
"""simple docstring"""





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCamelCase_					:						List[str]				= logging.get_logger(__name__)



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				["input_features"]




			def __init__(				self						:					Optional[int]       ,   _snake_case						:					Tuple=80       ,   _snake_case						:					Any=16_000       ,   _snake_case						:					Tuple=160       ,   _snake_case						:					Optional[int]=30       ,   _snake_case						:					Dict=400       ,   _snake_case						:					int=0.0       ,   _snake_case						:					Optional[int]=False       ,   **_snake_case						:					Union[str, Any]       ,   )      -> List[Any]:







								"""simple docstring"""
								super().__init__(
								    feature_size=_snake_case       ,   sampling_rate=_snake_case       ,   padding_value=_snake_case       ,   return_attention_mask=_snake_case       ,   **_snake_case       ,   )
								A_				 =						n_fft
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						chunk_length * sampling_rate
								A_				 =						self.n_samples // hop_length
								A_				 =						sampling_rate
								A_				 =						mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2       ,   num_mel_filters=_snake_case       ,   min_frequency=0.0       ,   max_frequency=8_0_0_0.0       ,   sampling_rate=_snake_case       ,   norm="slaney"       ,   mel_scale="slaney"       ,   )




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					np.array	)      -> np.ndarray:







								"""simple docstring"""
								A_				 =						spectrogram(
								    _snake_case       ,   window_function(self.n_fft       ,   "hann"	)       ,   frame_length=self.n_fft       ,   hop_length=self.hop_length       ,   power=2.0       ,   mel_filters=self.mel_filters       ,   log_mel="log10"       ,   )
								A_				 =						log_spec[:, :-1]
								A_				 =						np.maximum(_snake_case       ,   log_spec.max() - 8.0	)
								A_				 =						(log_spec + 4.0) / 4.0
								return log_spec




			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def      lowerCamelCase__     (				_snake_case						:					List[np.ndarray]       ,   _snake_case						:					List[np.ndarray]       ,   _snake_case						:					float = 0.0	)      -> List[np.ndarray]:







								"""simple docstring"""
								if attention_mask is not None:
													A_				 =						np.array(_snake_case       ,   np.intaa	)
													A_				 =						[]

													for vector, length in zip(_snake_case       ,   attention_mask.sum(-1	)	):
																		A_				 =						(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
																		if length < normed_slice.shape[0]:
																							A_				 =						padding_value

																		normed_input_values.append(_snake_case	)
								else:
													A_				 =						[(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

								return normed_input_values




			def __call__(				self						:					Dict       ,   _snake_case						:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,   _snake_case						:					bool = True       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					Optional[Union[str, TensorType]] = None       ,   _snake_case						:					Optional[bool] = None       ,   _snake_case						:					Optional[str] = "max_length"       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					Optional[bool] = None       ,   **_snake_case						:					Tuple       ,   )      -> 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.__class__.__name__} was trained using a'
																		    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																		    F' was sampled with {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."	)

								A_				 =						isinstance(_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}'	)
								A_				 =						is_batched_numpy or (
								    isinstance(_snake_case       ,   (list, tuple)	) and (isinstance(raw_speech[0]       ,   (np.ndarray, tuple, list)	))
								)

								if is_batched:
													A_				 =						[np.asarray([speech]       ,   dtype=np.floataa	).T for speech in raw_speech]
								elif not is_batched and not isinstance(_snake_case       ,   np.ndarray	):
													A_				 =						np.asarray(_snake_case       ,   dtype=np.floataa	)
								elif isinstance(_snake_case       ,   np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
													A_				 =						raw_speech.astype(np.floataa	)

								# always return batch
								if not is_batched:
													A_				 =						[np.asarray([raw_speech]	).T]

								A_				 =						BatchFeature({"input_features": raw_speech}	)

								# convert into correct format for padding

								A_				 =						self.pad(
								    _snake_case       ,   padding=_snake_case       ,   max_length=max_length if max_length else self.n_samples       ,   truncation=_snake_case       ,   pad_to_multiple_of=_snake_case       ,   return_attention_mask=return_attention_mask or do_normalize       ,   )

								# zero-mean and unit-variance normalization
								if do_normalize:
													A_				 =						self.zero_mean_unit_var_norm(
													    padded_inputs["input_features"]       ,   attention_mask=padded_inputs["attention_mask"]       ,   padding_value=self.padding_value       ,   )
													A_				 =						np.stack(padded_inputs["input_features"]       ,   axis=0	)

								# make sure list is in array format
								A_				 =						padded_inputs.get("input_features"	).transpose(2       ,   0       ,   1	)

								A_				 =						[self._np_extract_fbank_features(_snake_case	) for waveform in input_features[0]]

								if isinstance(input_features[0]       ,   _snake_case	):
													A_				 =						[np.asarray(_snake_case       ,   dtype=np.floataa	) for feature in input_features]
								else:
													A_				 =						input_features

								if return_attention_mask:
													# rescale from sample (48000) to feature (3000)
													A_				 =						padded_inputs["attention_mask"][:, :: self.hop_length]

								if return_tensors is not None:
													A_				 =						padded_inputs.convert_to_tensors(_snake_case	)

								return padded_inputs






			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Dict[str, Any]:







								"""simple docstring"""
								A_				 =						copy.deepcopy(self.__dict__	)
								A_				 =						self.__class__.__name__
								if "mel_filters" in output:
													del output["mel_filters"]
								return output






"""simple docstring"""





import os

import pytest
from attr import dataclass


UpperCamelCase_					:						str				= '''us-east-1'''  # defaults region



@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				42
			snake_case	      =				"arn:aws:iam::558105141721:role/sagemaker_execution_role"
			snake_case	      =				{
			    "task_name": "mnli",
			    "per_device_train_batch_size": 16,
			    "per_device_eval_batch_size": 16,
			    "do_train": True,
			    "do_eval": True,
			    "do_predict": True,
			    "output_dir": "/opt/ml/model",
			    "overwrite_output_dir": True,
			    "max_steps": 5_00,
			    "save_steps": 55_00,
			}
			snake_case	      =				{**hyperparameters, "max_steps": 10_00}




			@property
			def      lowerCamelCase__     (				self						:					int	)      -> str:







								"""simple docstring"""
								if self.framework == "pytorch":
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
													    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
													]
								else:
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
													    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
													]




			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								return F'{self.framework}-transfromers-test'




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								return F'./tests/sagemaker/scripts/{self.framework}'






			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								if self.framework == "pytorch":
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
								else:
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"





@pytest.fixture(scope="class"				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						SageMakerTestEnvironment(framework=request.cls.framework				)




