
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Optional[int]           =			(DDIMParallelScheduler,)
      _UpperCAmelCase				:    Any           =			(("""eta""", 0.0), ("""num_inference_steps""", 50))
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       lowerCamelCase		:	Tuple							=      {
           """num_train_timesteps""": 1_0_0_0,
           """beta_start""": 0.0_001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
           """clip_sample""": True,
       }

       config.update(**__magic_name__      )
       return config
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       lowerCamelCase		:	Union[str, Any]							=      self.scheduler_classes[0]
       lowerCamelCase		:	Dict							=      self.get_scheduler_config(**__magic_name__      )
       lowerCamelCase		:	Union[str, Any]							=      scheduler_class(**__magic_name__      )

       lowerCamelCase			,    lowerCamelCase		:	Dict							=      1_0, 0.0

       lowerCamelCase		:	Any							=      self.dummy_model()
       lowerCamelCase		:	Union[str, Any]							=      self.dummy_sample_deter

       scheduler.set_timesteps(__magic_name__      )

       for t in scheduler.timesteps:
        lowerCamelCase		:	List[Any]							=      model(__magic_name__    ,  __magic_name__      )
        lowerCamelCase		:	List[str]							=      scheduler.step(__magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ).prev_sample

       return sample
      def 						UpperCamelCase__							(      self      ):
       for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=__magic_name__      )

       lowerCamelCase		:	Optional[int]							=      self.scheduler_classes[0]
       lowerCamelCase		:	List[Any]							=      self.get_scheduler_config(steps_offset=1      )
       lowerCamelCase		:	Optional[Any]							=      scheduler_class(**__magic_name__      )
       scheduler.set_timesteps(5      )
       assert torch.equal(scheduler.timesteps    ,  torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]      )      )
      def 						UpperCamelCase__							(      self      ):
       for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,  [0.002, 0.02, 0.2, 2]      ):
        self.check_over_configs(beta_start=__magic_name__    ,  beta_end=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       self.check_over_configs(thresholding=__magic_name__      )
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=__magic_name__    ,  prediction_type=__magic_name__    ,  sample_max_value=__magic_name__    ,  )
      def 						UpperCamelCase__							(      self      ):
       for t in [1, 1_0, 4_9]:
        self.check_over_forward(time_step=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for t, num_inference_steps in zip([1, 1_0, 5_0]    ,  [1_0, 5_0, 5_0_0]      ):
        self.check_over_forward(time_step=__magic_name__    ,  num_inference_steps=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for t, eta in zip([1, 1_0, 4_9]    ,  [0.0, 0.5, 1.0]      ):
        self.check_over_forward(time_step=__magic_name__    ,  eta=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.scheduler_classes[0]
       lowerCamelCase		:	str							=      self.get_scheduler_config()
       lowerCamelCase		:	Optional[int]							=      scheduler_class(**__magic_name__      )

       assert torch.sum(torch.abs(scheduler._get_variance(0    ,  0      ) - 0.0      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_2_0    ,  4_0_0      ) - 0.14_771      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_8_0    ,  9_6_0      ) - 0.32_460      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0    ,  0      ) - 0.0      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7    ,  4_8_6      ) - 0.00_979      )      ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ,  9_9_8      ) - 0.02      )      ) < 1e-5
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[Any]							=      self.scheduler_classes[0]
       lowerCamelCase		:	Optional[int]							=      self.get_scheduler_config()
       lowerCamelCase		:	List[Any]							=      scheduler_class(**__magic_name__      )

       lowerCamelCase			,    lowerCamelCase		:	int							=      1_0, 0.0
       scheduler.set_timesteps(__magic_name__      )

       lowerCamelCase		:	Any							=      self.dummy_model()
       lowerCamelCase		:	Any							=      self.dummy_sample_deter
       lowerCamelCase		:	int							=      self.dummy_sample_deter + 0.1
       lowerCamelCase		:	Dict							=      self.dummy_sample_deter - 0.1

       lowerCamelCase		:	Union[str, Any]							=      samplea.shape[0]
       lowerCamelCase		:	Optional[Any]							=      torch.stack([samplea, samplea, samplea]    ,  dim=0      )
       lowerCamelCase		:	Optional[Any]							=      torch.arange(__magic_name__      )[0:3, None].repeat(1    ,  __magic_name__      )

       lowerCamelCase		:	str							=      model(samples.flatten(0    ,  1      )    ,  timesteps.flatten(0    ,  1      )      )
       lowerCamelCase		:	Union[str, Any]							=      scheduler.batch_step_no_noise(__magic_name__    ,  timesteps.flatten(0    ,  1      )    ,  samples.flatten(0    ,  1      )    ,  __magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      torch.sum(torch.abs(__magic_name__      )      )
       lowerCamelCase		:	List[str]							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_sum.item() - 1_147.7_904      ) < 1e-2
       assert abs(result_mean.item() - 0.4_982      ) < 1e-3
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.full_loop()

       lowerCamelCase		:	Optional[int]							=      torch.sum(torch.abs(__magic_name__      )      )
       lowerCamelCase		:	List[Any]							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_sum.item() - 172.0_067      ) < 1e-2
       assert abs(result_mean.item() - 0.223_967      ) < 1e-3
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      self.full_loop(prediction_type="""v_prediction"""      )

       lowerCamelCase		:	Tuple							=      torch.sum(torch.abs(__magic_name__      )      )
       lowerCamelCase		:	Tuple							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_sum.item() - 52.5_302      ) < 1e-2
       assert abs(result_mean.item() - 0.0_684      ) < 1e-3
      def 						UpperCamelCase__							(      self      ):
       # We specify different beta, so that the first alpha is 0.99
       lowerCamelCase		:	int							=      self.full_loop(set_alpha_to_one=__magic_name__    ,  beta_start=0.01      )
       lowerCamelCase		:	Optional[int]							=      torch.sum(torch.abs(__magic_name__      )      )
       lowerCamelCase		:	Optional[Any]							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_sum.item() - 149.8_295      ) < 1e-2
       assert abs(result_mean.item() - 0.1_951      ) < 1e-3






      def 						UpperCamelCase__							(      self      ):
       # We specify different beta, so that the first alpha is 0.99
       lowerCamelCase		:	Union[str, Any]							=      self.full_loop(set_alpha_to_one=__magic_name__    ,  beta_start=0.01      )
       lowerCamelCase		:	Optional[Any]							=      torch.sum(torch.abs(__magic_name__      )      )
       lowerCamelCase		:	Union[str, Any]							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_sum.item() - 149.0_784      ) < 1e-2
       assert abs(result_mean.item() - 0.1_941      ) < 1e-3






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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """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__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""segformer"""
      def __init__(      self    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=[2, 2, 2, 2]    ,  __magic_name__=[8, 4, 2, 1]    ,  __magic_name__=[3_2, 6_4, 1_6_0, 2_5_6]    ,  __magic_name__=[7, 3, 3, 3]    ,  __magic_name__=[4, 2, 2, 2]    ,  __magic_name__=[1, 2, 5, 8]    ,  __magic_name__=[4, 4, 4, 4]    ,  __magic_name__="gelu"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=0.02    ,  __magic_name__=0.1    ,  __magic_name__=1e-6    ,  __magic_name__=2_5_6    ,  __magic_name__=2_5_5    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       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."""    ,  __magic_name__    ,  )

       lowerCamelCase		:	Optional[Any]							=      num_channels
       lowerCamelCase		:	str							=      num_encoder_blocks
       lowerCamelCase		:	Any							=      depths
       lowerCamelCase		:	List[Any]							=      sr_ratios
       lowerCamelCase		:	int							=      hidden_sizes
       lowerCamelCase		:	Union[str, Any]							=      patch_sizes
       lowerCamelCase		:	Optional[Any]							=      strides
       lowerCamelCase		:	Dict							=      mlp_ratios
       lowerCamelCase		:	str							=      num_attention_heads
       lowerCamelCase		:	Any							=      hidden_act
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Union[str, Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Dict							=      classifier_dropout_prob
       lowerCamelCase		:	Tuple							=      initializer_range
       lowerCamelCase		:	Dict							=      drop_path_rate
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	Any							=      decoder_hidden_size
       lowerCamelCase		:	str							=      kwargs.get("""reshape_last_stage"""    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      semantic_loss_ignore_index





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-4






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




