperCamelCase	(     self: Dict    ,						**UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


         A__										=  {
             """num_train_timesteps""": 11_00,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
         }

         config.update(**UpperCamelCase       )
         return config

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=UpperCamelCase       )

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]    ,						[0.0_002, 0.002, 0.02]       ):
              self.check_over_configs(beta_start=UpperCamelCase    ,						beta_end=UpperCamelCase       )

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  self.scheduler_classes[0]
         A__										=  self.get_scheduler_config(prediction_type="""v_prediction"""       )
         A__										=  scheduler_class(**UpperCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         A__										=  self.dummy_model()
         A__										=  self.dummy_sample_deter * scheduler.init_noise_sigma
         A__										=  sample.to(UpperCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
              A__										=  scheduler.scale_model_input(UpperCamelCase    ,						UpperCamelCase       )

              A__										=  model(UpperCamelCase    ,						UpperCamelCase       )

              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )
              A__										=  output.prev_sample

         A__										=  torch.sum(torch.abs(UpperCamelCase       )       )
         A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

         if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.6_9_3_4e-0_7       ) < 1e-2
              assert abs(result_mean.item() - 6.1_1_1_2e-1_0       ) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7       ) < 1e-2
              assert abs(result_mean.item() - 0.0_002       ) < 1e-3

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         if torch_device == "mps":
              return
         A__										=  self.scheduler_classes[0]
         A__										=  self.get_scheduler_config()
         A__										=  scheduler_class(**UpperCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         A__										=  self.dummy_model()
         A__										=  self.dummy_sample_deter * scheduler.init_noise_sigma
         A__										=  sample.to(UpperCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
              A__										=  scheduler.scale_model_input(UpperCamelCase    ,						UpperCamelCase       )

              A__										=  model(UpperCamelCase    ,						UpperCamelCase       )

              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )
              A__										=  output.prev_sample

         A__										=  torch.sum(torch.abs(UpperCamelCase       )       )
         A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

         if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3





    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         if torch_device == "mps":
              return
         A__										=  self.scheduler_classes[0]
         A__										=  self.get_scheduler_config()
         A__										=  scheduler_class(**UpperCamelCase       )

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

         A__										=  self.dummy_model()
         A__										=  self.dummy_sample_deter.to(UpperCamelCase       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              A__										=  scheduler.scale_model_input(UpperCamelCase    ,						UpperCamelCase       )

              A__										=  model(UpperCamelCase    ,						UpperCamelCase       )

              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )
              A__										=  output.prev_sample

         A__										=  torch.sum(torch.abs(UpperCamelCase       )       )
         A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

         if str(UpperCamelCase       ).startswith("""cpu"""       ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3
         else:
              # CUDA
              assert abs(result_sum.item() - 20.4_125       ) < 1e-2
              assert abs(result_mean.item() - 0.0_266       ) < 1e-3






"""simple docstring"""



import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_					:	Any							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						{
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"segformer"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, Any]=3    ,						UpperCamelCase: int=4    ,						UpperCamelCase: int=[2, 2, 2, 2]    ,						UpperCamelCase: List[str]=[8, 4, 2, 1]    ,						UpperCamelCase: List[Any]=[32, 64, 1_60, 2_56]    ,						UpperCamelCase: Any=[7, 3, 3, 3]    ,						UpperCamelCase: Union[str, Any]=[4, 2, 2, 2]    ,						UpperCamelCase: Tuple=[1, 2, 5, 8]    ,						UpperCamelCase: Optional[int]=[4, 4, 4, 4]    ,						UpperCamelCase: Dict="gelu"    ,						UpperCamelCase: Optional[Any]=0.0    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: List[Any]=0.02    ,						UpperCamelCase: int=0.1    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=2_56    ,						UpperCamelCase: Union[str, Any]=2_55    ,						**UpperCamelCase: Optional[Any]    ,						):






         """simple docstring"""


         super().__init__(**UpperCamelCase       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
                  """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,						UpperCamelCase    ,						)

         A__										=  num_channels
         A__										=  num_encoder_blocks
         A__										=  depths
         A__										=  sr_ratios
         A__										=  hidden_sizes
         A__										=  patch_sizes
         A__										=  strides
         A__										=  mlp_ratios
         A__										=  num_attention_heads
         A__										=  hidden_act
         A__										=  hidden_dropout_prob
         A__										=  attention_probs_dropout_prob
         A__										=  classifier_dropout_prob
         A__										=  initializer_range
         A__										=  drop_path_rate
         A__										=  layer_norm_eps
         A__										=  decoder_hidden_size
         A__										=  kwargs.get("""reshape_last_stage"""    ,						UpperCamelCase       )
         A__										=  semantic_loss_ignore_index



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					version.parse("1.11"				)

    @property
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]       )

    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 1e-4





    @property
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         return 12






