






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-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_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= '''deberta-v2'''




	def __init__(       self       ,			a=12_81_00       ,			a=15_36       ,			a=24       ,			a=24       ,			a=61_44       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=0       ,			a=0.02       ,			a=1E-7       ,			a=False       ,			a=-1       ,			a=0       ,			a=True       ,			a=None       ,			a=0       ,			a="gelu"       ,			**a       ,			)       ->    Optional[int]:
								super().__init__(**a					)

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

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

								snake_case_				=							pos_att_type
								snake_case_				=							vocab_size
								snake_case_				=							layer_norm_eps

								snake_case_				=							kwargs.get('pooler_hidden_size'       ,			a					)
								snake_case_				=							pooler_dropout
								snake_case_				=							pooler_hidden_act






class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




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






	def 							_UpperCamelCase			(       self       ,			a       ,			a = -1       ,			a = -1       ,			a = -1       ,			a = False       ,			a = None       ,			a = 3       ,			a = 40       ,			a = 40       ,			a = None       ,			)       ->    Mapping[str, Any]:
								snake_case_				=							super().generate_dummy_inputs(preprocessor=a       ,			framework=a					)
								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

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
	import torch

	from transformers import AutoModelForImageClassification

if is_vision_available():
	from transformers import AutoImageProcessor




@require_torch
@require_vision
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								snake_case_				=							AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								model.to(a					)

								from datasets import load_dataset

								snake_case_				=							load_dataset('nielsr/rvlcdip-demo'					)

								snake_case_				=							dataset['train'][0]['image'].convert('RGB'					)

								snake_case_				=							image_processor(a       ,			return_tensors='pt'					).to(a					)

								# forward pass
								with torch.no_grad():
															snake_case_				=							model(**a					)
															snake_case_				=							outputs.logits

								snake_case_				=							torch.Size((1, 16)					)
								self.assertEqual(logits.shape       ,			a					)

								snake_case_				=							torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347]       ,			device=a       ,			dtype=torch.float       ,			)
								self.assertTrue(torch.allclose(logits[0, :3]       ,			a       ,			atol=1E-4					)					)





