import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





class       lowercase__		(     snake_case_			,							unittest.TestCase):
						UpperCamelCase_  =	CanineTokenizer
						UpperCamelCase_  =	False


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												super().setUp()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							CanineTokenizer()
												tokenizer.save_pretrained(self.tmpdirname		)


						@cached_property
						def  __A ( self			:       str		):





												'''simple docstring'''





												return CanineTokenizer.from_pretrained('''google/canine-s'''		)


						def  __A ( self			:       List[Any]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.tokenizer_class.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							1024
												return tokenizer


						@require_torch
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.canine_tokenizer
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
												# fmt: off
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
												# fmt: on
												SCREAMING_SNAKE_CASE							:					int           =							tokenizer(UpperCamelCase__					,	padding=UpperCamelCase__					,	return_tensors='''pt'''		)
												self.assertIsInstance(UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					int           =							list(batch.input_ids.numpy()[0]		)

												self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

												self.assertEqual((2, 39)					,	batch.input_ids.shape		)
												self.assertEqual((2, 39)					,	batch.attention_mask.shape		)


						@require_torch
						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.canine_tokenizer
												SCREAMING_SNAKE_CASE							:					List[Any]           =							['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
												SCREAMING_SNAKE_CASE							:					str           =							tokenizer(UpperCamelCase__					,	padding=UpperCamelCase__					,	return_tensors='''pt'''		)
												# check if input_ids, attention_mask and token_type_ids are returned
												self.assertIn('''input_ids'''					,	UpperCamelCase__		)
												self.assertIn('''attention_mask'''					,	UpperCamelCase__		)
												self.assertIn('''token_type_ids'''					,	UpperCamelCase__		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.canine_tokenizer
												SCREAMING_SNAKE_CASE							:					Dict           =							[
												    '''What\'s the weater?''',
												    '''It\'s about 25 degrees.''',
												]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer(
												    text_target=UpperCamelCase__					,	max_length=32					,	padding='''max_length'''					,	truncation=UpperCamelCase__					,	return_tensors='''pt'''		)
												self.assertEqual(32					,	targets['''input_ids'''].shape[1]		)


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.get_tokenizers()
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								self.assertNotEqual(tokenizer.model_max_length					,	42		)

        # Now let's start the test
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_tokenizers()
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								# Isolate this from the other tests because we save additional tokens/etc
																								SCREAMING_SNAKE_CASE							:					Tuple           =							tempfile.mkdtemp()

																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							''' He is very happy, UNwant\u00E9d,running'''
																								SCREAMING_SNAKE_CASE							:					str           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								tokenizer.save_pretrained(UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.__class__.from_pretrained(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							after_tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

																								shutil.rmtree(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizers(model_max_length=42		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								# Isolate this from the other tests because we save additional tokens/etc
																								SCREAMING_SNAKE_CASE							:					List[str]           =							tempfile.mkdtemp()

																								SCREAMING_SNAKE_CASE							:					str           =							''' He is very happy, UNwant\u00E9d,running'''

																								SCREAMING_SNAKE_CASE							:					int           =							tokenizer.additional_special_tokens

																								# We can add a new special token for Canine as follows:
																								SCREAMING_SNAKE_CASE							:					Dict           =							chr(0Xe0_07		)
																								additional_special_tokens.append(UpperCamelCase__		)
																								tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								tokenizer.save_pretrained(UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.__class__.from_pretrained(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							after_tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)
																								self.assertIn(UpperCamelCase__					,	after_tokenizer.additional_special_tokens		)
																								self.assertEqual(after_tokenizer.model_max_length					,	42		)

																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer.__class__.from_pretrained(UpperCamelCase__					,	model_max_length=43		)
																								self.assertEqual(tokenizer.model_max_length					,	43		)

																								shutil.rmtree(UpperCamelCase__		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_clean_sequence(UpperCamelCase__		)

																								# a special token for Canine can be defined as follows:
																								SCREAMING_SNAKE_CASE							:					Dict           =							0Xe0_05
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							chr(UpperCamelCase__		)

																								tokenizer.add_special_tokens({'''cls_token''': special_token}		)
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								self.assertEqual(len(UpperCamelCase__		)					,	1		)

																								SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.decode(ids + encoded_special_token					,	clean_up_tokenization_spaces=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					int           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								self.assertEqual(UpperCamelCase__					,	input_encoded + special_token_id		)

																								SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.decode(UpperCamelCase__					,	skip_special_tokens=UpperCamelCase__		)
																								self.assertTrue(special_token not in decoded		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							chr(0Xe0_05		)
																								SCREAMING_SNAKE_CASE							:					List[str]           =							chr(0Xe0_06		)

																								# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																								tokenizer.add_tokens([SPECIAL_TOKEN_1]					,	special_tokens=UpperCamelCase__		)
																								# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																								# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																								tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}		)

																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.tokenize(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					int           =							tokenizer.tokenize(UpperCamelCase__		)

																								self.assertEqual(len(UpperCamelCase__		)					,	1		)
																								self.assertEqual(len(UpperCamelCase__		)					,	1		)
																								self.assertEqual(token_a[0]					,	UpperCamelCase__		)
																								self.assertEqual(token_a[0]					,	UpperCamelCase__		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								# a special token for Canine can be defined as follows:
																								SCREAMING_SNAKE_CASE							:					str           =							0Xe0_06
																								SCREAMING_SNAKE_CASE							:					Tuple           =							chr(UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					List[str]           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__		)
																								tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}		)

																								with tempfile.TemporaryDirectory() as tmp_dir_name:
																														tokenizer.save_pretrained(UpperCamelCase__		)
																														tokenizer.from_pretrained(UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							[]
												if self.test_slow_tokenizer:
																		tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

												if self.test_rust_tokenizer:
																		tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

												for tokenizer_class, tokenizer_utils in tokenizer_list:
																		with tempfile.TemporaryDirectory() as tmp_dir:
																								tokenizer_utils.save_pretrained(UpperCamelCase__		)

																								with open(os.path.join(UpperCamelCase__					,	'''special_tokens_map.json'''		)					,	encoding='''utf-8'''		) as json_file:
																														SCREAMING_SNAKE_CASE							:					Dict           =							json.load(UpperCamelCase__		)

																								with open(os.path.join(UpperCamelCase__					,	'''tokenizer_config.json'''		)					,	encoding='''utf-8'''		) as json_file:
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							json.load(UpperCamelCase__		)

																								# a special token for Canine can be defined as follows:
																								SCREAMING_SNAKE_CASE							:					str           =							0Xe0_06
																								SCREAMING_SNAKE_CASE							:					List[str]           =							chr(UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					int           =							[new_token_a]
																								SCREAMING_SNAKE_CASE							:					int           =							[new_token_a]

																								with open(os.path.join(UpperCamelCase__					,	'''special_tokens_map.json'''		)					,	'''w'''					,	encoding='''utf-8'''		) as outfile:
																														json.dump(UpperCamelCase__					,	UpperCamelCase__		)
																								with open(os.path.join(UpperCamelCase__					,	'''tokenizer_config.json'''		)					,	'''w'''					,	encoding='''utf-8'''		) as outfile:
																														json.dump(UpperCamelCase__					,	UpperCamelCase__		)

																								# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																								# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																								# "special_tokens_map.json" files
																								SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer_class.from_pretrained(UpperCamelCase__					,	extra_ids=0		)
																								self.assertIn(UpperCamelCase__					,	tokenizer_without_change_in_init.additional_special_tokens		)
																								# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																								self.assertEqual(
																								    [new_token_a]					,	tokenizer_without_change_in_init.convert_ids_to_tokens(
																								        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]		)		)					,	)

																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							0Xe0_07
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							chr(UpperCamelCase__		)
																								# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																								SCREAMING_SNAKE_CASE							:					int           =							[AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__		)]
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer_class.from_pretrained(
																								    UpperCamelCase__					,	additional_special_tokens=UpperCamelCase__					,	extra_ids=0		)

																								self.assertIn(UpperCamelCase__					,	tokenizer.additional_special_tokens		)
																								# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																								self.assertEqual(
																								    [new_token_a]					,	tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]		)		)		)


						@require_tokenizers
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''hello world'''
																								if self.space_between_special_tokens:
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							'''[CLS] hello world [SEP]'''
																								else:
																														SCREAMING_SNAKE_CASE							:					Dict           =							input
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer.decode(UpperCamelCase__					,	spaces_between_special_tokens=self.space_between_special_tokens		)
																								self.assertIn(UpperCamelCase__					,	[output, output.lower()]		)


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_tokenizers()
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[
																								    '''bos_token''',
																								    '''eos_token''',
																								    '''unk_token''',
																								    '''sep_token''',
																								    '''pad_token''',
																								    '''cls_token''',
																								    '''mask_token''',
																								]

												SCREAMING_SNAKE_CASE							:					Tuple           =							'''a'''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							ord(UpperCamelCase__		)

												for attr in attributes_list:
																		setattr(UpperCamelCase__					,	attr + '''_id'''					,	UpperCamelCase__		)
																		self.assertEqual(getattr(UpperCamelCase__					,	UpperCamelCase__		)					,	UpperCamelCase__		)
																		self.assertEqual(getattr(UpperCamelCase__					,	attr + '''_id'''		)					,	UpperCamelCase__		)

																		setattr(UpperCamelCase__					,	attr + '''_id'''					,	UpperCamelCase__		)
																		self.assertEqual(getattr(UpperCamelCase__					,	UpperCamelCase__		)					,	UpperCamelCase__		)
																		self.assertEqual(getattr(UpperCamelCase__					,	attr + '''_id'''		)					,	UpperCamelCase__		)

												setattr(UpperCamelCase__					,	'''additional_special_tokens_ids'''					,	[]		)
												self.assertListEqual(getattr(UpperCamelCase__					,	'''additional_special_tokens'''		)					,	[]		)
												self.assertListEqual(getattr(UpperCamelCase__					,	'''additional_special_tokens_ids'''		)					,	[]		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							0Xe0_06
												SCREAMING_SNAKE_CASE							:					Dict           =							chr(UpperCamelCase__		)
												setattr(UpperCamelCase__					,	'''additional_special_tokens_ids'''					,	[additional_special_token_id]		)
												self.assertListEqual(getattr(UpperCamelCase__					,	'''additional_special_tokens'''		)					,	[additional_special_token]		)
												self.assertListEqual(getattr(UpperCamelCase__					,	'''additional_special_tokens_ids'''		)					,	[additional_special_token_id]		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												pass


						def  __A ( self			:       str		):





												'''simple docstring'''





												pass


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												pass


						def  __A ( self			:       int		):





												'''simple docstring'''





												pass


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												pass


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





												'''simple docstring'''





												pass


						def  __A ( self			:       int		):





												'''simple docstring'''





												pass



						def  __A ( self			:       Any		):





												'''simple docstring'''





												pass






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	: Union[str, Any]    		=  logging.get_logger(__name__)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""input_features""", """is_longer"""]


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dict=64					,	UpperCamelCase__			:       Optional[Any]=4_8000					,	UpperCamelCase__			:       Tuple=480					,	UpperCamelCase__			:       Union[str, Any]=10					,	UpperCamelCase__			:       List[Any]=1024					,	UpperCamelCase__			:       Tuple=0.0					,	UpperCamelCase__			:       int=False					,	UpperCamelCase__			:       float = 0					,	UpperCamelCase__			:       float = 1_4000					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       str = "fusion"					,	UpperCamelCase__			:       str = "repeatpad"					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												super().__init__(
												    feature_size=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	padding_value=UpperCamelCase__					,	return_attention_mask=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							top_db
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							truncation
												SCREAMING_SNAKE_CASE							:					str           =							padding
												SCREAMING_SNAKE_CASE							:					List[Any]           =							fft_window_size
												SCREAMING_SNAKE_CASE							:					Tuple           =							(fft_window_size >> 1) + 1
												SCREAMING_SNAKE_CASE							:					List[str]           =							hop_length
												SCREAMING_SNAKE_CASE							:					List[Any]           =							max_length_s
												SCREAMING_SNAKE_CASE							:					Tuple           =							max_length_s * sampling_rate
												SCREAMING_SNAKE_CASE							:					List[Any]           =							sampling_rate
												SCREAMING_SNAKE_CASE							:					List[str]           =							frequency_min
												SCREAMING_SNAKE_CASE							:					Any           =							frequency_max
												SCREAMING_SNAKE_CASE							:					List[Any]           =							mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins					,	num_mel_filters=UpperCamelCase__					,	min_frequency=UpperCamelCase__					,	max_frequency=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	norm=UpperCamelCase__					,	mel_scale='''htk'''					,	)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							mel_filter_bank(
												    num_frequency_bins=self.nb_frequency_bins					,	num_mel_filters=UpperCamelCase__					,	min_frequency=UpperCamelCase__					,	max_frequency=UpperCamelCase__					,	sampling_rate=UpperCamelCase__					,	norm='''slaney'''					,	mel_scale='''slaney'''					,	)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							copy.deepcopy(self.__dict__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       np.array					,	UpperCamelCase__			:       Optional[np.array] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							spectrogram(
												    UpperCamelCase__					,	window_function(self.fft_window_size					,	'''hann'''		)					,	frame_length=self.fft_window_size					,	hop_length=self.hop_length					,	power=2.0					,	mel_filters=UpperCamelCase__					,	log_mel='''dB'''					,	)
												return log_mel_spectrogram.T


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[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
																		SCREAMING_SNAKE_CASE							:					int           =							[0]
												if len(ranges[2]		) == 0:
																		# if the audio is too short, we just use the first chunk
																		SCREAMING_SNAKE_CASE							:					Any           =							[0]
												# randomly choose index for each part
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.random.choice(ranges[0]		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							np.random.choice(ranges[1]		)
												SCREAMING_SNAKE_CASE							:					int           =							np.random.choice(ranges[2]		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							mel[idx_front : idx_front + chunk_frames, :]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							mel[idx_middle : idx_middle + chunk_frames, :]
												SCREAMING_SNAKE_CASE							:					Tuple           =							mel[idx_back : idx_back + chunk_frames, :]

												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor(mel[None, None, :]		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.nn.functional.interpolate(
												    UpperCamelCase__					,	size=[chunk_frames, 64]					,	mode='''bilinear'''					,	align_corners=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							mel_shrink[0][0].numpy()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,	axis=0		)
												return mel_fusion


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.array					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





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

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

																		if truncation == "fusion":
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters		)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.stack([input_mel, input_mel, input_mel, input_mel]					,	axis=0		)
																		else:
																								SCREAMING_SNAKE_CASE							:					List[str]           =							self._np_extract_fbank_features(UpperCamelCase__					,	self.mel_filters_slaney		)[None, :]

												return input_mel, longer



						def __call__( self			:       Union[str, 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					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							truncation if truncation is not None else self.truncation
												SCREAMING_SNAKE_CASE							:					List[str]           =							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.'''		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							isinstance(UpperCamelCase__					,	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}"""		)
												SCREAMING_SNAKE_CASE							:					int           =							is_batched_numpy or (
												    isinstance(UpperCamelCase__					,	(list, tuple)		) and (isinstance(raw_speech[0]					,	(np.ndarray, tuple, list)		))
												)

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

												# always return batch
												if not is_batched:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[np.asarray(UpperCamelCase__		)]

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

												SCREAMING_SNAKE_CASE							:					str           =							[]
												SCREAMING_SNAKE_CASE							:					List[str]           =							[]
												for mel, longer in padded_inputs:
																		input_mel.append(UpperCamelCase__		)
																		is_longer.append(UpperCamelCase__		)

												if truncation == "fusion" and sum(UpperCamelCase__		) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.random.randint(0					,	len(UpperCamelCase__		)		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							True

												if isinstance(input_mel[0]					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[np.asarray(UpperCamelCase__					,	dtype=np.floataa		) for feature in input_mel]

												# is_longer is a list of bool
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[[longer] for longer in is_longer]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{'''input_features''': input_mel, '''is_longer''': is_longer}
												SCREAMING_SNAKE_CASE							:					int           =							BatchFeature(UpperCamelCase__		)

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							input_features.convert_to_tensors(UpperCamelCase__		)

												return input_features





