'''simple docstring'''





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any		=(UnCLIPScheduler,)



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            _A=	{
                'num_train_timesteps': 1000,
                'variance_type': 'fixed_small_log',
                'clip_sample': True,
                'clip_sample_range': 1.0,
                'prediction_type': 'epsilon',
            }

            config.update(**lowerCAmelCase__       )
            return config



     def 			a__  (					self       ):
            for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for variance in ["fixed_small_log", "learned_range"]:
                   self.check_over_configs(variance_type=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for clip_sample_range in [1, 5, 10, 20]:
                   self.check_over_configs(clip_sample_range=lowerCAmelCase__       )



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



     def 			a__  (					self       ):
            for time_step in [0, 500, 999]:
                   for prev_timestep in [None, 5, 100, 250, 500, 750]:
                          if prev_timestep is not None and prev_timestep >= time_step:
                                 continue

                          self.check_over_forward(time_step=lowerCAmelCase__				,      prev_timestep=lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config(variance_type='fixed_small_log'       )
            _A=	scheduler_class(**lowerCAmelCase__       )

            assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000E-10       )       ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0549625       )       ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9994987       )       ) < 1E-5



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config(variance_type='learned_range'       )
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	0.5

            assert scheduler._get_variance(1				,      predicted_variance=lowerCAmelCase__       ) - -10.1712790 < 1E-5
            assert scheduler._get_variance(487				,      predicted_variance=lowerCAmelCase__       ) - -5.7998052 < 1E-5
            assert scheduler._get_variance(999				,      predicted_variance=lowerCAmelCase__       ) - -0.0010011 < 1E-5



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	scheduler.timesteps

            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter
            _A=	torch.manual_seed(0       )

            for i, t in enumerate(lowerCAmelCase__       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

                   # 2. predict previous mean of sample x_t-1
                   _A=	scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

                   _A=	pred_prev_sample

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_sum.item() - 252.2682495       ) < 1E-2
            assert abs(result_mean.item() - 0.3284743       ) < 1E-3



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            scheduler.set_timesteps(25       )

            _A=	scheduler.timesteps

            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter
            _A=	torch.manual_seed(0       )

            for i, t in enumerate(lowerCAmelCase__       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

                   if i + 1 == timesteps.shape[0]:
                          _A=	None
                   else:
                          _A=	timesteps[i + 1]

                   # 2. predict previous mean of sample x_t-1
                   _A=	scheduler.step(
                       lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      prev_timestep=lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

                   _A=	pred_prev_sample

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_sum.item() - 258.2044983       ) < 1E-2
            assert abs(result_mean.item() - 0.3362038       ) < 1E-3



     def 			a__  (					self       ):
            pass





     def 			a__  (					self       ):
            pass
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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''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      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Tuple		="""segformer"""



     def __init__(					self				,      lowerCAmelCase__=3				,      lowerCAmelCase__=4				,      lowerCAmelCase__=[2, 2, 2, 2]				,      lowerCAmelCase__=[8, 4, 2, 1]				,      lowerCAmelCase__=[32, 64, 160, 256]				,      lowerCAmelCase__=[7, 3, 3, 3]				,      lowerCAmelCase__=[4, 2, 2, 2]				,      lowerCAmelCase__=[1, 2, 5, 8]				,      lowerCAmelCase__=[4, 4, 4, 4]				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=1E-6				,      lowerCAmelCase__=256				,      lowerCAmelCase__=255				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            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.'				,      lowerCAmelCase__				,      )

            _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'				,      lowerCAmelCase__       )
            _A=	semantic_loss_ignore_index





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=version.parse("""1.11"""			)



     @property
     def 			a__  (					self       ):
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ]       )



     @property
     def 			a__  (					self       ):
            return 1E-4





     @property
     def 			a__  (					self       ):
            return 12