




'''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       (					A			,   A=1.0			,   A=None			,   A=None	)						->   Any:

							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 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=7 ,   lowerCamelCase_=4_00 ,   lowerCamelCase_=20_00 ,   lowerCamelCase_=20_48 ,   lowerCamelCase_=1_28 ,   lowerCamelCase_=1 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=30 ,   lowerCamelCase_=4_41_00 ,   )	->							List[Any]:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=False ,   lowerCamelCase_=False       )	->							List[Any]:
														def _flatten(lowerCamelCase_       ):
																					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 		a__    (							__lowercase    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				TvltFeatureExtractor







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     TvltFeatureExtractionTester(self       )







							def   __SCREAMING_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   __SCREAMING_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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														# 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(8_00 ,   14_00 ,   2_00       )]
														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_41_00       ).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_41_00       ).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_41_00 ,   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 (8_00, 8_00, 8_00)]
														lowerCAmelCase__	       =     np.asarray(_a       )
														lowerCAmelCase__	       =     feature_extractor(_a ,   return_tensors='''np''' ,   sampling_rate=4_41_00       ).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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Optional[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   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     self._load_datasamples(1       )
														lowerCAmelCase__	       =     TvltFeatureExtractor()
														lowerCAmelCase__	       =     feature_extractor(_a ,   return_tensors='''pt'''       ).audio_values

														self.assertEquals(audio_values.shape ,   (1, 1, 1_92, 1_28)       )

														lowerCAmelCase__	       =     torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]       )
														self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,   _a ,   atol=1e-4       )       )



'''simple docstring'''






import argparse
import json
import subprocess


def  _snake_case       (					A			,   A	)						->   Tuple:
							lowerCAmelCase__	       =     []

							lowerCAmelCase__	       =     (
							    F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
							    ''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
							)
							lowerCAmelCase__	       =     subprocess.run(A			,   shell=A			,   stdout=subprocess.PIPE	)
							lowerCAmelCase__	       =     output.stdout.decode('''utf-8'''	)
							lowerCAmelCase__	       =     json.loads(A	)

							lowerCAmelCase__	       =     status['''runners''']
							for runner in runners:
														if runner["name"] in target_runners:
																					if runner["status"] == "offline":
																												offline_runners.append(A	)

    # save the result so we can report them on Slack
							with open('''offline_runners.txt'''			,   '''w'''	) as fp:
														fp.write(json.dumps(A	)	)

							if len(A	) > 0:
														lowerCAmelCase__	       =     '''\n'''.join([x['''name'''] for x in offline_runners]	)
														raise ValueError(F"""The following runners are offline:\n{failed}"""	)


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   Optional[Any]:
									return values.split(''','''	)

		__UpperCAmelCase       =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--target_runners''',
		    default=None,
		    type=list_str,
		    required=True,
		    help='''Comma-separated list of runners to check status.''',
		)

		parser.add_argument(
		    '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
		)
		__UpperCAmelCase       =							parser.parse_args()






		get_runner_status(args.target_runners, args.token)