


"""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

A     :   Optional[int]			    =			random.Random()


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=1.0    , _UpperCamelCase=None    , _UpperCamelCase=None      ):

			'''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   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=7    ,   __a=4_00    ,   __a=20_00    ,   __a=20_48    ,   __a=1_28    ,   __a=1    ,   __a=5_12    ,   __a=30    ,   __a=4_41_00    ,   ):
										__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	):
										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	):
										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   _UpperCamelCase  (   __SCREAMING_SNAKE_CASE       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Dict				      =TvltFeatureExtractor

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			TvltFeatureExtractionTester(self	)

							def 			snake_case				(     self	):
										__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	):
										__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	):
										__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	):
										# 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 			snake_case				(     self    ,   __a	):
										__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	):
										__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_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	)	)






import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= {}

				__A   			= job["started_at"]
				__A   			= job["completed_at"]

				__A   			= date_parser.parse(a_						)
				__A   			= date_parser.parse(a_						)

				__A   			= round((end_datetime - start_datetime).total_seconds() / 60.0						)

				__A   			= start
				__A   			= end
				__A   			= duration_in_min

				return job_info


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	str:







				"""simple docstring"""



				__A   			= None
				if token is not None:
								__A   			= {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

				__A   			= F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
				__A   			= requests.get(a_		,   headers=a_						).json()
				__A   			= {}

				try:
								job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)
								__A   			= math.ceil((result["total_count"] - 1_0_0) / 1_0_0						)

								for i in range(a_						):
												__A   			= requests.get(url + F'''&page={i + 2}'''		,   headers=a_						).json()
												job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)

								return job_time
				except Exception:
								print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''						)

				return {}


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

					for k, v in job_time.items():
										print(f'''{k}: {v["duration"]}''')
