


'''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


_SCREAMING_SNAKE_CASE							: List[str]										=			logging.get_logger(__name__)






class 		_snake_case     (					__SCREAMING_SNAKE_CASE		):






						'''simple docstring'''







						__snake_case				=   ["input_features", "is_longer"]







						def __init__(	self:						str   ,				__UpperCamelCase:						List[str]=64   ,				__UpperCamelCase:						Optional[int]=4_8000   ,				__UpperCamelCase:						int=480   ,				__UpperCamelCase:						Optional[Any]=10   ,				__UpperCamelCase:						Dict=1024   ,				__UpperCamelCase:						Tuple=0.0   ,				__UpperCamelCase:						Union[str, Any]=False   ,				__UpperCamelCase:						float = 0   ,				__UpperCamelCase:						float = 1_4000   ,				__UpperCamelCase:						int = None   ,				__UpperCamelCase:						str = "fusion"   ,				__UpperCamelCase:						str = "repeatpad"   ,				**__UpperCamelCase:						List[Any]   ,				)				->						Tuple:
										super().__init__(
										    feature_size=_SCREAMING_SNAKE_CASE   ,				sampling_rate=_SCREAMING_SNAKE_CASE   ,				padding_value=_SCREAMING_SNAKE_CASE   ,				return_attention_mask=_SCREAMING_SNAKE_CASE   ,				**_SCREAMING_SNAKE_CASE   ,				)
										__magic_name__					:       Optional[int]    							=							top_db
										__magic_name__					:       Dict    							=							truncation
										__magic_name__					:       Optional[Any]    							=							padding
										__magic_name__					:       List[str]    							=							fft_window_size
										__magic_name__					:       Dict    							=							(fft_window_size >> 1) + 1
										__magic_name__					:       Optional[int]    							=							hop_length
										__magic_name__					:       str    							=							max_length_s
										__magic_name__					:       Tuple    							=							max_length_s * sampling_rate
										__magic_name__					:       str    							=							sampling_rate
										__magic_name__					:       Optional[Any]    							=							frequency_min
										__magic_name__					:       str    							=							frequency_max
										__magic_name__					:       List[Any]    							=							mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,				num_mel_filters=_SCREAMING_SNAKE_CASE   ,				min_frequency=_SCREAMING_SNAKE_CASE   ,				max_frequency=_SCREAMING_SNAKE_CASE   ,				sampling_rate=_SCREAMING_SNAKE_CASE   ,				norm=_SCREAMING_SNAKE_CASE   ,				mel_scale="htk"   ,				)
										__magic_name__					:       Dict    							=							mel_filter_bank(
										    num_frequency_bins=self.nb_frequency_bins   ,				num_mel_filters=_SCREAMING_SNAKE_CASE   ,				min_frequency=_SCREAMING_SNAKE_CASE   ,				max_frequency=_SCREAMING_SNAKE_CASE   ,				sampling_rate=_SCREAMING_SNAKE_CASE   ,				norm="slaney"   ,				mel_scale="slaney"   ,				)







						def lowerCAmelCase__			(	self:						Any  )				->						Dict[str, Any]:
										__magic_name__					:       List[str]    							=							copy.deepcopy(self.__dict__  )
										__magic_name__					:       Optional[int]    							=							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:						Any   ,				__UpperCamelCase:						np.array   ,				__UpperCamelCase:						Optional[np.array] = None  )				->						np.ndarray:
										__magic_name__					:       Any    							=							spectrogram(
										    _SCREAMING_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=_SCREAMING_SNAKE_CASE   ,				log_mel="dB"   ,				)
										return log_mel_spectrogram.T







						def lowerCAmelCase__			(	self:						Optional[Any]   ,				__UpperCamelCase:						int   ,				__UpperCamelCase:						List[str]   ,				__UpperCamelCase:						Dict  )				->						List[str]:
										__magic_name__					:       Any    							=							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
														__magic_name__					:       Any    							=							[0]
										if len(ranges[2]  ) == 0:
														# if the audio is too short, we just use the first chunk
														__magic_name__					:       Union[str, Any]    							=							[0]
										# randomly choose index for each part
										__magic_name__					:       Union[str, Any]    							=							np.random.choice(ranges[0]  )
										__magic_name__					:       Optional[Any]    							=							np.random.choice(ranges[1]  )
										__magic_name__					:       Tuple    							=							np.random.choice(ranges[2]  )

										__magic_name__					:       int    							=							mel[idx_front : idx_front + chunk_frames, :]
										__magic_name__					:       Dict    							=							mel[idx_middle : idx_middle + chunk_frames, :]
										__magic_name__					:       int    							=							mel[idx_back : idx_back + chunk_frames, :]

										__magic_name__					:       Optional[int]    							=							torch.tensor(mel[None, None, :]  )
										__magic_name__					:       List[str]    							=							torch.nn.functional.interpolate(
										    _SCREAMING_SNAKE_CASE   ,				size=[chunk_frames, 64]   ,				mode="bilinear"   ,				align_corners=_SCREAMING_SNAKE_CASE  )
										__magic_name__					:       int    							=							mel_shrink[0][0].numpy()
										__magic_name__					:       Tuple    							=							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,				axis=0  )
										return mel_fusion







						def lowerCAmelCase__			(	self:						Union[str, Any]   ,				__UpperCamelCase:						np.array   ,				__UpperCamelCase:						List[Any]   ,				__UpperCamelCase:						str   ,				__UpperCamelCase:						Tuple  )				->						np.array:

										if waveform.shape[0] > max_length:
														if truncation == "rand_trunc":
																		__magic_name__					:       Optional[Any]    							=							True
																		# random crop to max_length (for compatibility) -> this should be handled by self.pad
																		__magic_name__					:       str    							=							len(_SCREAMING_SNAKE_CASE  ) - max_length
																		__magic_name__					:       str    							=							np.random.randint(0   ,				overflow + 1  )
																		__magic_name__					:       Dict    							=							waveform[idx : idx + max_length]
																		__magic_name__					:       Any    							=							self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE   ,				self.mel_filters_slaney  )[None, :]
														elif truncation == "fusion":
																		__magic_name__					:       Dict    							=							self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE   ,				self.mel_filters  )
																		__magic_name__					:       Dict    							=							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																		__magic_name__					:       Dict    							=							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.
																						__magic_name__					:       Optional[Any]    							=							np.stack([mel, mel, mel, mel]   ,				axis=0  )
																						__magic_name__					:       Union[str, Any]    							=							False
																		else:
																						__magic_name__					:       Tuple    							=							self._random_mel_fusion(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE  )
																						__magic_name__					:       Optional[Any]    							=							True
														else:
																		raise NotImplementedError(f"""data_truncating {truncation} not implemented"""  )

										else:
														__magic_name__					:       Optional[Any]    							=							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":
																						__magic_name__					:       Union[str, Any]    							=							int(max_length / len(_SCREAMING_SNAKE_CASE  )  )
																						__magic_name__					:       Tuple    							=							np.stack(np.tile(_SCREAMING_SNAKE_CASE   ,				n_repeat + 1  )  )[:max_length]
																		if padding == "repeatpad":
																						__magic_name__					:       Any    							=							int(max_length / len(_SCREAMING_SNAKE_CASE  )  )
																						__magic_name__					:       Tuple    							=							np.stack(np.tile(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE  )  )
																		__magic_name__					:       Dict    							=							np.pad(_SCREAMING_SNAKE_CASE   ,				(0, max_length - waveform.shape[0])   ,				mode="constant"   ,				constant_values=0  )

														if truncation == "fusion":
																		__magic_name__					:       Optional[int]    							=							self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE   ,				self.mel_filters  )
																		__magic_name__					:       Dict    							=							np.stack([input_mel, input_mel, input_mel, input_mel]   ,				axis=0  )
														else:
																		__magic_name__					:       Tuple    							=							self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE   ,				self.mel_filters_slaney  )[None, :]

										return input_mel, longer







						def __call__(	self:						Any   ,				__UpperCamelCase:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,				__UpperCamelCase:						str = None   ,				__UpperCamelCase:						Optional[str] = None   ,				__UpperCamelCase:						Optional[int] = None   ,				__UpperCamelCase:						Optional[int] = None   ,				__UpperCamelCase:						Optional[Union[str, TensorType]] = None   ,				**__UpperCamelCase:						Any   ,				)				->						BatchFeature:
										__magic_name__					:       List[str]    							=							truncation if truncation is not None else self.truncation
										__magic_name__					:       Tuple    							=							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."  )

										__magic_name__					:       int    							=							isinstance(_SCREAMING_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}"""  )
										__magic_name__					:       Optional[Any]    							=							is_batched_numpy or (
										    isinstance(_SCREAMING_SNAKE_CASE   ,				(list, tuple)  ) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)  ))
										)

										if is_batched:
														__magic_name__					:       List[str]    							=							[np.asarray(_SCREAMING_SNAKE_CASE   ,				dtype=np.floataa  ) for speech in raw_speech]
										elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE   ,				np.ndarray  ):
														__magic_name__					:       Dict    							=							np.asarray(_SCREAMING_SNAKE_CASE   ,				dtype=np.floataa  )
										elif isinstance(_SCREAMING_SNAKE_CASE   ,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
														__magic_name__					:       Union[str, Any]    							=							raw_speech.astype(np.floataa  )

										# always return batch
										if not is_batched:
														__magic_name__					:       str    							=							[np.asarray(_SCREAMING_SNAKE_CASE  )]

										# convert to mel spectrogram, truncate and pad if needed.
										__magic_name__					:       Optional[int]    							=							[
										    self._get_input_mel(_SCREAMING_SNAKE_CASE   ,				max_length if max_length else self.nb_max_samples   ,				_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE  )
										    for waveform in raw_speech
										]

										__magic_name__					:       Optional[int]    							=							[]
										__magic_name__					:       List[Any]    							=							[]
										for mel, longer in padded_inputs:
														input_mel.append(_SCREAMING_SNAKE_CASE  )
														is_longer.append(_SCREAMING_SNAKE_CASE  )

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

										if isinstance(input_mel[0]   ,				_SCREAMING_SNAKE_CASE  ):
														__magic_name__					:       Optional[int]    							=							[np.asarray(_SCREAMING_SNAKE_CASE   ,				dtype=np.floataa  ) for feature in input_mel]

										# is_longer is a list of bool
										__magic_name__					:       str    							=							[[longer] for longer in is_longer]

										__magic_name__					:       List[str]    							=							{"input_features": input_mel, "is_longer": is_longer}
										__magic_name__					:       Dict    							=							BatchFeature(_SCREAMING_SNAKE_CASE  )

										if return_tensors is not None:
														__magic_name__					:       Dict    							=							input_features.convert_to_tensors(_SCREAMING_SNAKE_CASE  )

										return input_features




"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
	from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case				:  List[Any] =       get_tests_dir('fixtures/test_sentencepiece.model')

__snake_case				:  Union[str, Any] =       {'target_lang': 'fi', 'source_lang': 'en'}
__snake_case				:  Union[str, Any] =       '>>zh<<'
__snake_case				:  List[str] =       'Helsinki-NLP/'

if is_torch_available():
	__snake_case				:  Optional[int] =       'pt'
elif is_tf_available():
	__snake_case				:  List[Any] =       'tf'
else:
	__snake_case				:  Union[str, Any] =       'jax'



@require_sentencepiece
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      MarianTokenizer
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      True

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Tuple:

				"""simple docstring"""

				super().setUp()
				__lowerCAmelCase :    Dict				=			["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
				__lowerCAmelCase :    Union[str, Any]				=			dict(zip(_SCREAMING_SNAKE_CASE			,     range(len(_SCREAMING_SNAKE_CASE))))
				__lowerCAmelCase :    int				=			Path(self.tmpdirname)
				save_json(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["vocab"])
				save_json(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"])
				if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
							copyfile(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["source_spm"])
							copyfile(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["target_spm"])

				__lowerCAmelCase :    Tuple				=			MarianTokenizer.from_pretrained(self.tmpdirname)
				tokenizer.save_pretrained(self.tmpdirname)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			MarianTokenizer:

				"""simple docstring"""

				return MarianTokenizer.from_pretrained(self.tmpdirname			,     **_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Dict:

				"""simple docstring"""

				return (
				    "This is a test",
				    "This is a test",
				)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			"</s>"
				__lowerCAmelCase :    int				=			0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE)			,     _SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			list(self.get_tokenizer().get_vocab().keys())

				self.assertEqual(vocab_keys[0]			,     "</s>")
				self.assertEqual(vocab_keys[1]			,     "<unk>")
				self.assertEqual(vocab_keys[-1]			,     "<pad>")
				self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     9)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Dict:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size			,     9)

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
				__lowerCAmelCase :    int				=			en_de_tokenizer(["I am a small frog"]			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			[38, 121, 14, 697, 3_8848, 0]
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     batch.input_ids[0])

				__lowerCAmelCase :    Tuple				=			tempfile.mkdtemp()
				en_de_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			[x.name for x in Path(_SCREAMING_SNAKE_CASE).glob("*")]
				self.assertIn("source.spm"			,     _SCREAMING_SNAKE_CASE)
				MarianTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.get_tokenizer()

				__lowerCAmelCase :    int				=			tok(
				    ["I am a small frog" * 1000, "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     truncation=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch.input_ids.shape			,     (2, 512))

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.get_tokenizer()
				__lowerCAmelCase :    str				=			tok(["I am a tiny frog", "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch_smaller.input_ids.shape			,     (2, 10))

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_SCREAMING_SNAKE_CASE			,     model_name="Helsinki-NLP/opus-mt-en-de"			,     revision="1a8c2263da11e68e50938f97e10cd57820bd504c"			,     decode_kwargs={"use_source_tokenizer": True}			,     )







	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

				__lowerCAmelCase :    List[str]				=			"Tämä on testi"
				__lowerCAmelCase :    int				=			"This is a test"

				__lowerCAmelCase :    Union[str, Any]				=			[76, 7, 2047, 2]
				__lowerCAmelCase :    Dict				=			[69, 12, 11, 940, 2]

				__lowerCAmelCase :    List[str]				=			tokenizer(_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    str				=			tokenizer(text_target=_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			tokenizer.decode(_SCREAMING_SNAKE_CASE			,     skip_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)