




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__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{
    '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    ):
					_lowerCamelCase						    =					"""deberta"""

					def __init__(							self					,			__lowercase=50_265					,			__lowercase=768					,			__lowercase=12					,			__lowercase=12					,			__lowercase=3_072					,			__lowercase="gelu"					,			__lowercase=0.1					,			__lowercase=0.1					,			__lowercase=512					,			__lowercase=0					,			__lowercase=0.02					,			__lowercase=1E-7					,			__lowercase=False					,			__lowercase=-1					,			__lowercase=0					,			__lowercase=True					,			__lowercase=None					,			__lowercase=0					,			__lowercase="gelu"					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__(**__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				hidden_size
						lowerCamelCase_				:						List[Any]				  =				num_hidden_layers
						lowerCamelCase_				:						Dict				  =				num_attention_heads
						lowerCamelCase_				:						List[Any]				  =				intermediate_size
						lowerCamelCase_				:						str				  =				hidden_act
						lowerCamelCase_				:						Dict				  =				hidden_dropout_prob
						lowerCamelCase_				:						str				  =				attention_probs_dropout_prob
						lowerCamelCase_				:						Union[str, Any]				  =				max_position_embeddings
						lowerCamelCase_				:						List[Any]				  =				type_vocab_size
						lowerCamelCase_				:						Optional[Any]				  =				initializer_range
						lowerCamelCase_				:						Optional[Any]				  =				relative_attention
						lowerCamelCase_				:						Any				  =				max_relative_positions
						lowerCamelCase_				:						Tuple				  =				pad_token_id
						lowerCamelCase_				:						Optional[Any]				  =				position_biased_input

						# Backwards compatibility
						if type(__lowercase   ) == str:
							lowerCamelCase_				:						Optional[Any]				  =				[x.strip() for x in pos_att_type.lower().split('|'   )]

						lowerCamelCase_				:						List[str]				  =				pos_att_type
						lowerCamelCase_				:						int				  =				vocab_size
						lowerCamelCase_				:						Optional[Any]				  =				layer_norm_eps

						lowerCamelCase_				:						str				  =				kwargs.get('pooler_hidden_size'					,			__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				pooler_dropout
						lowerCamelCase_				:						Optional[int]				  =				pooler_hidden_act


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					@property
					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self.task == "multiple-choice":
							lowerCamelCase_				:						Tuple				  =				{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
							lowerCamelCase_				:						Dict				  =				{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   ):







						'''simple docstring'''




						return 12





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = False					,			__lowercase = None					,			__lowercase = 3					,			__lowercase = 40					,			__lowercase = 40					,			__lowercase = None					,			):







						'''simple docstring'''




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








import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{
          'model_details': {
              'Organization': 'testing',
              'Model date': 'today',
              'Model version': 'v2.1, Developed by Test Corp in 2019.',
              'Architecture': 'Convolutional Neural Network.',
          },
          'metrics': 'BLEU and ROUGE-1',
          'evaluation_data': {
              'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
              'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
          },
          'training_data': {
              'Dataset': 'English Wikipedia dump dated 2018-12-01',
              'Preprocessing': (
                  'Using SentencePiece vocabulary of size 52k tokens. See details on'
                  ' https://arxiv.org/pdf/1810.03993.pdf'
              ),
          },
          'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 7_6},
      }





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'model_details'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'intended_use'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'factors'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'metrics'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'evaluation_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'training_data'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'quantitative_analyses'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'ethical_considerations'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'caveats_and_recommendations'))





     def     __UpperCamelCase						(		self					:	Any)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)
      SCREAMING_SNAKE_CASE				    =		json.loads(modelcard.to_json_string())
      for key, value in self.inputs_dict.items():
       self.assertEqual(obj[key]  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	int)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE				    =		os.path.join(lowerCAmelCase__  ,  'modelcard.json')
       model_card_first.to_json_file(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_json_file(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ModelCard.from_dict(self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		ModelCard.from_pretrained(lowerCAmelCase__)

      self.assertEqual(model_card_second.to_dict()  ,  model_card_first.to_dict())




