'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class     __UpperCAmelCase       ( __a		):
  __A :				Dict											= (CMStochasticIterativeScheduler,)
  __A :				Union[str, Any]											= 10





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      {
         '''num_train_timesteps''': 201,
         '''sigma_min''': 0.0_02,
         '''sigma_max''': 80.0,
     }

     config.update(**_lowerCamelCase )
     return config





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      10

     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      self.scheduler_classes[0](**_lowerCamelCase )

     scheduler.set_timesteps(_lowerCamelCase )

     lowerCAmelCase_			=      scheduler.timesteps[0]
     lowerCAmelCase_			=      scheduler.timesteps[1]

     lowerCAmelCase_			=      self.dummy_sample
     lowerCAmelCase_			=      0.1 * sample

     lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ).prev_sample
     lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ).prev_sample

     self.assertEqual(output_a.shape    ,     sample.shape )
     self.assertEqual(output_a.shape    ,     output_a.shape )





  def 						UpperCAmelCase_	(	self ):
     for timesteps in [10, 50, 100, 1000]:
        self.check_over_configs(num_train_timesteps=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     for clip_denoised in [True, False]:
        self.check_over_configs(clip_denoised=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      1
     scheduler.set_timesteps(_lowerCamelCase )
     lowerCAmelCase_			=      scheduler.timesteps

     lowerCAmelCase_			=      torch.manual_seed(0 )

     lowerCAmelCase_			=      self.dummy_model()
     lowerCAmelCase_			=      self.dummy_sample_deter * scheduler.init_noise_sigma

     for i, t in enumerate(_lowerCamelCase ):
        # 1. scale model input
        lowerCAmelCase_			=      scheduler.scale_model_input(_lowerCamelCase    ,     _lowerCamelCase )

        # 2. predict noise residual
        lowerCAmelCase_			=      model(_lowerCamelCase    ,     _lowerCamelCase )

        # 3. predict previous sample x_t-1
        lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     generator=_lowerCamelCase ).prev_sample

        lowerCAmelCase_			=      pred_prev_sample

     lowerCAmelCase_			=      torch.sum(torch.abs(_lowerCamelCase ) )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_sum.item() - 1_92.76_14 ) < 1E-2
     assert abs(result_mean.item() - 0.25_10 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [106, 0]
     scheduler.set_timesteps(timesteps=_lowerCamelCase )
     lowerCAmelCase_			=      scheduler.timesteps

     lowerCAmelCase_			=      torch.manual_seed(0 )

     lowerCAmelCase_			=      self.dummy_model()
     lowerCAmelCase_			=      self.dummy_sample_deter * scheduler.init_noise_sigma

     for t in timesteps:
        # 1. scale model input
        lowerCAmelCase_			=      scheduler.scale_model_input(_lowerCamelCase    ,     _lowerCamelCase )

        # 2. predict noise residual
        lowerCAmelCase_			=      model(_lowerCamelCase    ,     _lowerCamelCase )

        # 3. predict previous sample x_t-1
        lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     generator=_lowerCamelCase ).prev_sample

        lowerCAmelCase_			=      pred_prev_sample

     lowerCAmelCase_			=      torch.sum(torch.abs(_lowerCamelCase ) )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_sum.item() - 3_47.63_57 ) < 1E-2
     assert abs(result_mean.item() - 0.45_27 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [39, 30, 12, 15, 0]

     with self.assertRaises(_lowerCamelCase    ,     msg='''`timesteps` must be in descending order.''' ):
        scheduler.set_timesteps(timesteps=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [39, 30, 12, 1, 0]
     lowerCAmelCase_			=      len(_lowerCamelCase )

     with self.assertRaises(_lowerCamelCase    ,     msg='''Can only pass one of `num_inference_steps` or `timesteps`.''' ):
        scheduler.set_timesteps(num_inference_steps=_lowerCamelCase    ,     timesteps=_lowerCamelCase )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [scheduler.config.num_train_timesteps]

     with self.assertRaises(
         _lowerCamelCase    ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,     ):
        scheduler.set_timesteps(timesteps=_lowerCamelCase )






'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


A_     :						Tuple    							=logging.get_logger(__name__)

A_     :						Dict    							={
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}


class     __UpperCAmelCase       ( __a		):
  __A :				Union[str, Any]											= 'efficientnet'





  def __init__(	self    ,     _lowerCamelCase = 3    ,     _lowerCamelCase = 600    ,     _lowerCamelCase = 2.0    ,     _lowerCamelCase = 3.1    ,     _lowerCamelCase = 8    ,     _lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]    ,     _lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]    ,     _lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]    ,     _lowerCamelCase = []    ,     _lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]    ,     _lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]    ,     _lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]    ,     _lowerCamelCase = 0.25    ,     _lowerCamelCase = "swish"    ,     _lowerCamelCase = 2560    ,     _lowerCamelCase = "mean"    ,     _lowerCamelCase = 0.02    ,     _lowerCamelCase = 0.0_01    ,     _lowerCamelCase = 0.99    ,     _lowerCamelCase = 0.5    ,     _lowerCamelCase = 0.2    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )

     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      width_coefficient
     lowerCAmelCase_			=      depth_coefficient
     lowerCAmelCase_			=      depth_divisor
     lowerCAmelCase_			=      kernel_sizes
     lowerCAmelCase_			=      in_channels
     lowerCAmelCase_			=      out_channels
     lowerCAmelCase_			=      depthwise_padding
     lowerCAmelCase_			=      strides
     lowerCAmelCase_			=      num_block_repeats
     lowerCAmelCase_			=      expand_ratios
     lowerCAmelCase_			=      squeeze_expansion_ratio
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dim
     lowerCAmelCase_			=      pooling_type
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      batch_norm_eps
     lowerCAmelCase_			=      batch_norm_momentum
     lowerCAmelCase_			=      dropout_rate
     lowerCAmelCase_			=      drop_connect_rate
     lowerCAmelCase_			=      sum(_lowerCamelCase ) * 4






class     __UpperCAmelCase       ( __a		):
  __A :				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-5





