
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

lowerCamelCase_						: Tuple         =  random.Random()







def        A__					(						lowerCamelCase     ,    lowerCamelCase=1.0     ,    lowerCamelCase=None     ,    lowerCamelCase=None						)  ->	Any:
				if rng is None:
								UpperCamelCase_:							List[str]	=						global_rng

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

				return values

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def __init__(       self :       Optional[int]						,	snake_case_ :       List[str]						,	snake_case_ :       Any=7						,	snake_case_ :       Optional[int]=400						,	snake_case_ :       Any=2000						,	snake_case_ :       Any=2048						,	snake_case_ :       List[Any]=128						,	snake_case_ :       Dict=1						,	snake_case_ :       str=512						,	snake_case_ :       Dict=30						,	snake_case_ :       Optional[int]=4_4100						,	):
							UpperCamelCase_:							str	=						parent
							UpperCamelCase_:							Tuple	=						batch_size
							UpperCamelCase_:							Dict	=						min_seq_length
							UpperCamelCase_:							Optional[int]	=						max_seq_length
							UpperCamelCase_:							Union[str, Any]	=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							UpperCamelCase_:							Union[str, Any]	=						spectrogram_length
							UpperCamelCase_:							Dict	=						feature_size
							UpperCamelCase_:							int	=						num_audio_channels
							UpperCamelCase_:							int	=						hop_length
							UpperCamelCase_:							Any	=						chunk_length
							UpperCamelCase_:							Optional[Any]	=						sampling_rate

			def       lowerCAmelCase__	(       self :       int      ):
							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       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Optional[Any]=False						,	snake_case_ :       int=False      ):
							def _flatten(snake_case_ :       Union[str, Any]      ):
											return list(itertools.chain(*snake_case_      )      )

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




@require_torch
@require_torchaudio
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[Any]					      = TvltFeatureExtractor

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Union[str, Any]	=						TvltFeatureExtractionTester(self      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.feature_extraction_class(**self.feat_extract_dict      )
							self.assertTrue(hasattr(snake_case_						,	"""spectrogram_length"""      )      )
							self.assertTrue(hasattr(snake_case_						,	"""feature_size"""      )      )
							self.assertTrue(hasattr(snake_case_						,	"""num_audio_channels"""      )      )
							self.assertTrue(hasattr(snake_case_						,	"""hop_length"""      )      )
							self.assertTrue(hasattr(snake_case_						,	"""chunk_length"""      )      )
							self.assertTrue(hasattr(snake_case_						,	"""sampling_rate"""      )      )

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

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							Optional[int]	=						feat_extract_first.save_pretrained(snake_case_      )[0]
											check_json_file_has_correct_format(snake_case_      )
											UpperCamelCase_:							Union[str, Any]	=						self.feature_extraction_class.from_pretrained(snake_case_      )

							UpperCamelCase_:							Any	=						feat_extract_first.to_dict()
							UpperCamelCase_:							List[str]	=						feat_extract_second.to_dict()
							UpperCamelCase_:							Any	=						dict_first.pop("""mel_filters"""      )
							UpperCamelCase_:							Optional[Any]	=						dict_second.pop("""mel_filters"""      )
							self.assertTrue(np.allclose(snake_case_						,	snake_case_      )      )
							self.assertEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						self.feature_extraction_class(**self.feat_extract_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							int	=						os.path.join(snake_case_						,	"""feat_extract.json"""      )
											feat_extract_first.to_json_file(snake_case_      )
											UpperCamelCase_:							Optional[Any]	=						self.feature_extraction_class.from_json_file(snake_case_      )

							UpperCamelCase_:							Any	=						feat_extract_first.to_dict()
							UpperCamelCase_:							str	=						feat_extract_second.to_dict()
							UpperCamelCase_:							Dict	=						dict_first.pop("""mel_filters"""      )
							UpperCamelCase_:							Any	=						dict_second.pop("""mel_filters"""      )
							self.assertTrue(np.allclose(snake_case_						,	snake_case_      )      )
							self.assertEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       int      ):
							# Initialize feature_extractor
							UpperCamelCase_:							Union[str, Any]	=						self.feature_extraction_class(**self.feat_extract_dict      )

							# create three inputs of length 800, 1000, and 1200
							UpperCamelCase_:							Dict	=						[floats_list((1, x)      )[0] for x in range(800						,	1400						,	200      )]
							UpperCamelCase_:							List[str]	=						[np.asarray(snake_case_      ) for speech_input in speech_inputs]

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

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




			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							List[str]	=						self._load_datasamples(1      )
							UpperCamelCase_:							str	=						TvltFeatureExtractor()
							UpperCamelCase_:							str	=						feature_extractor(snake_case_						,	return_tensors="""pt"""      ).audio_values

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

							UpperCamelCase_:							Dict	=						torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]      )
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,	snake_case_						,	atol=1e-4      )      )








import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""Testing 1, 2, 3"""

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	msg + """\n"""      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) as cl:
											# this action activates the env var
											logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""						,	cl.out      )

							# no need to restore as nothing was changed




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""      ):
											# nothing should be logged as env var disables this method
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	""""""      )

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""      ):
											# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






