


'''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


lowercase__					  =		logging.get_logger(__name__)

lowercase__					  =		{
    "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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					'''deberta'''






     def __init__(      self  ,				__UpperCamelCase=50_265  ,				__UpperCamelCase=768  ,				__UpperCamelCase=12  ,				__UpperCamelCase=12  ,				__UpperCamelCase=3_072  ,				__UpperCamelCase="gelu"  ,				__UpperCamelCase=0.1  ,				__UpperCamelCase=0.1  ,				__UpperCamelCase=512  ,				__UpperCamelCase=0  ,				__UpperCamelCase=0.02  ,				__UpperCamelCase=1e-7  ,				__UpperCamelCase=False  ,				__UpperCamelCase=-1  ,				__UpperCamelCase=0  ,				__UpperCamelCase=True  ,				__UpperCamelCase=None  ,				__UpperCamelCase=0  ,				__UpperCamelCase="gelu"  ,				**__UpperCamelCase  ,				)							->     Union[str, Any]:
          super().__init__(**__UpperCamelCase		)

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

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

          _a            =		pos_att_type
          _a            =		vocab_size
          _a            =		layer_norm_eps

          _a            =		kwargs.get("pooler_hidden_size"  ,				__UpperCamelCase		)
          _a            =		pooler_dropout
          _a            =		pooler_hidden_act







class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):






     @property
     def 			a_       (      self		)							->     Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a            =		{0: "batch", 1: "choice", 2: "sequence"}
          else:
               _a            =		{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 			a_       (      self		)							->     int:
          return 12






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase = -1  ,				__UpperCamelCase = -1  ,				__UpperCamelCase = -1  ,				__UpperCamelCase = False  ,				__UpperCamelCase = None  ,				__UpperCamelCase = 3  ,				__UpperCamelCase = 40  ,				__UpperCamelCase = 40  ,				__UpperCamelCase = None  ,				)							->     Mapping[str, Any]:
          _a            =		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 IFImgaImgPipeline
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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

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

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

          return inputs






     def 			a_       (      self		)							->     Any:
          self._test_save_load_optional_components()






     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()  ,				reason="XFormers attention is only available with CUDA and `xformers` installed"  ,				)
     def 			a_       (      self		)							->     Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     List[Any]:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

