





"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase						=			'''▁'''

__UpperCAmelCase						=			{'''vocab_file''': '''spiece.model'''}

__UpperCAmelCase						=			{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

__UpperCAmelCase						=			{
    '''google/pegasus-xsum''': 512,
}


__UpperCAmelCase						=			logging.get_logger(__name__)



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Dict				     =				VOCAB_FILES_NAMES

				__lowerCamelCase				:	List[Any]				     =				VOCAB_FILES_NAMES
				__lowerCamelCase				:	Optional[int]				     =				PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase				:	Any				     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase				:	str				     =				["input_ids", "attention_mask"]
				def __init__(		self    :			List[str]	, a_    :			List[str]	, a_    :			Dict="<pad>"	, a_    :			Optional[Any]="</s>"	, a_    :			List[Any]="<unk>"	, a_    :			Tuple="<mask_2>"	, a_    :			Dict="<mask_1>"	, a_    :			List[str]=None	, a_    :			int=1_03	, a_    :			Optional[Dict[str, Any]] = None	, **a_    :			Dict	, )	->	None:



							'''simple docstring'''



							a__  :	Tuple		 =			offset
							if additional_special_tokens is not None:
										if not isinstance(a_	, a_    ):
													raise TypeError(
													    F"additional_special_tokens should be of type {type(a_    )}, but is"
													    F" {type(a_    )}"    )

										a__  :	Dict		 =			(
										    ([mask_token_sent] + additional_special_tokens)
										    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
										    else additional_special_tokens
										)
										# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
										additional_special_tokens_extended += [
										    F"<unk_{i}>" for i in range(len(a_    )	, self.offset - 1    )
										]

										if len(set(a_    )    ) != len(a_    ):
													raise ValueError(
													    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
													    F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."    )
										a__  :	str		 =			additional_special_tokens_extended
							else:
										a__  :	int		 =			[mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [F"<unk_{i}>" for i in range(2	, self.offset    )]

							a__  :	Tuple		 =			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=a_	, unk_token=a_	, mask_token=a_	, pad_token=a_	, mask_token_sent=a_	, offset=a_	, additional_special_tokens=a_	, sp_model_kwargs=self.sp_model_kwargs	, **a_	, )
							a__  :	int		 =			mask_token_sent
							a__  :	List[Any]		 =			vocab_file
							a__  :	int		 =			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(a_    )

							# add special tokens to encoder dict
							a__  :	Dict[int, str]		 =			{
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
										self.encoder.update(
										    {
										        2: self.mask_token_sent,
										        3: self.mask_token,
										    }    )

							if self.offset > 0:
										# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
										# mask_token_sent is already added to list -> so start at 1
										self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1	, self.offset - 1    )}    )

							a__  :	Dict[str, int]		 =			{v: k for k, v in self.encoder.items()}
				@property
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							return len(self.sp_model    ) + self.offset
				def       UpperCAmelCase      (		self    :			Any    )	->	Dict[str, int]:



							'''simple docstring'''



							a__  :	str		 =			{self.convert_ids_to_tokens(a_    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab
				def __getstate__(		self    :			List[Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.__dict__.copy()
							a__  :	List[str]		 =			None
							return state
				def __setstate__(		self    :			Tuple	, a_    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			d

							# for backward compatibility
							if not hasattr(self	, "sp_model_kwargs"    ):
										a__  :	int		 =			{}

							a__  :	Optional[int]		 =			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			str    )	->	List[str]:



							'''simple docstring'''



							return self.sp_model.encode(a_	, out_type=a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			str    )	->	int:



							'''simple docstring'''



							if token in self.decoder:
										return self.decoder[token]
							elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
							a__  :	Dict		 =			self.sp_model.piece_to_id(a_    )
							return sp_id + self.offset
				def       UpperCAmelCase      (		self    :			str	, a_    :			int    )	->	str:



							'''simple docstring'''



							if index in self.encoder:
										return self.encoder[index]
							elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
							else:
										a__  :	Any		 =			self.sp_model.IdToPiece(index - self.offset    )
							return token
				def       UpperCAmelCase      (		self    :			Any	, a_    :			Tuple    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	str		 =			[]
							a__  :	str		 =			""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(a_    ) + token
													a__  :	List[Any]		 =			[]
										else:
													current_sub_tokens.append(a_    )
							out_string += self.sp_model.decode(a_    )
							return out_string.strip()
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			List[str]=False    )	->	Optional[Any]:



							'''simple docstring'''



							return 1
				def       UpperCAmelCase      (		self    :			str	, a_    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	Dict		 =			set(self.all_special_ids    )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			List	, a_    :			Optional[List] = None	, a_    :			bool = False    )	->	List[int]:



							'''simple docstring'''



							if already_has_special_tokens:
										return self._special_token_mask(a_    )
							elif token_ids_a is None:
										return self._special_token_mask(a_    ) + [1]
							else:
										return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			str	, a_    :			Tuple=None    )	->	List[int]:



							'''simple docstring'''



							if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]






				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			str	, a_    :			Optional[str] = None    )	->	Tuple[str]:



							'''simple docstring'''



							if not os.path.isdir(a_    ):
										logger.error(F"Vocabulary path ({save_directory}) should be a directory"    )
										return
							a__  :	int		 =			os.path.join(
							    a_	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

							if os.path.abspath(self.vocab_file    ) != os.path.abspath(a_    ) and os.path.isfile(self.vocab_file    ):
										copyfile(self.vocab_file	, a_    )
							elif not os.path.isfile(self.vocab_file    ):
										with open(a_	, "wb"    ) as fi:
													a__  :	Optional[Any]		 =			self.sp_model.serialized_model_proto()
													fi.write(a_    )

							return (out_vocab_file,)






"""simple docstring"""






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 					__UpperCAmelCase							(						_UpperCamelCase  ):
				# to overwrite at feature extractactor specific tests
				__lowerCamelCase				:	int				     =				None
				__lowerCamelCase				:	Tuple				     =				None
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							return self.feat_extract_tester.prepare_feat_extract_dict()
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							self.assertTrue(hasattr(a_	, "feature_size"    )    )
							self.assertTrue(hasattr(a_	, "sampling_rate"    )    )
							self.assertTrue(hasattr(a_	, "padding_value"    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



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

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

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

							a__  :	Any		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	List[Any]		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="np"    )

							a__  :	Union[str, Any]		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	List[Any]		 =			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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	List[Any]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Optional[Any]		 =			feat_extract.model_input_names[0]

							a__  :	Any		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="pt"    )

							a__  :	Optional[int]		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	Any		 =			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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[int]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	Optional[int]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	str		 =			feat_extract.model_input_names[0]

							a__  :	List[str]		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="tf"    )

							a__  :	Tuple		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	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)    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[Any]=False    )	->	List[Any]:



							'''simple docstring'''



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

							def _inputs_are_equal(a_    :			List[Any]	, a_    :			str    ):
										if len(a_    ) != len(a_    ):
													return False

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

							a__  :	int		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	int		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	Tuple		 =			feat_extract.model_input_names[0]

							a__  :	List[str]		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	Optional[int]		 =			self.feat_extract_tester.seq_length_diff
							a__  :	Optional[Any]		 =			self.feat_extract_tester.max_seq_length + pad_diff
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.min_seq_length
							a__  :	Tuple		 =			self.feat_extract_tester.batch_size
							a__  :	str		 =			self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							a__  :	Any		 =			feat_extract.pad(a_	, padding=a_    )
							a__  :	List[str]		 =			input_a[input_name]

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	Optional[Any]		 =			feat_extract.pad(a_	, padding="max_length"	, max_length=len(speech_inputs[-1]    )    )
							a__  :	Any		 =			input_a[input_name]

							a__  :	Tuple		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

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

							a__  :	Tuple		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							self.assertFalse(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_are_equal(a_	, a_    )    )
							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
							a__  :	Optional[int]		 =			feat_extract.pad(a_	, pad_to_multiple_of=10    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	List[str]		 =			feat_extract.pad(a_	, padding="longest"	, pad_to_multiple_of=10    )
							a__  :	Dict		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_    )
							a__  :	Tuple		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_	, return_tensors="np"	, )
							a__  :	Optional[int]		 =			input_a[input_name]

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

							a__  :	List[str]		 =			pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							self.assertTrue(all(len(a_    ) == 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
							a__  :	Optional[int]		 =			(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       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[int]=False    )	->	List[str]:



							'''simple docstring'''



							def _inputs_have_equal_length(a_    :			List[Any]    ):
										a__  :	List[str]		 =			len(input[0]    )
										for input_slice in input[1:]:
													if len(a_    ) != length:
																return False
										return True

							def _inputs_are_equal(a_    :			Optional[int]	, a_    :			List[Any]    ):
										if len(a_    ) != len(a_    ):
													return False

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

							a__  :	Dict		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

							# truncate to smallest
							a__  :	int		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, truncation=a_    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	int		 =			feat_extract.pad(a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )    )
							a__  :	List[Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )

							# truncate to smallest with np
							a__  :	Union[str, Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, return_tensors="np"	, truncation=a_	, )
							a__  :	Tuple		 =			input_a[input_name]

							a__  :	str		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, return_tensors="np"    )
							a__  :	Union[str, Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							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(a_    )    )

							# truncate to middle
							a__  :	str		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, truncation=a_	, return_tensors="np"	, )
							a__  :	List[Any]		 =			input_a[input_name]

							a__  :	List[str]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, truncation=a_    )
							a__  :	str		 =			input_a[input_name]

							a__  :	Any		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, return_tensors="np"    )
							a__  :	Optional[int]		 =			input_a[input_name]

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

							# 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(a_    )    )
							self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

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

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

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

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

							# test truncation for `pad_to_multiple_of` for List[int] + numpy
							a__  :	Tuple		 =			12
							a__  :	Dict		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, pad_to_multiple_of=a_	, truncation=a_	, )
							a__  :	Optional[int]		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, pad_to_multiple_of=a_	, )
							a__  :	str		 =			input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							a__  :	Dict		 =			len(speech_inputs[0]    )
							if expected_length % pad_to_multiple_of != 0:
										a__  :	Tuple		 =			((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(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	int:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				@require_torch
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Tuple		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Tuple		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	int		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	str		 =			feat_extract.pad(a_	, 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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	str:



							'''simple docstring'''



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

							a__  :	Dict		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	List[Any]		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	List[str]		 =			feat_extract.pad(a_	, 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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			self.feat_extract_dict
							a__  :	Dict		 =			True
							a__  :	Dict		 =			self.feature_extraction_class(**a_    )
							a__  :	List[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	List[Any]		 =			[len(a_    ) for x in speech_inputs]
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

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

							a__  :	Tuple		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )
							self.assertIn("attention_mask"	, a_    )
							self.assertListEqual(list(processed.attention_mask.shape    )	, list(processed[input_name].shape[:2]    )    )
							self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()	, a_    )






				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[Any]:



							'''simple docstring'''



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

							a__  :	int		 =			BatchFeature({input_name: speech_inputs}    )
							a__  :	Tuple		 =			min(a_    )

							a__  :	int		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, truncation=a_	, return_tensors="np"    )
							self.assertIn("attention_mask"	, a_    )
							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]    )