import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class       lowercase__		(     __SCREAMING_SNAKE_CASE):
      # to overwrite at feature extractactor specific tests
      UpperCamelCase_  =	None
      UpperCamelCase_  =	None


      @property
      def  __A ( self			:       Any		):





            '''simple docstring'''





            return self.feat_extract_tester.prepare_feat_extract_dict()


      def  __A ( self			:       Tuple		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Dict           =							self.feature_extraction_class(**self.feat_extract_dict		)
            self.assertTrue(hasattr(__snake_case					,	'''feature_size'''		)		)
            self.assertTrue(hasattr(__snake_case					,	'''sampling_rate'''		)		)
            self.assertTrue(hasattr(__snake_case					,	'''padding_value'''		)		)


      def  __A ( self			:       Dict		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					int           =							self.feat_extract_tester.prepare_inputs_for_common()
            SCREAMING_SNAKE_CASE							:					Any           =							self.feature_extraction_class(**self.feat_extract_dict		)
            SCREAMING_SNAKE_CASE							:					str           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							BatchFeature({input_name: speech_inputs}		)

            self.assertTrue(all(len(__snake_case		) == len(__snake_case		) for x, y in zip(__snake_case					,	processed_features[input_name]		)		)		)

            SCREAMING_SNAKE_CASE							:					Dict           =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							BatchFeature({input_name: speech_inputs}					,	tensor_type='''np'''		)

            SCREAMING_SNAKE_CASE							:					Dict           =							processed_features[input_name]

            if len(batch_features_input.shape		) < 3:
                  SCREAMING_SNAKE_CASE							:					int           =							batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)


      @require_torch
      def  __A ( self			:       List[str]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Dict           =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							self.feature_extraction_class(**self.feat_extract_dict		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							BatchFeature({input_name: speech_inputs}					,	tensor_type='''pt'''		)

            SCREAMING_SNAKE_CASE							:					Dict           =							processed_features[input_name]

            if len(batch_features_input.shape		) < 3:
                  SCREAMING_SNAKE_CASE							:					List[str]           =							batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)


      @require_tf
      def  __A ( self			:       Optional[Any]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					int           =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							self.feature_extraction_class(**self.feat_extract_dict		)
            SCREAMING_SNAKE_CASE							:					Tuple           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					str           =							BatchFeature({input_name: speech_inputs}					,	tensor_type='''tf'''		)

            SCREAMING_SNAKE_CASE							:					List[Any]           =							processed_features[input_name]

            if len(batch_features_input.shape		) < 3:
                  SCREAMING_SNAKE_CASE							:					Tuple           =							batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)


      def  __A ( self			:       Dict					,	UpperCamelCase__			:       Tuple=False		):





            '''simple docstring'''





            def _inputs_have_equal_length(UpperCamelCase__			:       int		):
                  SCREAMING_SNAKE_CASE							:					Any           =							len(input[0]		)
                  for input_slice in input[1:]:
                        if len(__snake_case		) != length:
                              return False
                  return True

            def _inputs_are_equal(UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Tuple		):
                  if len(__snake_case		) != len(__snake_case		):
                        return False

                  for input_slice_a, input_slice_a in zip(__snake_case					,	__snake_case		):
                        if not np.allclose(np.asarray(__snake_case		)					,	np.asarray(__snake_case		)					,	atol=1E-3		):
                              return False
                  return True

            SCREAMING_SNAKE_CASE							:					List[Any]           =							self.feature_extraction_class(**self.feat_extract_dict		)
            SCREAMING_SNAKE_CASE							:					Tuple           =							self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case		)
            SCREAMING_SNAKE_CASE							:					Any           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					Tuple           =							BatchFeature({input_name: speech_inputs}		)

            SCREAMING_SNAKE_CASE							:					List[Any]           =							self.feat_extract_tester.seq_length_diff
            SCREAMING_SNAKE_CASE							:					List[str]           =							self.feat_extract_tester.max_seq_length + pad_diff
            SCREAMING_SNAKE_CASE							:					List[str]           =							self.feat_extract_tester.min_seq_length
            SCREAMING_SNAKE_CASE							:					Any           =							self.feat_extract_tester.batch_size
            SCREAMING_SNAKE_CASE							:					List[Any]           =							self.feat_extract_tester.feature_size

            # test padding for List[int] + numpy
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							feat_extract.pad(__snake_case					,	padding=__snake_case		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							feat_extract.pad(__snake_case					,	padding='''longest'''		)
            SCREAMING_SNAKE_CASE							:					Dict           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					List[str]           =							feat_extract.pad(__snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[-1]		)		)
            SCREAMING_SNAKE_CASE							:					List[Any]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Tuple           =							feat_extract.pad(__snake_case					,	padding='''longest'''					,	return_tensors='''np'''		)
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_a[input_name]

            # max_length parameter has to be provided when setting `padding="max_length"`
            with self.assertRaises(__snake_case		):
                  feat_extract.pad(__snake_case					,	padding='''max_length'''		)[input_name]

            SCREAMING_SNAKE_CASE							:					Any           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=__snake_case					,	return_tensors='''np'''		)
            SCREAMING_SNAKE_CASE							:					List[str]           =							input_a[input_name]

            self.assertFalse(_inputs_have_equal_length(__snake_case		)		)
            self.assertTrue(_inputs_have_equal_length(__snake_case		)		)
            self.assertTrue(_inputs_have_equal_length(__snake_case		)		)
            self.assertTrue(_inputs_are_equal(__snake_case					,	__snake_case		)		)
            self.assertTrue(len(input_a[0]		) == pad_min_length		)
            self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
            self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
            self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

            if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

            # test padding for `pad_to_multiple_of` for List[int] + numpy
            SCREAMING_SNAKE_CASE							:					Tuple           =							feat_extract.pad(__snake_case					,	pad_to_multiple_of=10		)
            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Dict           =							feat_extract.pad(__snake_case					,	padding='''longest'''					,	pad_to_multiple_of=10		)
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Tuple           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	pad_to_multiple_of=10					,	max_length=__snake_case		)
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Tuple           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	pad_to_multiple_of=10					,	max_length=__snake_case					,	return_tensors='''np'''					,	)
            SCREAMING_SNAKE_CASE							:					str           =							input_a[input_name]

            self.assertTrue(all(len(__snake_case		) % 10 == 0 for x in input_a		)		)
            self.assertTrue(_inputs_are_equal(__snake_case					,	__snake_case		)		)

            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
            self.assertTrue(all(len(__snake_case		) == expected_mult_pad_length for x in input_a		)		)
            self.assertEqual(input_a.shape[:2]					,	(batch_size, expected_mult_pad_length)		)

            if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == feature_size		)

            # Check padding value is correct
            SCREAMING_SNAKE_CASE							:					str           =							(np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
            self.assertTrue(
                abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
                < 1E-3		)
            self.assertTrue(
                abs(
                    np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
                    - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
                < 1E-3		)
            self.assertTrue(
                abs(
                    np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
                    - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
                < 1E-3		)
            self.assertTrue(
                abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1E-3		)
            self.assertTrue(
                abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
                < 1E-3		)


      def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union[str, Any]=False		):





            '''simple docstring'''





            def _inputs_have_equal_length(UpperCamelCase__			:       List[str]		):
                  SCREAMING_SNAKE_CASE							:					str           =							len(input[0]		)
                  for input_slice in input[1:]:
                        if len(__snake_case		) != length:
                              return False
                  return True

            def _inputs_are_equal(UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[str]		):
                  if len(__snake_case		) != len(__snake_case		):
                        return False

                  for input_slice_a, input_slice_a in zip(__snake_case					,	__snake_case		):
                        if not np.allclose(np.asarray(__snake_case		)					,	np.asarray(__snake_case		)					,	atol=1E-3		):
                              return False
                  return True

            SCREAMING_SNAKE_CASE							:					str           =							self.feature_extraction_class(**self.feat_extract_dict		)
            SCREAMING_SNAKE_CASE							:					Any           =							self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case		)
            SCREAMING_SNAKE_CASE							:					Tuple           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					Optional[int]           =							BatchFeature({input_name: speech_inputs}		)

            # truncate to smallest
            SCREAMING_SNAKE_CASE							:					List[str]           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[0]		)					,	truncation=__snake_case		)
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					List[Any]           =							feat_extract.pad(__snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[0]		)		)
            SCREAMING_SNAKE_CASE							:					Any           =							input_a[input_name]

            self.assertTrue(_inputs_have_equal_length(__snake_case		)		)
            self.assertFalse(_inputs_have_equal_length(__snake_case		)		)

            # truncate to smallest with np
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[0]		)					,	return_tensors='''np'''					,	truncation=__snake_case					,	)
            SCREAMING_SNAKE_CASE							:					int           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					List[Any]           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[0]		)					,	return_tensors='''np'''		)
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_a[input_name]

            self.assertTrue(_inputs_have_equal_length(__snake_case		)		)
            self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

            # since truncation forces padding to be smaller than longest input
            # function can't return `np.ndarray`, but has to return list
            self.assertFalse(_inputs_have_equal_length(__snake_case		)		)

            # truncate to middle
            SCREAMING_SNAKE_CASE							:					Any           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[1]		)					,	truncation=__snake_case					,	return_tensors='''np'''					,	)
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Optional[int]           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[1]		)					,	truncation=__snake_case		)
            SCREAMING_SNAKE_CASE							:					Dict           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Optional[int]           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[1]		)					,	return_tensors='''np'''		)
            SCREAMING_SNAKE_CASE							:					List[Any]           =							input_a[input_name]

            self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
            self.assertTrue(_inputs_have_equal_length(__snake_case		)		)
            self.assertTrue(_inputs_have_equal_length(__snake_case		)		)
            self.assertTrue(_inputs_are_equal(__snake_case					,	__snake_case		)		)

            # since truncation forces padding to be smaller than longest input
            # function can't return `np.ndarray`, but has to return list
            self.assertFalse(_inputs_have_equal_length(__snake_case		)		)
            self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__snake_case		):
                  feat_extract.pad(__snake_case					,	truncation=__snake_case		)[input_name]

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__snake_case		):
                  feat_extract.pad(__snake_case					,	padding='''longest'''					,	truncation=__snake_case		)[input_name]

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__snake_case		):
                  feat_extract.pad(__snake_case					,	padding='''longest'''					,	truncation=__snake_case		)[input_name]

            # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
            with self.assertRaises(__snake_case		):
                  feat_extract.pad(__snake_case					,	padding='''max_length'''					,	truncation=__snake_case		)[input_name]

            # test truncation for `pad_to_multiple_of` for List[int] + numpy
            SCREAMING_SNAKE_CASE							:					Any           =							12
            SCREAMING_SNAKE_CASE							:					Dict           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[0]		)					,	pad_to_multiple_of=__snake_case					,	truncation=__snake_case					,	)
            SCREAMING_SNAKE_CASE							:					List[str]           =							input_a[input_name]

            SCREAMING_SNAKE_CASE							:					Dict           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=len(speech_inputs[0]		)					,	pad_to_multiple_of=__snake_case					,	)
            SCREAMING_SNAKE_CASE							:					List[str]           =							input_a[input_name]

            # retrieve expected_length as multiple of pad_to_multiple_of
            SCREAMING_SNAKE_CASE							:					List[str]           =							len(speech_inputs[0]		)
            if expected_length % pad_to_multiple_of != 0:
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

            self.assertTrue(len(input_a[0]		) == expected_length		)
            self.assertTrue(_inputs_have_equal_length(__snake_case		)		)
            self.assertFalse(_inputs_have_equal_length(__snake_case		)		)


      def  __A ( self			:       Union[str, Any]		):





            '''simple docstring'''





            self._check_padding(numpify=__snake_case		)


      def  __A ( self			:       int		):





            '''simple docstring'''





            self._check_padding(numpify=__snake_case		)


      def  __A ( self			:       Union[str, Any]		):





            '''simple docstring'''





            self._check_truncation(numpify=__snake_case		)


      def  __A ( self			:       str		):





            '''simple docstring'''





            self._check_truncation(numpify=__snake_case		)


      @require_torch
      def  __A ( self			:       Optional[int]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.feature_extraction_class(**self.feat_extract_dict		)
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.feat_extract_tester.prepare_inputs_for_common()
            SCREAMING_SNAKE_CASE							:					Dict           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					Any           =							BatchFeature({input_name: speech_inputs}		)

            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feat_extract.pad(__snake_case					,	padding='''longest'''					,	return_tensors='''np'''		)[input_name]
            SCREAMING_SNAKE_CASE							:					Dict           =							feat_extract.pad(__snake_case					,	padding='''longest'''					,	return_tensors='''pt'''		)[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1E-2		)


      @require_tf
      def  __A ( self			:       Optional[Any]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					List[Any]           =							self.feature_extraction_class(**self.feat_extract_dict		)
            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.feat_extract_tester.prepare_inputs_for_common()
            SCREAMING_SNAKE_CASE							:					Tuple           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					int           =							BatchFeature({input_name: speech_inputs}		)

            SCREAMING_SNAKE_CASE							:					str           =							feat_extract.pad(__snake_case					,	padding='''longest'''					,	return_tensors='''np'''		)[input_name]
            SCREAMING_SNAKE_CASE							:					List[Any]           =							feat_extract.pad(__snake_case					,	padding='''longest'''					,	return_tensors='''tf'''		)[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_tf.numpy().astype(np.floataa		).sum()		) < 1E-2		)


      def  __A ( self			:       int		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.feat_extract_dict
            SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
            SCREAMING_SNAKE_CASE							:					int           =							self.feature_extraction_class(**__snake_case		)
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.feat_extract_tester.prepare_inputs_for_common()
            SCREAMING_SNAKE_CASE							:					Tuple           =							[len(__snake_case		) for x in speech_inputs]
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					int           =							BatchFeature({input_name: speech_inputs}		)

            SCREAMING_SNAKE_CASE							:					int           =							feat_extract.pad(__snake_case					,	padding='''longest'''					,	return_tensors='''np'''		)
            self.assertIn('''attention_mask'''					,	__snake_case		)
            self.assertListEqual(list(processed.attention_mask.shape		)					,	list(processed[input_name].shape[:2]		)		)
            self.assertListEqual(processed.attention_mask.sum(-1		).tolist()					,	__snake_case		)



      def  __A ( self			:       Any		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					str           =							self.feat_extract_dict
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							True
            SCREAMING_SNAKE_CASE							:					int           =							self.feature_extraction_class(**__snake_case		)
            SCREAMING_SNAKE_CASE							:					List[Any]           =							self.feat_extract_tester.prepare_inputs_for_common()
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							[len(__snake_case		) for x in speech_inputs]
            SCREAMING_SNAKE_CASE							:					List[Any]           =							feat_extract.model_input_names[0]

            SCREAMING_SNAKE_CASE							:					str           =							BatchFeature({input_name: speech_inputs}		)
            SCREAMING_SNAKE_CASE							:					str           =							min(__snake_case		)

            SCREAMING_SNAKE_CASE							:					List[str]           =							feat_extract.pad(
                __snake_case					,	padding='''max_length'''					,	max_length=__snake_case					,	truncation=__snake_case					,	return_tensors='''np'''		)
            self.assertIn('''attention_mask'''					,	__snake_case		)
            self.assertListEqual(
                list(processed_pad.attention_mask.shape		)					,	[processed_pad[input_name].shape[0], max_length]		)
            self.assertListEqual(
                processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()					,	[max_length for x in speech_inputs]		)






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[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    '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       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


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





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

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

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{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			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	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					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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





