


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		(DDIMParallelScheduler,)
    __magic_name__        =		(('eta', 0.0), ('num_inference_steps', 50))
    def 		a_      (	self      , **__snake_case   ):
         snake_case					=    {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''clip_sample''': True,
         }

         config.update(**__snake_case   )
         return config
    def 		a_      (	self      , **__snake_case   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config(**__snake_case   )
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case			,  snake_case					=    1_0, 0.0

         snake_case					=    self.dummy_model()
         snake_case					=    self.dummy_sample_deter

         scheduler.set_timesteps(__snake_case   )

         for t in scheduler.timesteps:
              snake_case					=    model(__snake_case      , __snake_case   )
              snake_case					=    scheduler.step(__snake_case      , __snake_case      , __snake_case      , __snake_case   ).prev_sample

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

         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config(steps_offset=1   )
         snake_case					=    scheduler_class(**__snake_case   )
         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 		a_      (	self   ):
         for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]   ):
              self.check_over_configs(beta_start=__snake_case      , beta_end=__snake_case   )
    def 		a_      (	self   ):
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__snake_case   )
    def 		a_      (	self   ):
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__snake_case   )
    def 		a_      (	self   ):
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__snake_case   )
    def 		a_      (	self   ):
         for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=__snake_case   )
    def 		a_      (	self   ):
         for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=__snake_case   )
    def 		a_      (	self   ):
         self.check_over_configs(thresholding=__snake_case   )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__snake_case      , prediction_type=__snake_case      , sample_max_value=__snake_case      , )
    def 		a_      (	self   ):
         for t in [1, 1_0, 4_9]:
              self.check_over_forward(time_step=__snake_case   )
    def 		a_      (	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=__snake_case      , num_inference_steps=__snake_case   )
    def 		a_      (	self   ):
         for t, eta in zip([1, 1_0, 4_9]      , [0.0, 0.5, 1.0]   ):
              self.check_over_forward(time_step=__snake_case      , eta=__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         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   ) - 0.1_4771   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      , 9_6_0   ) - 0.3_2460   )   ) < 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   ) - 0.0_0979   )   ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      , 9_9_8   ) - 0.02   )   ) < 1E-5
    def 		a_      (	self   ):
         snake_case					=    self.scheduler_classes[0]
         snake_case					=    self.get_scheduler_config()
         snake_case					=    scheduler_class(**__snake_case   )

         snake_case			,  snake_case					=    1_0, 0.0
         scheduler.set_timesteps(__snake_case   )

         snake_case					=    self.dummy_model()
         snake_case					=    self.dummy_sample_deter
         snake_case					=    self.dummy_sample_deter + 0.1
         snake_case					=    self.dummy_sample_deter - 0.1

         snake_case					=    samplea.shape[0]
         snake_case					=    torch.stack([samplea, samplea, samplea]      , dim=0   )
         snake_case					=    torch.arange(__snake_case   )[0:3, None].repeat(1      , __snake_case   )

         snake_case					=    model(samples.flatten(0      , 1   )      , timesteps.flatten(0      , 1   )   )
         snake_case					=    scheduler.batch_step_no_noise(__snake_case      , timesteps.flatten(0      , 1   )      , samples.flatten(0      , 1   )      , __snake_case   )

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 1147.7904   ) < 1E-2
         assert abs(result_mean.item() - 0.4982   ) < 1E-3
    def 		a_      (	self   ):
         snake_case					=    self.full_loop()

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 172.0067   ) < 1E-2
         assert abs(result_mean.item() - 0.22_3967   ) < 1E-3
    def 		a_      (	self   ):
         snake_case					=    self.full_loop(prediction_type='''v_prediction'''   )

         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 52.5302   ) < 1E-2
         assert abs(result_mean.item() - 0.0684   ) < 1E-3
    def 		a_      (	self   ):
         # We specify different beta, so that the first alpha is 0.99
         snake_case					=    self.full_loop(set_alpha_to_one=__snake_case      , beta_start=0.01   )
         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 149.8295   ) < 1E-2
         assert abs(result_mean.item() - 0.1951   ) < 1E-3




    def 		a_      (	self   ):
         # We specify different beta, so that the first alpha is 0.99
         snake_case					=    self.full_loop(set_alpha_to_one=__snake_case      , beta_start=0.01   )
         snake_case					=    torch.sum(torch.abs(__snake_case   )   )
         snake_case					=    torch.mean(torch.abs(__snake_case   )   )

         assert abs(result_sum.item() - 149.0784   ) < 1E-2
         assert abs(result_mean.item() - 0.1941   ) < 1E-3








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


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'efficientnet'
    def __init__(	self      , __snake_case = 3      , __snake_case = 6_0_0      , __snake_case = 2.0      , __snake_case = 3.1      , __snake_case = 8      , __snake_case = [3, 3, 5, 3, 5, 5, 3]      , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]      , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]      , __snake_case = []      , __snake_case = [1, 2, 2, 2, 1, 2, 1]      , __snake_case = [1, 2, 2, 3, 3, 4, 1]      , __snake_case = [1, 6, 6, 6, 6, 6, 6]      , __snake_case = 0.25      , __snake_case = "swish"      , __snake_case = 2_5_6_0      , __snake_case = "mean"      , __snake_case = 0.02      , __snake_case = 0.001      , __snake_case = 0.99      , __snake_case = 0.5      , __snake_case = 0.2      , **__snake_case      , ):
         super().__init__(**__snake_case   )

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






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		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-5




