
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase__   :					str												=				logging.get_logger(__name__)



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		int       =     ['''input_features''', '''attention_mask''']



				def __init__(self     , SCREAMING_SNAKE_CASE__=80     , SCREAMING_SNAKE_CASE__=1_60_00     , SCREAMING_SNAKE_CASE__=80     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(feature_size=SCREAMING_SNAKE_CASE__     , sampling_rate=SCREAMING_SNAKE_CASE__     , padding_value=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     num_mel_bins
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     do_ceptral_normalize
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     normalize_means
											SCREAMING_SNAKE_CASE__		: int     =     normalize_vars
											SCREAMING_SNAKE_CASE__		: Any     =     True



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , )      ->	np.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     torch.from_numpy(SCREAMING_SNAKE_CASE__					).unsqueeze(0					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     ta_kaldi.fbank(SCREAMING_SNAKE_CASE__     , num_mel_bins=self.num_mel_bins     , sample_frequency=self.sampling_rate					)
											return features.numpy()



				@staticmethod
				def 			__magic_name__					(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = 0.0     , )      ->	np.ndarray:



											"""simple docstring"""




											if normalize_means:
																		SCREAMING_SNAKE_CASE__		: Dict     =     x[:input_length].mean(axis=0					)
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     np.subtract(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											if normalize_vars:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     x[:input_length].std(axis=0					)
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     np.divide(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											if input_length < x.shape[0]:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     padding_value

											# make sure array is in float32
											SCREAMING_SNAKE_CASE__		: List[Any]     =     x.astype(np.floataa					)

											return x



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None					)      ->	List[np.ndarray]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
											return [
											    self.utterance_cmvn(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , self.normalize_means     , self.normalize_vars     , self.padding_value					)
											    for x, n in zip(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											]



				def __call__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	BatchFeature:



											"""simple docstring"""




											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																									    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																									    F''' {self.sampling_rate} and not {sampling_rate}.'''					)
											else:
																		logger.warning(
																		    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
																		    """Failing to do so can result in silent errors that might be hard to debug."""					)

											SCREAMING_SNAKE_CASE__		: Any     =     isinstance(SCREAMING_SNAKE_CASE__     , np.ndarray					) and len(raw_speech.shape					) > 1
											if is_batched_numpy and len(raw_speech.shape					) > 2:
																		raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     is_batched_numpy or (
											    isinstance(SCREAMING_SNAKE_CASE__     , (list, tuple)					) and (isinstance(raw_speech[0]     , (np.ndarray, tuple, list)					))
											)

											if is_batched:
																		SCREAMING_SNAKE_CASE__		: str     =     [np.asarray(SCREAMING_SNAKE_CASE__     , dtype=np.floataa					) for speech in raw_speech]
											elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE__     , np.ndarray					):
																		SCREAMING_SNAKE_CASE__		: Dict     =     np.asarray(SCREAMING_SNAKE_CASE__     , dtype=np.floataa					)
											elif isinstance(SCREAMING_SNAKE_CASE__     , np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     raw_speech.astype(np.floataa					)

											# always return batch
											if not is_batched:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     [raw_speech]

											# extract fbank features
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [self._extract_fbank_features(SCREAMING_SNAKE_CASE__					) for waveform in raw_speech]

											# convert into correct format for padding
											SCREAMING_SNAKE_CASE__		: str     =     BatchFeature({"""input_features""": features}					)

											SCREAMING_SNAKE_CASE__		: int     =     self.pad(
											    SCREAMING_SNAKE_CASE__     , padding=SCREAMING_SNAKE_CASE__     , max_length=SCREAMING_SNAKE_CASE__     , truncation=SCREAMING_SNAKE_CASE__     , pad_to_multiple_of=SCREAMING_SNAKE_CASE__     , return_attention_mask=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__     , )

											# make sure list is in array format
											SCREAMING_SNAKE_CASE__		: Any     =     padded_inputs.get("""input_features"""					)
											if isinstance(input_features[0]     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [np.asarray(SCREAMING_SNAKE_CASE__     , dtype=np.floataa					) for feature in input_features]

											SCREAMING_SNAKE_CASE__		: List[Any]     =     padded_inputs.get("""attention_mask"""					)
											if attention_mask is not None:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     [np.asarray(SCREAMING_SNAKE_CASE__     , dtype=np.intaa					) for array in attention_mask]

											# Utterance-level cepstral mean and variance normalization
											if self.do_ceptral_normalize:
																		SCREAMING_SNAKE_CASE__		: str     =     (
																		    np.array(SCREAMING_SNAKE_CASE__     , dtype=np.intaa					)
																		    if self._get_padding_strategies(SCREAMING_SNAKE_CASE__     , max_length=SCREAMING_SNAKE_CASE__					) is not PaddingStrategy.DO_NOT_PAD
																		    else None
																		)
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.normalize(
																		    padded_inputs["""input_features"""]     , attention_mask=SCREAMING_SNAKE_CASE__					)

											if return_tensors is not None:
																		SCREAMING_SNAKE_CASE__		: int     =     padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__					)

											return padded_inputs






"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def lowercase_						(     _snake_case     ,_snake_case=7		):
							SCREAMING_SNAKE_CASE__		: Dict     =     None
							if token is not None:
														SCREAMING_SNAKE_CASE__		: str     =     {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

							# The id of a workflow (not of a workflow run)
							SCREAMING_SNAKE_CASE__		: List[str]     =     """636036"""

							SCREAMING_SNAKE_CASE__		: Any     =     f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
							# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
							url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

							SCREAMING_SNAKE_CASE__		: str     =     requests.get(_snake_case     ,headers=_snake_case		).json()

							return result["workflow_runs"]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     get_daily_ci_runs(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     workflow_run["""id"""]
																					break

							return workflow_run_id

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_last_daily_ci_runs(_snake_case		)
							if workflow_run_id is not None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     get_artifacts_links(worflow_run_id=_snake_case     ,token=_snake_case		)
														for artifact_name in artifact_names:
																					if artifact_name in artifacts_links:
																												SCREAMING_SNAKE_CASE__		: Tuple     =     artifacts_links[artifact_name]
																												download_artifact(
																												    artifact_name=_snake_case     ,artifact_url=_snake_case     ,output_dir=_snake_case     ,token=_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							get_last_daily_ci_artifacts(_snake_case     ,_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     {}
							for artifact_name in artifact_names:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(_snake_case     ,f'''{artifact_name}.zip'''		)
														if os.path.isfile(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     {}
																					with zipfile.ZipFile(_snake_case		) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_snake_case		):
																																										# read the file
																																										with z.open(_snake_case		) as f:
																																																	SCREAMING_SNAKE_CASE__		: List[Any]     =     f.read().decode("""UTF-8"""		)

							return results




