

"""simple docstring"""





import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
							from transformers import WhisperFeatureExtractor

if is_torch_available():
							import torch

__UpperCamelCase  :      List[Any]							      =		random.Random()

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=1.0						,     A_=None						,     A_=None ):
	if rng is None:
		lowerCAmelCase__	:						List[Any]  				=	global_rng

	lowerCAmelCase__	:						Union[str, Any]  				=	[]
	for batch_idx in range(shape[0] ):
		values.append([] )
		for _ in range(shape[1] ):
			values[-1].append(rng.random() * scale )

	return values







@require_torch
@require_torchaudio
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Union[str, Any]=7   ,lowercase_			:				str=4_0_0   ,lowercase_			:				Union[str, Any]=2_0_0_0   ,lowercase_			:				Tuple=1_0   ,lowercase_			:				Dict=1_6_0   ,lowercase_			:				List[Any]=8   ,lowercase_			:				Tuple=0.0   ,lowercase_			:				Dict=4_0_0_0   ,lowercase_			:				List[str]=False   ,lowercase_			:				Union[str, Any]=True   ,):
						lowerCAmelCase__	:						Tuple  				=	parent
						lowerCAmelCase__	:						Dict  				=	batch_size
						lowerCAmelCase__	:						Tuple  				=	min_seq_length
						lowerCAmelCase__	:						List[Any]  				=	max_seq_length
						lowerCAmelCase__	:						List[str]  				=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						lowerCAmelCase__	:						Any  				=	padding_value
						lowerCAmelCase__	:						List[Any]  				=	sampling_rate
						lowerCAmelCase__	:						Optional[int]  				=	return_attention_mask
						lowerCAmelCase__	:						Union[str, Any]  				=	do_normalize
						lowerCAmelCase__	:						int  				=	feature_size
						lowerCAmelCase__	:						Dict  				=	chunk_length
						lowerCAmelCase__	:						str  				=	hop_length







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						return {
						    "feature_size": self.feature_size,
						    "hop_length": self.hop_length,
						    "chunk_length": self.chunk_length,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "return_attention_mask": self.return_attention_mask,
						    "do_normalize": self.do_normalize,
						}







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				List[str]=False   ,lowercase_			:				Tuple=False		):
						def _flatten(lowercase_			:				Dict		):
							return list(itertools.chain(*lowercase_		)		)

						if equal_length:
							lowerCAmelCase__	:						Optional[int]  				=	[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
						else:
							# make sure that inputs increase in size
							lowerCAmelCase__	:						Optional[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:
							lowerCAmelCase__	:						Any  				=	[np.asarray(lowercase_		) for x in speech_inputs]
						return speech_inputs







@require_torch
@require_torchaudio
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       WhisperFeatureExtractor if is_speech_available() else None







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	WhisperFeatureExtractionTester(self		)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.feature_extraction_class(**self.feat_extract_dict		)

						with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase__	:						Union[str, Any]  				=	feat_extract_first.save_pretrained(lowercase_		)[0]
							check_json_file_has_correct_format(lowercase_		)
							lowerCAmelCase__	:						List[str]  				=	self.feature_extraction_class.from_pretrained(lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	feat_extract_first.to_dict()
						lowerCAmelCase__	:						int  				=	feat_extract_second.to_dict()
						lowerCAmelCase__	:						Tuple  				=	feat_extract_first.mel_filters
						lowerCAmelCase__	:						List[str]  				=	feat_extract_second.mel_filters
						self.assertTrue(np.allclose(lowercase_   ,lowercase_		)		)
						self.assertEqual(lowercase_   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						List[str]  				=	self.feature_extraction_class(**self.feat_extract_dict		)

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

						lowerCAmelCase__	:						str  				=	feat_extract_first.to_dict()
						lowerCAmelCase__	:						Optional[Any]  				=	feat_extract_second.to_dict()
						lowerCAmelCase__	:						List[str]  				=	feat_extract_first.mel_filters
						lowerCAmelCase__	:						Tuple  				=	feat_extract_second.mel_filters
						self.assertTrue(np.allclose(lowercase_   ,lowercase_		)		)
						self.assertEqual(lowercase_   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						# Tests that all call wrap to encode_plus and batch_encode_plus
						lowerCAmelCase__	:						Dict  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						# create three inputs of length 800, 1000, and 1200
						lowerCAmelCase__	:						List[str]  				=	[floats_list((1, x)		)[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0		)]
						lowerCAmelCase__	:						Tuple  				=	[np.asarray(lowercase_		) for speech_input in speech_inputs]

						# Test feature size
						lowerCAmelCase__	:						List[str]  				=	feature_extractor(lowercase_   ,padding='''max_length'''   ,return_tensors='''np'''		).input_features
						self.assertTrue(input_features.ndim == 3		)
						self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames		)
						self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size		)

						# Test not batched input
						lowerCAmelCase__	:						str  				=	feature_extractor(speech_inputs[0]   ,return_tensors='''np'''		).input_features
						lowerCAmelCase__	:						str  				=	feature_extractor(np_speech_inputs[0]   ,return_tensors='''np'''		).input_features
						self.assertTrue(np.allclose(lowercase_   ,lowercase_   ,atol=1E-3		)		)

						# Test batched
						lowerCAmelCase__	:						str  				=	feature_extractor(lowercase_   ,return_tensors='''np'''		).input_features
						lowerCAmelCase__	:						List[Any]  				=	feature_extractor(lowercase_   ,return_tensors='''np'''		).input_features
						for enc_seq_a, enc_seq_a in zip(lowercase_   ,lowercase_		):
							self.assertTrue(np.allclose(lowercase_   ,lowercase_   ,atol=1E-3		)		)

						# Test 2-D numpy arrays are batched.
						lowerCAmelCase__	:						str  				=	[floats_list((1, x)		)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						lowerCAmelCase__	:						str  				=	np.asarray(lowercase_		)
						lowerCAmelCase__	:						Any  				=	feature_extractor(lowercase_   ,return_tensors='''np'''		).input_features
						lowerCAmelCase__	:						List[str]  				=	feature_extractor(lowercase_   ,return_tensors='''np'''		).input_features
						for enc_seq_a, enc_seq_a in zip(lowercase_   ,lowercase_		):
							self.assertTrue(np.allclose(lowercase_   ,lowercase_   ,atol=1E-3		)		)

						# Test truncation required
						lowerCAmelCase__	:						Tuple  				=	[floats_list((1, x)		)[0] for x in range(2_0_0   ,(feature_extractor.n_samples + 5_0_0)   ,2_0_0		)]
						lowerCAmelCase__	:						str  				=	[np.asarray(lowercase_		) for speech_input in speech_inputs]

						lowerCAmelCase__	:						str  				=	[x[: feature_extractor.n_samples] for x in speech_inputs]
						lowerCAmelCase__	:						List[Any]  				=	[np.asarray(lowercase_		) for speech_input in speech_inputs_truncated]

						lowerCAmelCase__	:						List[str]  				=	feature_extractor(lowercase_   ,return_tensors='''np'''		).input_features
						lowerCAmelCase__	:						Optional[Any]  				=	feature_extractor(lowercase_   ,return_tensors='''np'''		).input_features
						for enc_seq_a, enc_seq_a in zip(lowercase_   ,lowercase_		):
							self.assertTrue(np.allclose(lowercase_   ,lowercase_   ,atol=1E-3		)		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						import torch

						lowerCAmelCase__	:						str  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						lowerCAmelCase__	:						str  				=	np.random.rand(1_0_0   ,3_2		).astype(np.floataa		)
						lowerCAmelCase__	:						Optional[Any]  				=	np_speech_inputs.tolist()

						for inputs in [py_speech_inputs, np_speech_inputs]:
							lowerCAmelCase__	:						Any  				=	feature_extractor.pad([{'''input_features''': inputs}]   ,return_tensors='''np'''		)
							self.assertTrue(np_processed.input_features.dtype == np.floataa		)
							lowerCAmelCase__	:						List[str]  				=	feature_extractor.pad([{'''input_features''': inputs}]   ,return_tensors='''pt'''		)
							self.assertTrue(pt_processed.input_features.dtype == torch.floataa		)







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				Optional[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''   ,'''clean'''   ,split='''validation'''		)
						# automatic decoding with librispeech
						lowerCAmelCase__	:						int  				=	ds.sort('''id'''		).select(range(lowercase_		)		)[:num_samples]['''audio''']

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







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						# fmt: off
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor(
						    [
						        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
						        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
						        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
						        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
						    ]		)
						# fmt: on

						lowerCAmelCase__	:						Union[str, Any]  				=	self._load_datasamples(1		)
						lowerCAmelCase__	:						List[Any]  				=	WhisperFeatureExtractor()
						lowerCAmelCase__	:						Union[str, Any]  				=	feature_extractor(lowercase_   ,return_tensors='''pt'''		).input_features
						self.assertEqual(input_features.shape   ,(1, 8_0, 3_0_0_0)		)
						self.assertTrue(torch.allclose(input_features[0, 0, :3_0]   ,lowercase_   ,atol=1E-4		)		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						int  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						lowerCAmelCase__	:						List[Any]  				=	self._load_datasamples(1		)[0]
						lowerCAmelCase__	:						Union[str, Any]  				=	((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
						lowerCAmelCase__	:						List[str]  				=	feat_extract.zero_mean_unit_var_norm([audio]   ,attention_mask=lowercase_		)[0]

						self.assertTrue(np.all(np.mean(lowercase_		) < 1E-3		)		)
						self.assertTrue(np.all(np.abs(np.var(lowercase_		) - 1		) < 1E-3		)		)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




