
from collections import OrderedDict
from typing import Mapping

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


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}





class    __A(					a_							):
					SCREAMING_SNAKE_CASE           =     '''roberta-prelayernorm'''

					def __init__(						self       :  Dict ,       __UpperCamelCase       :  int=5_0_2_6_5 ,       __UpperCamelCase       :  Dict=7_6_8 ,       __UpperCamelCase       :  List[str]=1_2 ,       __UpperCamelCase       :  Any=1_2 ,       __UpperCamelCase       :  Optional[int]=3_0_7_2 ,       __UpperCamelCase       :  Tuple="gelu" ,       __UpperCamelCase       :  Any=0.1 ,       __UpperCamelCase       :  Any=0.1 ,       __UpperCamelCase       :  Union[str, Any]=5_1_2 ,       __UpperCamelCase       :  List[Any]=2 ,       __UpperCamelCase       :  Any=0.02 ,       __UpperCamelCase       :  Tuple=1E-12 ,       __UpperCamelCase       :  Optional[Any]=1 ,       __UpperCamelCase       :  List[Any]=0 ,       __UpperCamelCase       :  Optional[Any]=2 ,       __UpperCamelCase       :  str="absolute" ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  Any=None ,       **__UpperCamelCase       :  Union[str, Any] ,       ):
												super().__init__(pad_token_id=_a ,       bos_token_id=_a ,       eos_token_id=_a ,       **_a )

												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       type_vocab_size
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       layer_norm_eps
												lowerCamelCase_					=       position_embedding_type
												lowerCamelCase_					=       use_cache
												lowerCamelCase_					=       classifier_dropout





class    __A(					a_							):

					@property
					def  lowercase__   (						self       :  str ):
												if self.task == "multiple-choice":
																			lowerCamelCase_					=       {0: "batch", 1: "choice", 2: "sequence"}
												else:
																			lowerCamelCase_					=       {0: "batch", 1: "sequence"}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												    ] )







"""simple docstring"""




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 						a__		( unittest.TestCase		):
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							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      __magic_name__     (     self							):
          lowercase       :							List[str]									=				self.dummy_uncond_unet
          lowercase       :							Dict									=				KarrasVeScheduler()

          lowercase       :							Any									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							int									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"      ,       return_dict=_a							)[0]

          lowercase       :							Any									=				image[0, -3:, -3:, -1]
          lowercase       :							Dict									=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase       :							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 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"google/ncsnpp-celebahq-256"
          lowercase       :							str									=				UNetaDModel.from_pretrained(_a							)
          lowercase       :							Optional[Any]									=				KarrasVeScheduler()

          lowercase       :							Union[str, Any]									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Dict									=				pipe(num_inference_steps=20      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							Optional[Any]									=				image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          lowercase       :							Optional[Any]									=				np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]							)

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


