
"""simple docstring"""

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


UpperCamelCase      :							Optional[int]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"deberta"





     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=0      ,	__UpperCAmelCase=0.0_2      ,	__UpperCAmelCase=1E-7      ,	__UpperCAmelCase=False      ,	__UpperCAmelCase=-1      ,	__UpperCAmelCase=0      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=0      ,	__UpperCAmelCase="gelu"      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase )

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

      # Backwards compatibility
      if type(__UpperCAmelCase ) == str:
       __UpperCamelCase							    =			[x.strip() for x in pos_att_type.lower().split('|' )]

      __UpperCamelCase							    =			pos_att_type
      __UpperCamelCase							    =			vocab_size
      __UpperCamelCase							    =			layer_norm_eps

      __UpperCamelCase							    =			kwargs.get('pooler_hidden_size'      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			pooler_dropout
      __UpperCamelCase							    =			pooler_hidden_act


class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):





     @property
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      if self.task == "multiple-choice":
       __UpperCamelCase							    =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       __UpperCamelCase							    =			{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 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return 12







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = -1      ,	__UpperCAmelCase = -1      ,	__UpperCAmelCase = -1      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = 3      ,	__UpperCAmelCase = 40      ,	__UpperCAmelCase = 40      ,	__UpperCAmelCase = None      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			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





"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,	)



