



'''simple docstring'''

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     		=   random.Random()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]=1.0							,		_SCREAMING_SNAKE_CASE					:						Dict=None							,		_SCREAMING_SNAKE_CASE					:						Tuple=None							)      ->						Union[str, Any]:







	"""simple docstring"""
	if rng is None:
		lowerCAmelCase   =     global_rng

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

	return values

class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=7     ,	A_=400     ,	A_=2000     ,	A_=2048     ,	A_=128     ,	A_=1     ,	A_=512     ,	A_=30     ,	A_=4_4100     ,	)     ->     int:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     min_seq_length
							lowerCAmelCase   =     max_seq_length
							lowerCAmelCase   =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCAmelCase   =     spectrogram_length
							lowerCAmelCase   =     feature_size
							lowerCAmelCase   =     num_audio_channels
							lowerCAmelCase   =     hop_length
							lowerCAmelCase   =     chunk_length
							lowerCAmelCase   =     sampling_rate


						def     __snake_case   ( self				)     ->     Dict:
							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     __snake_case   ( self     ,	A_=False     ,	A_=False				)     ->     Any:
							def _flatten(A_				):
								return list(itertools.chain(*A_				)				)

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







@require_torch
@require_torchaudio
class __snake_case(				_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		TvltFeatureExtractor


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     TvltFeatureExtractionTester(self				)


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     self.feature_extraction_class(**self.feat_extract_dict				)
							self.assertTrue(hasattr(A_     ,	"""spectrogram_length"""				)				)
							self.assertTrue(hasattr(A_     ,	"""feature_size"""				)				)
							self.assertTrue(hasattr(A_     ,	"""num_audio_channels"""				)				)
							self.assertTrue(hasattr(A_     ,	"""hop_length"""				)				)
							self.assertTrue(hasattr(A_     ,	"""chunk_length"""				)				)
							self.assertTrue(hasattr(A_     ,	"""sampling_rate"""				)				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.feature_extraction_class(**self.feat_extract_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
								lowerCAmelCase   =     feat_extract_first.save_pretrained(A_				)[0]
								check_json_file_has_correct_format(A_				)
								lowerCAmelCase   =     self.feature_extraction_class.from_pretrained(A_				)

							lowerCAmelCase   =     feat_extract_first.to_dict()
							lowerCAmelCase   =     feat_extract_second.to_dict()
							lowerCAmelCase   =     dict_first.pop("""mel_filters"""				)
							lowerCAmelCase   =     dict_second.pop("""mel_filters"""				)
							self.assertTrue(np.allclose(A_     ,	A_				)				)
							self.assertEqual(A_     ,	A_				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.feature_extraction_class(**self.feat_extract_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
								lowerCAmelCase   =     os.path.join(A_     ,	"""feat_extract.json"""				)
								feat_extract_first.to_json_file(A_				)
								lowerCAmelCase   =     self.feature_extraction_class.from_json_file(A_				)

							lowerCAmelCase   =     feat_extract_first.to_dict()
							lowerCAmelCase   =     feat_extract_second.to_dict()
							lowerCAmelCase   =     dict_first.pop("""mel_filters"""				)
							lowerCAmelCase   =     dict_second.pop("""mel_filters"""				)
							self.assertTrue(np.allclose(A_     ,	A_				)				)
							self.assertEqual(A_     ,	A_				)


						def     __snake_case   ( self				)     ->     int:
							# Initialize feature_extractor
							lowerCAmelCase   =     self.feature_extraction_class(**self.feat_extract_dict				)

							# create three inputs of length 800, 1000, and 1200
							lowerCAmelCase   =     [floats_list((1, x)				)[0] for x in range(800     ,	1400     ,	200				)]
							lowerCAmelCase   =     [np.asarray(A_				) for speech_input in speech_inputs]

							# Test not batched input
							lowerCAmelCase   =     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
							lowerCAmelCase   =     feature_extractor(A_     ,	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
							lowerCAmelCase   =     feature_extractor(
							    A_     ,	return_tensors="""np"""     ,	sampling_rate=4_4100     ,	mask_audio=A_				).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.
							lowerCAmelCase   =     [floats_list((1, x)				)[0] for x in (800, 800, 800)]
							lowerCAmelCase   =     np.asarray(A_				)
							lowerCAmelCase   =     feature_extractor(A_     ,	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     __snake_case   ( self     ,	A_				)     ->     List[Any]:
							lowerCAmelCase   =     load_dataset("""hf-internal-testing/librispeech_asr_dummy"""     ,	"""clean"""     ,	split="""validation"""				)
							# automatic decoding with librispeech
							lowerCAmelCase   =     ds.sort("""id"""				).select(range(A_				)				)[:num_samples]["""audio"""]

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




						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self._load_datasamples(1				)
							lowerCAmelCase   =     TvltFeatureExtractor()
							lowerCAmelCase   =     feature_extractor(A_     ,	return_tensors="""pt"""				).audio_values

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

							lowerCAmelCase   =     torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]				)
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,	A_     ,	atol=1e-4				)				)




'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers.generation import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)

@require_torch
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     3
							lowerCAmelCase   =     250

							lowerCAmelCase   =     ids_tensor((batch_size, length)     ,	A_				)
							lowerCAmelCase   =     torch.ones((batch_size, length)     ,	device=A_     ,	dtype=torch.float				) / length
							return input_ids, scores


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10				),
							        MaxTimeCriteria(max_time=0.1				),
							    ]				)

							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     MaxLengthCriteria(max_length=10				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     MaxNewTokensCriteria(start_length=5     ,	max_new_tokens=5				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     StoppingCriteriaList([criteria]				)
							self.assertEqual(criteria_list.max_length     ,	10				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1     ,	initial_timestamp=time.time() - 0.2				)
							self.assertTrue(criteria(A_     ,	A_				)				)




						def     __snake_case   ( self				)     ->     Optional[int]:
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	10				)

							with self.assertWarns(A_				):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	11				)

							lowerCAmelCase   =     validate_stopping_criteria(StoppingCriteriaList()     ,	11				)

							self.assertEqual(len(A_				)     ,	1				)