



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase     ):



   '''simple docstring'''






   @property
   def    UpperCamelCase_     (    self							:	Union[str, Any]) -> Optional[Any]:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case			:     List[str] 		=    UNetaDModel(
        block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=3 ,						out_channels=3 ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,						)
    return model


   def    UpperCamelCase_     (    self							:	Tuple) -> List[str]:

    """simple docstring"""







    _snake_case			:     Dict 		=    self.dummy_uncond_unet
    _snake_case			:     str 		=    KarrasVeScheduler()

    _snake_case			:     int 		=    KarrasVePipeline(unet=_UpperCAmelCase ,						scheduler=_UpperCAmelCase)
    pipe.to(_UpperCAmelCase)
    pipe.set_progress_bar_config(disable=_UpperCAmelCase)

    _snake_case			:     Union[str, Any] 		=    torch.manual_seed(0)
    _snake_case			:     int 		=    pipe(num_inference_steps=2 ,						generator=_UpperCAmelCase ,						output_type="""numpy""").images

    _snake_case			:     Tuple 		=    torch.manual_seed(0)
    _snake_case			:     Optional[Any] 		=    pipe(num_inference_steps=2 ,						generator=_UpperCAmelCase ,						output_type="""numpy""" ,						return_dict=_UpperCAmelCase)[0]

    _snake_case			:     int 		=    image[0, -3:, -3:, -1]
    _snake_case			:     List[str] 		=    image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case			:     Tuple 		=    np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







@slow
@require_torch
class 						snake_case  (		unittest.TestCase     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	str) -> Tuple:

    """simple docstring"""







    _snake_case			:     Optional[Any] 		=    'google/ncsnpp-celebahq-256'
    _snake_case			:     Optional[Any] 		=    UNetaDModel.from_pretrained(_UpperCAmelCase)
    _snake_case			:     Optional[Any] 		=    KarrasVeScheduler()

    _snake_case			:     Tuple 		=    KarrasVePipeline(unet=_UpperCAmelCase ,						scheduler=_UpperCAmelCase)
    pipe.to(_UpperCAmelCase)
    pipe.set_progress_bar_config(disable=_UpperCAmelCase)

    _snake_case			:     Optional[int] 		=    torch.manual_seed(0)
    _snake_case			:     Dict 		=    pipe(num_inference_steps=20 ,						generator=_UpperCAmelCase ,						output_type="""numpy""").images

    _snake_case			:     Tuple 		=    image[0, -3:, -3:, -1]
    assert image.shape == (1, 256, 256, 3)
    _snake_case			:     int 		=    np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowercase__    :      int						=    logging.get_logger(__name__)

lowercase__    :      int						=    {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta-prelayernorm'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: List[Any]      =       hidden_size
          __A						: Optional[int]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[str]      =       hidden_act
          __A						: Dict      =       intermediate_size
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: Tuple      =       max_position_embeddings
          __A						: Union[str, Any]      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: str      =       layer_norm_eps
          __A						: int      =       position_embedding_type
          __A						: Optional[Any]      =       use_cache
          __A						: Any      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: Dict      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: Union[str, Any]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])