




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_		:					Any          =  get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase_		:					int          =  250004
UpperCAmelCase_		:					List[str]          =  250020




@require_sentencepiece
@require_tokenizers
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       MBartTokenizer
						UpperCamelCase      =       MBartTokenizerFast
						UpperCamelCase      =       True
						UpperCamelCase      =       True

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__						:						List[str]										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)
										tokenizer.save_pretrained(self.tmpdirname			)

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Any										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)

										__magic_name__						:						int										=tokenizer.tokenize("""This is a test"""			)
										self.assertListEqual(__snake_case     ,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__snake_case			)     ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,			)

										__magic_name__						:						Optional[int]										=tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
										self.assertListEqual(
										    __snake_case     ,			[
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """9""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """é""",
										        """.""",
										    ]     ,			)
										__magic_name__						:						Optional[Any]										=tokenizer.convert_tokens_to_ids(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]     ,			)

										__magic_name__						:						Any										=tokenizer.convert_ids_to_tokens(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """<unk>""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """<unk>""",
										        """.""",
										    ]     ,			)


						def      A__    (		self :Tuple			):
										'''simple docstring'''

										if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

										__magic_name__						:						Tuple										=(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
																		__magic_name__						:						List[Any]										=self.rust_tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)
																		__magic_name__						:						List[str]										=self.tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)

																		__magic_name__						:						Optional[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it save with the same files + the tokenizer.json file for the fast one
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)
																		__magic_name__						:						int										=tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
																		self.assertSequenceEqual(__snake_case     ,			__snake_case			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						List[Any]										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)
																						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=True
																		__magic_name__						:						List[str]										=tempfile.mkdtemp()

																		__magic_name__						:						Optional[int]										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it save with the same files
																		self.assertSequenceEqual(__snake_case     ,			__snake_case			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						int										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=False
																		__magic_name__						:						List[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						List[str]										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it saved the tokenizer.json file
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						str										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						Optional[int]										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)

																		shutil.rmtree(__snake_case			)




@require_torch
@require_sentencepiece
@require_tokenizers
class 		__A   (      unittest.TestCase      ):
						UpperCamelCase      =       """facebook/mbart-large-en-ro"""
						UpperCamelCase      =       [
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						UpperCamelCase      =       [
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						UpperCamelCase      =       [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]

						@classmethod
						def      A__    (		cls :str			):
										'''simple docstring'''

										__magic_name__						:						MBartTokenizer										=MBartTokenizer.from_pretrained(
										    cls.checkpoint_name     ,			src_lang="""en_XX"""     ,			tgt_lang="""ro_RO"""			)
										__magic_name__						:						Any										=1
										return cls

						def      A__    (		self :Any			):
										'''simple docstring'''

										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]     ,			25_00_01			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]     ,			25_00_04			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]     ,			25_00_20			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										self.assertIn(__snake_case     ,			self.tokenizer.all_special_ids			)
										__magic_name__						:						Union[str, Any]										=[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
										__magic_name__						:						Optional[int]										=self.tokenizer.decode(__snake_case     ,			skip_special_tokens=__snake_case			)
										__magic_name__						:						List[str]										=self.tokenizer.decode(generated_ids[1:]     ,			skip_special_tokens=__snake_case			)
										self.assertEqual(__snake_case     ,			__snake_case			)
										self.assertNotIn(self.tokenizer.eos_token     ,			__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						str										=["""this is gunna be a long sentence """ * 20]
										assert isinstance(src_text[0]     ,			__snake_case			)
										__magic_name__						:						Dict										=10
										__magic_name__						:						Optional[Any]										=self.tokenizer(__snake_case     ,			max_length=__snake_case     ,			truncation=__snake_case			).input_ids[0]
										self.assertEqual(ids[-2]     ,			2			)
										self.assertEqual(ids[-1]     ,			__snake_case			)
										self.assertEqual(len(__snake_case			)     ,			__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)     ,			[25_00_26, 25_00_01]			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=tempfile.mkdtemp()
										__magic_name__						:						Dict										=self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__snake_case			)
										__magic_name__						:						Dict										=MBartTokenizer.from_pretrained(__snake_case			)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,			__snake_case			)

						@require_torch
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer(self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			return_tensors="""pt"""			)
										__magic_name__						:						str										=shift_tokens_right(batch["""labels"""]     ,			self.tokenizer.pad_token_id			)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
										assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
										assert batch.decoder_input_ids[1][-1] == 2
										assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

						@require_torch
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.tokenizer(
										    self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=len(self.expected_src_tokens			)     ,			return_tensors="""pt"""     ,			)

										__magic_name__						:						Any										=shift_tokens_right(batch["""labels"""]     ,			self.tokenizer.pad_token_id			)

										self.assertIsInstance(__snake_case     ,			__snake_case			)

										self.assertEqual((2, 14)     ,			batch.input_ids.shape			)
										self.assertEqual((2, 14)     ,			batch.attention_mask.shape			)
										__magic_name__						:						int										=batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)
										self.assertEqual(2     ,			batch.decoder_input_ids[0, -1]			)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens     ,			[]			)
										self.assertEqual(self.tokenizer.suffix_tokens     ,			[self.tokenizer.eos_token_id, EN_CODE]			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.tokenizer(self.src_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=3     ,			return_tensors="""pt"""			)
										__magic_name__						:						Tuple										=self.tokenizer(
										    text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=10     ,			return_tensors="""pt"""			)
										__magic_name__						:						List[Any]										=targets["""input_ids"""]
										__magic_name__						:						List[str]										=shift_tokens_right(__snake_case     ,			self.tokenizer.pad_token_id			)

										self.assertEqual(batch.input_ids.shape[1]     ,			3			)
										self.assertEqual(batch.decoder_input_ids.shape[1]     ,			10			)


						@require_torch
						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.tokenizer._build_translation_inputs(
										    """A test"""     ,			return_tensors="""pt"""     ,			src_lang="""en_XX"""     ,			tgt_lang="""ar_AR"""			)

										self.assertEqual(
										    nested_simplify(__snake_case			)     ,			{
										        # A, test, EOS, en_XX
										        """input_ids""": [[62, 30_34, 2, 25_00_04]],
										        """attention_mask""": [[1, 1, 1, 1]],
										        # ar_AR
										        """forced_bos_token_id""": 25_00_01,
										    }     ,			)










from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase_		:					Dict          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):

						def __init__(		self :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :float     ,			**__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=feature_size
										__magic_name__						:						Union[str, Any]										=sampling_rate
										__magic_name__						:						List[Any]										=padding_value

										__magic_name__						:						List[str]										=kwargs.pop("""padding_side"""     ,			"""right"""			)
										__magic_name__						:						Tuple										=kwargs.pop("""return_attention_mask"""     ,			__snake_case			)

										super().__init__(**__snake_case			)

						def      A__    (		self :Any     ,			__snake_case :Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]     ,			__snake_case :Union[bool, str, PaddingStrategy] = True     ,			__snake_case :Optional[int] = None     ,			__snake_case :bool = False     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			):
										'''simple docstring'''

										if isinstance(__snake_case     ,			(list, tuple)			) and isinstance(processed_features[0]     ,			(dict, BatchFeature)			):
														__magic_name__						:						Union[str, Any]										={
														    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
														}

										# The model's main input name, usually `input_values`, has be passed for padding
										if self.model_input_names[0] not in processed_features:
														raise ValueError(
														    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
														    f" to this method that includes {self.model_input_names[0]}, but you provided"
														    f" {list(processed_features.keys()			)}"			)

										__magic_name__						:						int										=processed_features[self.model_input_names[0]]
										__magic_name__						:						Union[str, Any]										=(
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(__snake_case			) == 0:
														if return_attention_mask:
																		__magic_name__						:						List[str]										=[]
														return processed_features

										# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
										# and rebuild them afterwards if no return_tensors is specified
										# Note that we lose the specific device the tensor may be on for PyTorch

										__magic_name__						:						Optional[int]										=required_input[0]
										if isinstance(__snake_case     ,			(list, tuple)			):
														# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
														__magic_name__						:						Optional[Any]										=0
														while len(required_input[index]			) == 0:
																		index += 1
														if index < len(__snake_case			):
																		__magic_name__						:						List[str]										=required_input[index][0]

										if return_tensors is None:
														if is_tf_tensor(__snake_case			):
																		__magic_name__						:						int										="""tf"""
														elif is_torch_tensor(__snake_case			):
																		__magic_name__						:						str										="""pt"""
														elif isinstance(__snake_case     ,			(int, float, list, tuple, np.ndarray)			):
																		__magic_name__						:						List[Any]										="""np"""
														else:
																		raise ValueError(
																		    f"type of {first_element} unknown: {type(__snake_case			)}. "
																		    """Should be one of a python, numpy, pytorch or tensorflow object."""			)

										for key, value in processed_features.items():
														if isinstance(value[0]     ,			(int, float)			):
																		__magic_name__						:						List[str]										=to_numpy(__snake_case			)
														else:
																		__magic_name__						:						str										=[to_numpy(__snake_case			) for v in value]

        # Convert padding_strategy in PaddingStrategy
										__magic_name__						:						Dict										=self._get_padding_strategies(padding=__snake_case     ,			max_length=__snake_case			)

										__magic_name__						:						Optional[Any]										=processed_features[self.model_input_names[0]]

										__magic_name__						:						Dict										=len(__snake_case			)
										if not all(len(__snake_case			) == batch_size for v in processed_features.values()			):
														raise ValueError("""Some items in the output dictionary have a different batch size than others."""			)

										__magic_name__						:						Optional[int]										=[]
										for i in range(__snake_case			):
														__magic_name__						:						Any										={k: v[i] for k, v in processed_features.items()}
														# truncation
														__magic_name__						:						List[str]										=self._truncate(
														    __snake_case     ,			max_length=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			truncation=__snake_case     ,			)
														truncated_inputs.append(__snake_case			)

										if padding_strategy == PaddingStrategy.LONGEST:
														# make sure that `max_length` cannot be longer than the longest truncated length
														__magic_name__						:						Optional[int]										=max(len(input_slice[self.model_input_names[0]]			) for input_slice in truncated_inputs			)
														__magic_name__						:						Tuple										=PaddingStrategy.MAX_LENGTH

										__magic_name__						:						str										={}
										for i in range(__snake_case			):
														# padding
														__magic_name__						:						List[str]										=self._pad(
														    truncated_inputs[i]     ,			max_length=__snake_case     ,			padding_strategy=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			return_attention_mask=__snake_case     ,			)

														for key, value in outputs.items():
																		if key not in batch_outputs:
																						__magic_name__						:						Dict										=[]
																		if value.dtype is np.dtype(np.floataa			):
																						__magic_name__						:						Optional[int]										=value.astype(np.floataa			)
																		batch_outputs[key].append(__snake_case			)

										return BatchFeature(__snake_case     ,			tensor_type=__snake_case			)

						def      A__    (		self :Any     ,			__snake_case :Union[Dict[str, np.ndarray], BatchFeature]     ,			__snake_case :Optional[int] = None     ,			__snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Dict										=processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
														__magic_name__						:						Any										=len(__snake_case			)

										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__						:						Dict										=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__						:						List[Any]										=padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case			) < max_length

										if return_attention_mask and "attention_mask" not in processed_features:
														__magic_name__						:						int										=np.ones(len(__snake_case			)     ,			dtype=np.intaa			)

										if needs_to_be_padded:
														__magic_name__						:						List[Any]										=max_length - len(__snake_case			)
														if self.padding_side == "right":
																		if return_attention_mask:
																						__magic_name__						:						str										=np.pad(
																						    processed_features["""attention_mask"""]     ,			(0, difference)			)
																		__magic_name__						:						Tuple										=((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																		__magic_name__						:						str										=np.pad(
																		    __snake_case     ,			__snake_case     ,			"""constant"""     ,			constant_values=self.padding_value			)
														elif self.padding_side == "left":
																		if return_attention_mask:
																						__magic_name__						:						str										=np.pad(
																						    processed_features["""attention_mask"""]     ,			(difference, 0)			)
																		__magic_name__						:						Optional[int]										=((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																		__magic_name__						:						List[Any]										=np.pad(
																		    __snake_case     ,			__snake_case     ,			"""constant"""     ,			constant_values=self.padding_value			)
														else:
																		raise ValueError("""Invalid padding strategy:""" + str(self.padding_side			)			)

										return processed_features

						def      A__    (		self :Optional[Any]     ,			__snake_case :Union[Dict[str, np.ndarray], BatchFeature]     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			):
										'''simple docstring'''

										if not truncation:
														return processed_features
										elif truncation and max_length is None:
														raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""			)

										__magic_name__						:						Union[str, Any]										=processed_features[self.model_input_names[0]]

										# find `max_length` that fits `pad_to_multiple_of`
										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__						:						List[str]										=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__						:						Any										=len(__snake_case			) > max_length

										if needs_to_be_truncated:
														__magic_name__						:						List[Any]										=processed_features[self.model_input_names[0]][:max_length]
														if "attention_mask" in processed_features:
																		__magic_name__						:						List[str]										=processed_features["""attention_mask"""][:max_length]

										return processed_features


						def      A__    (		self :List[Any]     ,			__snake_case :str=False     ,			__snake_case :Optional[int]=None			):
										'''simple docstring'''

										if padding is not False:
														if padding is True:
																		__magic_name__						:						Union[str, Any]										=PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
														elif not isinstance(__snake_case     ,			__snake_case			):
																		__magic_name__						:						Optional[int]										=PaddingStrategy(__snake_case			)
														elif isinstance(__snake_case     ,			__snake_case			):
																		__magic_name__						:						Any										=padding
										else:
														__magic_name__						:						Any										=PaddingStrategy.DO_NOT_PAD

										# Set max length if needed
										if max_length is None:
														if padding_strategy == PaddingStrategy.MAX_LENGTH:
																		raise ValueError(
																		    f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"			)

        # Test if we have a padding value
										if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
														raise ValueError(
														    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
														    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""			)

										return padding_strategy




