
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


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

__lowerCAmelCase			:     Dict           =   {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""deberta-v2"""
  def __init__( self			: Union[str, Any]			,							UpperCamelCase__			: int=12_8100			,							UpperCamelCase__			: Union[str, Any]=1536			,							UpperCamelCase__			: Any=24			,							UpperCamelCase__			: int=24			,							UpperCamelCase__			: List[Any]=6144			,							UpperCamelCase__			: Optional[Any]="gelu"			,							UpperCamelCase__			: Any=0.1			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: List[Any]=512			,							UpperCamelCase__			: str=0			,							UpperCamelCase__			: Tuple=0.02			,							UpperCamelCase__			: str=1E-7			,							UpperCamelCase__			: List[str]=False			,							UpperCamelCase__			: str=-1			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: Any=True			,							UpperCamelCase__			: str=None			,							UpperCamelCase__			: Optional[Any]=0			,							UpperCamelCase__			: List[Any]="gelu"			,							**UpperCamelCase__			: Optional[Any]			,							)			->	Optional[Any]:







    """simple docstring"""






    super().__init__(**UpperCamelCase__    )

    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       type_vocab_size
    __magic_name__				     =       initializer_range
    __magic_name__				     =       relative_attention
    __magic_name__				     =       max_relative_positions
    __magic_name__				     =       pad_token_id
    __magic_name__				     =       position_biased_input

    # Backwards compatibility
    if type(UpperCamelCase__    ) == str:
      __magic_name__				     =       [x.strip() for x in pos_att_type.lower().split("""|"""    )]

    __magic_name__				     =       pos_att_type
    __magic_name__				     =       vocab_size
    __magic_name__				     =       layer_norm_eps

    __magic_name__				     =       kwargs.get("""pooler_hidden_size"""			,							UpperCamelCase__    )
    __magic_name__				     =       pooler_dropout
    __magic_name__				     =       pooler_hidden_act







class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  @property
  def 							_lowercase							( self			: Optional[int]    )			->	Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    if self.task == "multiple-choice":
      __magic_name__				     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      __magic_name__				     =       {0: """batch""", 1: """sequence"""}
    if self._config.type_vocab_size > 0:
      return OrderedDict(
          [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]    )
    else:
      return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]    )
  @property
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    return 12





  def 							_lowercase							( self			: str			,							UpperCamelCase__			: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,							UpperCamelCase__			: int = -1			,							UpperCamelCase__			: int = -1			,							UpperCamelCase__			: int = -1			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: Optional["TensorType"] = None			,							UpperCamelCase__			: int = 3			,							UpperCamelCase__			: int = 40			,							UpperCamelCase__			: int = 40			,							UpperCamelCase__			: "PreTrainedTokenizerBase" = None			,							)			->	Mapping[str, Any]:







    """simple docstring"""






    __magic_name__				     =       super().generate_dummy_inputs(preprocessor=UpperCamelCase__			,							framework=UpperCamelCase__    )
    if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
      del dummy_inputs["token_type_ids"]
    return dummy_inputs




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class   __lowercase      (unittest.TestCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					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					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						Any					=					PNDMScheduler()

       __lowerCAmelCase					:						Dict					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						int					=					np.array([1.0, 1.0, 0.0, 1.0, 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   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					'''google/ddpm-cifar10-32'''

       __lowerCAmelCase					:						Union[str, Any]					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						int					=					PNDMScheduler()

       __lowerCAmelCase					:						Any					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)
       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]					)

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


