"""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    lowerCAmelCase_     (    self		:     Tuple    ):
      torch.manual_seed(0    )
      _UpperCAmelCase         =					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    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					self.dummy_uncond_unet
      _UpperCAmelCase         =					KarrasVeScheduler()

      _UpperCAmelCase         =					KarrasVePipeline(unet=A_      , scheduler=A_    )
      pipe.to(A_    )
      pipe.set_progress_bar_config(disable=A_    )

      _UpperCAmelCase         =					torch.manual_seed(0    )
      _UpperCAmelCase         =					pipe(num_inference_steps=2      , generator=A_      , output_type="""numpy"""    ).images

      _UpperCAmelCase         =					torch.manual_seed(0    )
      _UpperCAmelCase         =					pipe(num_inference_steps=2      , generator=A_      , output_type="""numpy"""      , return_dict=A_    )[0]

      _UpperCAmelCase         =					image[0, -3:, -3:, -1]
      _UpperCAmelCase         =					image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _UpperCAmelCase         =					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    lowerCAmelCase_     (    self		:     Any    ):
      _UpperCAmelCase         =					"google/ncsnpp-celebahq-256"
      _UpperCAmelCase         =					UNetaDModel.from_pretrained(A_    )
      _UpperCAmelCase         =					KarrasVeScheduler()

      _UpperCAmelCase         =					KarrasVePipeline(unet=A_      , scheduler=A_    )
      pipe.to(A_    )
      pipe.set_progress_bar_config(disable=A_    )

      _UpperCAmelCase         =					torch.manual_seed(0    )
      _UpperCAmelCase         =					pipe(num_inference_steps=20      , generator=A_      , output_type="""numpy"""    ).images

      _UpperCAmelCase         =					image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      _UpperCAmelCase         =					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







from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase       :     List[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   'roberta'





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




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

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



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task == "multiple-choice":
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]   )
