import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput





class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							process
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							params


						def __len__( self			:       Tuple		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       List[str]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.dataset[i]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.process(UpperCamelCase__					,	**self.params		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Dict					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]=None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							loader
												SCREAMING_SNAKE_CASE							:					List[Any]           =							infer
												SCREAMING_SNAKE_CASE							:					int           =							params
												if loader_batch_size == 1:
																		# Let's spare some time by deactivating altogether
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					List[str]           =							loader_batch_size

												# Internal bookkeeping
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					int           =							None


						def __len__( self			:       int		):





												'''simple docstring'''





												return len(self.loader		)


						def __iter__( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							iter(self.loader		)
												return self


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if isinstance(self._loader_batch_data					,	torch.Tensor		):
																		# Batch data is simple tensor, just fetch the slice
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._loader_batch_data[self._loader_batch_index]
												else:
																		# Batch data is assumed to be BaseModelOutput (or dict)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{}
																		for k, element in self._loader_batch_data.items():
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Convert ModelOutput to tuple first
																														SCREAMING_SNAKE_CASE							:					Dict           =							element.to_tuple()
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Dict           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(np.expand_dims(el[self._loader_batch_index]					,	0		) for el in element		)
																														continue
																								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Those are stored as lists of tensors so need specific unbatching.
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					List[str]           =							tuple(np.expand_dims(el[self._loader_batch_index]					,	0		) for el in element		)
																														continue
																								if element is None:
																														# This can happen for optional data that get passed around
																														SCREAMING_SNAKE_CASE							:					int           =							None
																								elif isinstance(element[self._loader_batch_index]					,	torch.Tensor		):
																														# Take correct batch data, but make it looked like batch_size=1
																														# For compatibility with other methods within transformers

																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							element[self._loader_batch_index].unsqueeze(0		)
																								elif isinstance(element[self._loader_batch_index]					,	np.ndarray		):
																														# Take correct batch data, but make it looked like batch_size=1
																														# For compatibility with other methods within transformers
																														SCREAMING_SNAKE_CASE							:					Tuple           =							np.expand_dims(element[self._loader_batch_index]					,	0		)
																								else:
																														# This is typically a list, so no need to `unsqueeze`.
																														SCREAMING_SNAKE_CASE							:					Tuple           =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																		SCREAMING_SNAKE_CASE							:					Any           =							self._loader_batch_data.__class__(UpperCamelCase__		)
												self._loader_batch_index += 1
												return result



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





												'''simple docstring'''





												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		# We are currently unrolling a batch so we just need to return
																		# the current item within a batch
																		return self.loader_batch_item()

												# We're out of items within a batch
												SCREAMING_SNAKE_CASE							:					Tuple           =							next(self.iterator		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.infer(UpperCamelCase__					,	**self.params		)
												# We now have a batch of "inferred things".
												if self.loader_batch_size is not None:
																		# Try to infer the size of the batch
																		if isinstance(UpperCamelCase__					,	torch.Tensor		):
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							processed
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							list(processed.keys()		)[0]
																								SCREAMING_SNAKE_CASE							:					int           =							processed[key]
																		if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																		else:
																								SCREAMING_SNAKE_CASE							:					Dict           =							first_tensor.shape[0]
																		if 0 < observed_batch_size < self.loader_batch_size:
																								# could be last batch so we can't unroll as many
																								# elements.
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							observed_batch_size
																		# Setting internal index to unwrap the batch
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																		SCREAMING_SNAKE_CASE							:					int           =							0
																		return self.loader_batch_item()
												else:
																		# We're not unrolling batches
																		return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]=None		):





												'''simple docstring'''





												super().__init__(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							iter(self.loader		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							None
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if self.subiterator is None:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.infer(next(self.iterator		)					,	**self.params		)
												try:
																		# Try to return next item
																		SCREAMING_SNAKE_CASE							:					Any           =							next(self.subiterator		)
												except StopIteration:
																		# When a preprocess iterator ends, we can start lookig at the next item
																		# ChunkIterator will keep feeding until ALL elements of iterator
																		# all have created their subiterator and have been iterating against.
																		#
																		# Another way to look at it, is we're basically flattening lists of lists
																		# into a single list, but with generators
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.infer(next(self.iterator		)					,	**self.params		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							next(self.subiterator		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __iter__( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							iter(self.loader		)
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							False
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		while self._loader_batch_index < self.loader_batch_size:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							self.loader_batch_item()
																								SCREAMING_SNAKE_CASE							:					Any           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
																								if is_last:
																														return accumulator

												while not is_last:
																		SCREAMING_SNAKE_CASE							:					Any           =							self.infer(next(self.iterator		)					,	**self.params		)
																		if self.loader_batch_size is not None:
																								if isinstance(UpperCamelCase__					,	torch.Tensor		):
																														SCREAMING_SNAKE_CASE							:					Tuple           =							processed
																								else:
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							list(processed.keys()		)[0]
																														SCREAMING_SNAKE_CASE							:					List[str]           =							processed[key]
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																								else:
																														SCREAMING_SNAKE_CASE							:					int           =							first_tensor.shape[0]
																								if 0 < observed_batch_size < self.loader_batch_size:
																														# could be last batch so we can't unroll as many
																														# elements.
																														SCREAMING_SNAKE_CASE							:					List[str]           =							observed_batch_size
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																								SCREAMING_SNAKE_CASE							:					str           =							0
																								while self._loader_batch_index < self.loader_batch_size:
																														SCREAMING_SNAKE_CASE							:					Any           =							self.loader_batch_item()
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							item.pop('''is_last'''		)
																														accumulator.append(UpperCamelCase__		)
																														if is_last:
																																				return accumulator
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							processed
																								SCREAMING_SNAKE_CASE							:					List[str]           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
												return accumulator






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Dict           =							key


						def __len__( self			:       Optional[int]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Dict					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return self.dataset[i][self.key]






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							dataset
												SCREAMING_SNAKE_CASE							:					List[str]           =							keya
												SCREAMING_SNAKE_CASE							:					Tuple           =							keya


						def __len__( self			:       List[str]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

__UpperCamelCase	: Dict    		=  random.Random()
def    A			(					_lowercase	,						_lowercase=1.0	,						_lowercase=None	,						_lowercase=None		):
						if rng is None:
												SCREAMING_SNAKE_CASE							:					Any           =							global_rng

						SCREAMING_SNAKE_CASE							:					int           =							[]
						for batch_idx in range(shape[0]		):
												values.append([]		)
												for _ in range(shape[1]		):
																		values[-1].append(rng.random() * scale		)

						return values





class       lowercase__		(     unittest.TestCase):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       str=7					,	UpperCamelCase__			:       Any=400					,	UpperCamelCase__			:       List[str]=2000					,	UpperCamelCase__			:       List[Any]=2048					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Union[str, Any]=1					,	UpperCamelCase__			:       List[Any]=512					,	UpperCamelCase__			:       str=30					,	UpperCamelCase__			:       Tuple=4_4100					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							parent
												SCREAMING_SNAKE_CASE							:					str           =							batch_size
												SCREAMING_SNAKE_CASE							:					str           =							min_seq_length
												SCREAMING_SNAKE_CASE							:					Dict           =							max_seq_length
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							spectrogram_length
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							feature_size
												SCREAMING_SNAKE_CASE							:					Tuple           =							num_audio_channels
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							hop_length
												SCREAMING_SNAKE_CASE							:					List[Any]           =							chunk_length
												SCREAMING_SNAKE_CASE							:					str           =							sampling_rate


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												return {
												    "spectrogram_length": self.spectrogram_length,
												    "feature_size": self.feature_size,
												    "num_audio_channels": self.num_audio_channels,
												    "hop_length": self.hop_length,
												    "chunk_length": self.chunk_length,
												    "sampling_rate": self.sampling_rate,
												}



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       Optional[int]=False		):





												'''simple docstring'''





												def _flatten(UpperCamelCase__			:       str		):
																		return list(itertools.chain(*UpperCamelCase__		)		)

												if equal_length:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
												else:
																		# make sure that inputs increase in size
																		SCREAMING_SNAKE_CASE							:					int           =							[
																		    floats_list((x, self.feature_size)		)
																		    for x in range(self.min_seq_length					,	self.max_seq_length					,	self.seq_length_diff		)
																		]
												if numpify:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[np.asarray(UpperCamelCase__		) for x in speech_inputs]
												return speech_inputs






@require_torch
@require_torchaudio
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	TvltFeatureExtractor


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							TvltFeatureExtractionTester(self		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.feature_extraction_class(**self.feat_extract_dict		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''spectrogram_length'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''feature_size'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''num_audio_channels'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''hop_length'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''chunk_length'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''sampling_rate'''		)		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feat_extract_first.save_pretrained(UpperCamelCase__		)[0]
																		check_json_file_has_correct_format(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							self.feature_extraction_class.from_pretrained(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feat_extract_first.to_dict()
												SCREAMING_SNAKE_CASE							:					str           =							feat_extract_second.to_dict()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict_first.pop('''mel_filters'''		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict_second.pop('''mel_filters'''		)
												self.assertTrue(np.allclose(UpperCamelCase__					,	UpperCamelCase__		)		)
												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							os.path.join(UpperCamelCase__					,	'''feat_extract.json'''		)
																		feat_extract_first.to_json_file(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.feature_extraction_class.from_json_file(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							feat_extract_first.to_dict()
												SCREAMING_SNAKE_CASE							:					str           =							feat_extract_second.to_dict()
												SCREAMING_SNAKE_CASE							:					int           =							dict_first.pop('''mel_filters'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							dict_second.pop('''mel_filters'''		)
												self.assertTrue(np.allclose(UpperCamelCase__					,	UpperCamelCase__		)		)
												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.feature_extraction_class(**self.feat_extract_dict		)

												# create three inputs of length 800, 1000, and 1200
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[floats_list((1, x)		)[0] for x in range(800					,	1400					,	200		)]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[np.asarray(UpperCamelCase__		) for speech_input in speech_inputs]

												# Test not batched input
												SCREAMING_SNAKE_CASE							:					List[Any]           =							feature_extractor(np_speech_inputs[0]					,	return_tensors='''np'''					,	sampling_rate=4_4100		).audio_values

												self.assertTrue(encoded_audios.ndim == 4		)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

												# Test batched
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feature_extractor(UpperCamelCase__					,	return_tensors='''np'''					,	sampling_rate=4_4100		).audio_values

												self.assertTrue(encoded_audios.ndim == 4		)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

												# Test audio masking
												SCREAMING_SNAKE_CASE							:					List[str]           =							feature_extractor(
												    UpperCamelCase__					,	return_tensors='''np'''					,	sampling_rate=4_4100					,	mask_audio=UpperCamelCase__		).audio_values

												self.assertTrue(encoded_audios.ndim == 4		)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

												# Test 2-D numpy arrays are batched.
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
												SCREAMING_SNAKE_CASE							:					Dict           =							np.asarray(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							feature_extractor(UpperCamelCase__					,	return_tensors='''np'''					,	sampling_rate=4_4100		).audio_values
												self.assertTrue(encoded_audios.ndim == 4		)
												self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
												self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
												self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,	'''clean'''					,	split='''validation'''		)
												# automatic decoding with librispeech
												SCREAMING_SNAKE_CASE							:					Dict           =							ds.sort('''id'''		).select(range(UpperCamelCase__		)		)[:num_samples]['''audio''']

												return [x["array"] for x in speech_samples]



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._load_datasamples(1		)
												SCREAMING_SNAKE_CASE							:					int           =							TvltFeatureExtractor()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							feature_extractor(UpperCamelCase__					,	return_tensors='''pt'''		).audio_values

												self.assertEquals(audio_values.shape					,	(1, 1, 192, 128)		)

												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
												self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,	UpperCamelCase__					,	atol=1E-4		)		)





