
"""simple docstring"""





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


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



class __lowerCAmelCase					(       snake_case__   ):




			"""simple docstring"""







			snake_case						   =					['input_features', 'is_longer']




			def __init__(					self    :       int							, _snake_case    :       str=64							, _snake_case    :       str=48_000							, _snake_case    :       Any=480							, _snake_case    :       List[str]=10							, _snake_case    :       List[Any]=1_024							, _snake_case    :       Union[str, Any]=0.0							, _snake_case    :       Tuple=False							, _snake_case    :       float = 0							, _snake_case    :       float = 14_000							, _snake_case    :       int = None							, _snake_case    :       str = "fusion"							, _snake_case    :       str = "repeatpad"							, **_snake_case    :       Optional[int]							, )   ->			Union[str, Any]:







								"""simple docstring"""
								super().__init__(
								    feature_size=_snake_case							, sampling_rate=_snake_case							, padding_value=_snake_case							, return_attention_mask=_snake_case							, **_snake_case							, )
								A_								=    top_db
								A_								=    truncation
								A_								=    padding
								A_								=    fft_window_size
								A_								=    (fft_window_size >> 1) + 1
								A_								=    hop_length
								A_								=    max_length_s
								A_								=    max_length_s * sampling_rate
								A_								=    sampling_rate
								A_								=    frequency_min
								A_								=    frequency_max
								A_								=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							, num_mel_filters=_snake_case							, min_frequency=_snake_case							, max_frequency=_snake_case							, sampling_rate=_snake_case							, norm=_snake_case							, mel_scale="htk"							, )
								A_								=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins							, num_mel_filters=_snake_case							, min_frequency=_snake_case							, max_frequency=_snake_case							, sampling_rate=_snake_case							, norm="slaney"							, mel_scale="slaney"							, )




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Any:







								"""simple docstring"""
								A_								=    copy.deepcopy(self.__dict__						)
								A_								=    self.__class__.__name__
								if "mel_filters" in output:
													del output["mel_filters"]
								if "mel_filters_slaney" in output:
													del output["mel_filters_slaney"]
								return output




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       np.array							, _snake_case    :       Optional[np.array] = None						)   ->			str:







								"""simple docstring"""
								A_								=    spectrogram(
								    _snake_case							, window_function(self.fft_window_size							, "hann"						)							, frame_length=self.fft_window_size							, hop_length=self.hop_length							, power=2.0							, mel_filters=_snake_case							, log_mel="dB"							, )
								return log_mel_spectrogram.T




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       int							, _snake_case    :       int							, _snake_case    :       Tuple						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    np.array_split(list(range(0							, total_frames - chunk_frames + 1						)						)							, 3						)
								if len(ranges[1]						) == 0:
													# if the audio is too short, we just use the first chunk
													A_								=    [0]
								if len(ranges[2]						) == 0:
													# if the audio is too short, we just use the first chunk
													A_								=    [0]
								# randomly choose index for each part
								A_								=    np.random.choice(ranges[0]						)
								A_								=    np.random.choice(ranges[1]						)
								A_								=    np.random.choice(ranges[2]						)

								A_								=    mel[idx_front : idx_front + chunk_frames, :]
								A_								=    mel[idx_middle : idx_middle + chunk_frames, :]
								A_								=    mel[idx_back : idx_back + chunk_frames, :]

								A_								=    torch.tensor(mel[None, None, :]						)
								A_								=    torch.nn.functional.interpolate(
								    _snake_case							, size=[chunk_frames, 64]							, mode="bilinear"							, align_corners=_snake_case						)
								A_								=    mel_shrink[0][0].numpy()
								A_								=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]							, axis=0						)
								return mel_fusion




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       np.array							, _snake_case    :       int							, _snake_case    :       List[str]							, _snake_case    :       List[str]						)   ->			Dict:







								"""simple docstring"""
								if waveform.shape[0] > max_length:
													if truncation == "rand_trunc":
																		A_								=    True
																		# random crop to max_length (for compatibility) -> this should be handled by self.pad
																		A_								=    len(_snake_case						) - max_length
																		A_								=    np.random.randint(0							, overflow + 1						)
																		A_								=    waveform[idx : idx + max_length]
																		A_								=    self._np_extract_fbank_features(_snake_case							, self.mel_filters_slaney						)[None, :]
													elif truncation == "fusion":
																		A_								=    self._np_extract_fbank_features(_snake_case							, self.mel_filters						)
																		A_								=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																		A_								=    mel.shape[0]
																		if chunk_frames == total_frames:
																							# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																							# In this case, we just use the whole audio.
																							A_								=    np.stack([mel, mel, mel, mel]							, axis=0						)
																							A_								=    False
																		else:
																							A_								=    self._random_mel_fusion(_snake_case							, _snake_case							, _snake_case						)
																							A_								=    True
													else:
																		raise NotImplementedError(F'data_truncating {truncation} not implemented'						)

								else:
													A_								=    False
													# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
													if waveform.shape[0] < max_length:
																		if padding == "repeat":
																							A_								=    int(max_length / len(_snake_case						)						)
																							A_								=    np.stack(np.tile(_snake_case							, n_repeat + 1						)						)[:max_length]
																		if padding == "repeatpad":
																							A_								=    int(max_length / len(_snake_case						)						)
																							A_								=    np.stack(np.tile(_snake_case							, _snake_case						)						)
																		A_								=    np.pad(_snake_case							, (0, max_length - waveform.shape[0])							, mode="constant"							, constant_values=0						)

													if truncation == "fusion":
																		A_								=    self._np_extract_fbank_features(_snake_case							, self.mel_filters						)
																		A_								=    np.stack([input_mel, input_mel, input_mel, input_mel]							, axis=0						)
													else:
																		A_								=    self._np_extract_fbank_features(_snake_case							, self.mel_filters_slaney						)[None, :]

								return input_mel, longer






			def __call__(					self    :       Tuple							, _snake_case    :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							, _snake_case    :       str = None							, _snake_case    :       Optional[str] = None							, _snake_case    :       Optional[int] = None							, _snake_case    :       Optional[int] = None							, _snake_case    :       Optional[Union[str, TensorType]] = None							, **_snake_case    :       int							, )   ->			List[str]:







								"""simple docstring"""
								A_								=    truncation if truncation is not None else self.truncation
								A_								=    padding if padding else self.padding

								if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																		raise ValueError(
																		    F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
																		    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																		    F' was sampled with {self.sampling_rate} and not {sampling_rate}.'						)
								else:
													logger.warning(
													    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
													    "Failing to do so can result in silent errors that might be hard to debug."						)

								A_								=    isinstance(_snake_case							, np.ndarray						) and len(raw_speech.shape						) > 1
								if is_batched_numpy and len(raw_speech.shape						) > 2:
													raise ValueError(F'Only mono-channel audio is supported for input to {self}'						)
								A_								=    is_batched_numpy or (
								    isinstance(_snake_case							, (list, tuple)						) and (isinstance(raw_speech[0]							, (np.ndarray, tuple, list)						))
								)

								if is_batched:
													A_								=    [np.asarray(_snake_case							, dtype=np.floataa						) for speech in raw_speech]
								elif not is_batched and not isinstance(_snake_case							, np.ndarray						):
													A_								=    np.asarray(_snake_case							, dtype=np.floataa						)
								elif isinstance(_snake_case							, np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
													A_								=    raw_speech.astype(np.floataa						)

								# always return batch
								if not is_batched:
													A_								=    [np.asarray(_snake_case						)]

								# convert to mel spectrogram, truncate and pad if needed.
								A_								=    [
								    self._get_input_mel(_snake_case							, max_length if max_length else self.nb_max_samples							, _snake_case							, _snake_case						)
								    for waveform in raw_speech
								]

								A_								=    []
								A_								=    []
								for mel, longer in padded_inputs:
													input_mel.append(_snake_case						)
													is_longer.append(_snake_case						)

								if truncation == "fusion" and sum(_snake_case						) == 0:
													# if no audio is longer than 10s, then randomly select one audio to be longer
													A_								=    np.random.randint(0							, len(_snake_case						)						)
													A_								=    True

								if isinstance(input_mel[0]							, _snake_case						):
													A_								=    [np.asarray(_snake_case							, dtype=np.floataa						) for feature in input_mel]

								# is_longer is a list of bool
								A_								=    [[longer] for longer in is_longer]

								A_								=    {'input_features': input_mel, 'is_longer': is_longer}
								A_								=    BatchFeature(_snake_case						)

								if return_tensors is not None:
													A_								=    input_features.convert_to_tensors(_snake_case						)

								return input_features











import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class 						UpperCamelCase_  (  snake_case__							):



					def __init__(		self     :     str       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     str=13       ,      lowerCamelCase     :     Optional[Any]=7       ,      lowerCamelCase     :     List[Any]=True       ,      lowerCamelCase     :     Dict=True       ,      lowerCamelCase     :     List[Any]=True       ,      lowerCamelCase     :     Any=True       ,      lowerCamelCase     :     Optional[Any]=99       ,      lowerCamelCase     :     Union[str, Any]=32       ,      lowerCamelCase     :     Any=5       ,      lowerCamelCase     :     Tuple=4       ,      lowerCamelCase     :     Optional[int]=37       ,      lowerCamelCase     :     Optional[Any]="gelu"       ,      lowerCamelCase     :     Optional[Any]=0.1       ,      lowerCamelCase     :     str=0.1       ,      lowerCamelCase     :     Tuple=5_12       ,      lowerCamelCase     :     Union[str, Any]=16       ,      lowerCamelCase     :     Union[str, Any]=2       ,      lowerCamelCase     :     Dict=0.02       ,      lowerCamelCase     :     Any=False       ,      lowerCamelCase     :     int=True       ,      lowerCamelCase     :     Optional[Any]="None"       ,      lowerCamelCase     :     Optional[int]=3       ,      lowerCamelCase     :     List[Any]=4       ,      lowerCamelCase     :     int=None       ,      ):
							lowerCamelCase_	:     int 						=      parent
							lowerCamelCase_	:     List[Any] 						=      batch_size
							lowerCamelCase_	:     Dict 						=      seq_length
							lowerCamelCase_	:     Union[str, Any] 						=      is_training
							lowerCamelCase_	:     str 						=      use_input_mask
							lowerCamelCase_	:     int 						=      use_token_type_ids
							lowerCamelCase_	:     Tuple 						=      use_labels
							lowerCamelCase_	:     str 						=      vocab_size
							lowerCamelCase_	:     int 						=      hidden_size
							lowerCamelCase_	:     Optional[int] 						=      num_hidden_layers
							lowerCamelCase_	:     Optional[Any] 						=      num_attention_heads
							lowerCamelCase_	:     Any 						=      intermediate_size
							lowerCamelCase_	:     List[Any] 						=      hidden_act
							lowerCamelCase_	:     List[Any] 						=      hidden_dropout_prob
							lowerCamelCase_	:     List[Any] 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     List[str] 						=      max_position_embeddings
							lowerCamelCase_	:     Tuple 						=      type_vocab_size
							lowerCamelCase_	:     str 						=      type_sequence_label_size
							lowerCamelCase_	:     Optional[int] 						=      initializer_range
							lowerCamelCase_	:     List[Any] 						=      num_labels
							lowerCamelCase_	:     str 						=      num_choices
							lowerCamelCase_	:     Dict 						=      relative_attention
							lowerCamelCase_	:     Optional[int] 						=      position_biased_input
							lowerCamelCase_	:     List[Any] 						=      pos_att_type
							lowerCamelCase_	:     Optional[int] 						=      scope



					def   __a     (		self     :     List[Any]						):
							lowerCamelCase_	:     Tuple 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size						)

							lowerCamelCase_	:     Dict 						=      None
							if self.use_input_mask:
									lowerCamelCase_	:     Optional[int] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      vocab_size=2						)

							lowerCamelCase_	:     str 						=      None
							if self.use_token_type_ids:
									lowerCamelCase_	:     Dict 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.type_vocab_size						)

							lowerCamelCase_	:     Union[str, Any] 						=      None
							lowerCamelCase_	:     List[Any] 						=      None
							lowerCamelCase_	:     str 						=      None
							if self.use_labels:
									lowerCamelCase_	:     Any 						=      ids_tensor([self.batch_size]       ,      self.type_sequence_label_size						)
									lowerCamelCase_	:     Optional[Any] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.num_labels						)
									lowerCamelCase_	:     Any 						=      ids_tensor([self.batch_size]       ,      self.num_choices						)

							lowerCamelCase_	:     Optional[Any] 						=      self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



					def   __a     (		self     :     Tuple						):
							return DebertaConfig(
							    vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      type_vocab_size=self.type_vocab_size       ,      initializer_range=self.initializer_range       ,      relative_attention=self.relative_attention       ,      position_biased_input=self.position_biased_input       ,      pos_att_type=self.pos_att_type       ,      )



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     int 						=      self.get_config()
							lowerCamelCase_	:     Any 						=      3_00
							return config



					def   __a     (		self     :     Optional[int]       ,      lowerCamelCase     :     Dict						):
							self.parent.assertListEqual(list(result.loss.size()						)       ,      []						)



					def   __a     (		self     :     Dict       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     str 						=      DebertaModel(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase						)[0]
							lowerCamelCase_	:     Union[str, Any] 						=      model(lowerCamelCase       ,      token_type_ids=lowerCamelCase						)[0]
							lowerCamelCase_	:     List[Any] 						=      model(lowerCamelCase						)[0]

							self.parent.assertListEqual(list(sequence_output.size()						)       ,      [self.batch_size, self.seq_length, self.hidden_size]						)



					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Any						):
							lowerCamelCase_	:     Any 						=      DebertaForMaskedLM(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Any 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      labels=lowerCamelCase						)

							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)						)



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.num_labels
							lowerCamelCase_	:     str 						=      DebertaForSequenceClassification(lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertListEqual(list(result.logits.size()						)       ,      [self.batch_size, self.num_labels]						)
							self.check_loss_output(lowerCamelCase						)



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Dict       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Optional[int]						):
							lowerCamelCase_	:     List[Any] 						=      self.num_labels
							lowerCamelCase_	:     str 						=      DebertaForTokenClassification(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)						)



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     str						):
							lowerCamelCase_	:     int 						=      DebertaForQuestionAnswering(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     List[Any] 						=      model(
							    lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      start_positions=lowerCamelCase       ,      end_positions=lowerCamelCase       ,      )
							self.parent.assertEqual(result.start_logits.shape       ,      (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape       ,      (self.batch_size, self.seq_length)						)





					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Any 						=      self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      
							)	:     Dict 						=      config_and_inputs
							lowerCamelCase_	:     str 						=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						str						    =					(
					    (
					        DebertaModel,
					        DebertaForMaskedLM,
					        DebertaForSequenceClassification,
					        DebertaForTokenClassification,
					        DebertaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					_a      :						Any						    =					(
					    {
					        'feature-extraction': DebertaModel,
					        'fill-mask': DebertaForMaskedLM,
					        'question-answering': DebertaForQuestionAnswering,
					        'text-classification': DebertaForSequenceClassification,
					        'token-classification': DebertaForTokenClassification,
					        'zero-shot': DebertaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					_a      :						Optional[int]						    =					True
					_a      :						List[str]						    =					False
					_a      :						List[str]						    =					False
					_a      :						List[str]						    =					False
					_a      :						List[Any]						    =					False



					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     List[Any] 						=      DebertaModelTester(self						)
							lowerCamelCase_	:     Any 						=      ConfigTester(self       ,      config_class=lowerCamelCase       ,      hidden_size=37						)



					def   __a     (		self     :     Any						):
							self.config_tester.run_common_tests()



					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     int 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*lowerCamelCase						)



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     str 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     Optional[Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase						)



					def   __a     (		self     :     str						):
							lowerCamelCase_	:     List[Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     List[Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase						)





					@slow
					def   __a     (		self     :     Optional[int]						):
							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     Optional[int] 						=      DebertaModel.from_pretrained(lowerCamelCase						)
									self.assertIsNotNone(lowerCamelCase						)






@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase_  (  unittest.TestCase							):



					@unittest.skip(reason='Model not available yet'						)
					def   __a     (		self     :     str						):
							pass





					@slow
					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     List[str] 						=      DebertaModel.from_pretrained('microsoft/deberta-base'						)

							lowerCamelCase_	:     Optional[Any] 						=      torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]						)
							lowerCamelCase_	:     List[str] 						=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
							with torch.no_grad():
									lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase						)[0]
							# compare the actual values for a slice.
							lowerCamelCase_	:     str 						=      torch.tensor(
							    [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]						)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,      lowerCamelCase       ,      atol=1E-4						)       ,      F"{output[:, 1:4, 1:4]}"						)
