



"""simple docstring"""







import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
			from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
			UpperCamelCase     =							True
			from torch.cuda.amp import autocast


UpperCamelCase     =							logging.getLogger(__name__)






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					str							  =     field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
			A__	:					Optional[str]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,		)
			A__	:					Optional[bool]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "Whether to freeze the feature extractor layers of the model."}      )
			A__	:					Optional[bool]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "Whether to log verbose messages or not."}       ,		)
			A__	:					Optional[float]							  =     field(
			    default=2.0       ,		metadata={"help": "Maximum temperature for gumbel softmax."}      )
			A__	:					Optional[float]							  =     field(
			    default=0.5       ,		metadata={"help": "Minimum temperature for gumbel softmax."}      )
			A__	:					Optional[float]							  =     field(
			    default=0.999_995       ,		metadata={"help": "Decay of gumbel temperature during training."}      )



def       _lowerCamelCase				(							UpperCAmelCase_ :       ModelArguments,	UpperCAmelCase_ :       TrainingArguments     )	-> Optional[int]:






	"""simple docstring"""





	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",	datefmt="%m/%d/%Y %H:%M:%S",	handlers=[logging.StreamHandler(sys.stdout     )],	)
	A__											=					logging.WARNING
	if model_args.verbose_logging:
		A__											=					logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank     ):
		A__											=					logging.INFO
	logger.setLevel(UpperCAmelCase_     )






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					str							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "The name of the dataset to use (via the datasets library)."}      )
			A__	:					Optional[str]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
			A__	:					Optional[str]							  =     field(
			    default="train"       ,		metadata={
			        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
			    }       ,		)
			A__	:					Optional[str]							  =     field(
			    default="validation"       ,		metadata={
			        "help": (
			            "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
			        )
			    }       ,		)
			A__	:					Optional[str]							  =     field(
			    default="file"       ,		metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}       ,		)
			A__	:					bool							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "Overwrite the cached preprocessed datasets or not."}      )
			A__	:					Optional[int]							  =     field(
			    default=1       ,		metadata={
			        "help": "The percentage of the train set used as validation set in case there's no validation split"
			    }       ,		)
			A__	:					Optional[int]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "The number of processes to use for the preprocessing."}       ,		)
			A__	:					Optional[float]							  =     field(
			    default=20.0       ,		metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}      )






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					WavaVecaForPreTraining
			A__	:					WavaVecaFeatureExtractor
			A__	:					Union[bool, str]							  =     "longest"
			A__	:					Optional[int]							  =     None
			A__	:					Optional[int]							  =     None


			def __call__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Dict[str, torch.Tensor]:
				# reformat list to dict and set to pytorch format
				A__											=					self.feature_extractor.pad(
				    SCREAMING_SNAKE_CASE__						,	max_length=self.max_length						,	padding=self.padding						,	pad_to_multiple_of=self.pad_to_multiple_of						,	return_tensors="pt"						,	)
				A__											=					self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]		)

				A__											=					batch["input_values"].shape[0]

				# make sure that no loss is computed on padded inputs
				if batch["attention_mask"] is not None:
					# compute real output lengths according to convolution formula
					A__											=					self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1		)		).to(
					    torch.long		)

					A__											=					torch.zeros(
					    (batch_size, mask_indices_seq_length)						,	dtype=torch.long						,	device=batch["input_values"].device		)

					# these two operations makes sure that all values
					# before the output lengths indices are attended to
					A__											=					1
					A__											=					attention_mask.flip([-1]		).cumsum(-1		).flip([-1]		).bool()

				# sample randomly masked indices
				A__											=					_compute_mask_indices(
				    (batch_size, mask_indices_seq_length)						,	self.model.config.mask_time_prob						,	self.model.config.mask_time_length						,	attention_mask=SCREAMING_SNAKE_CASE__						,	min_masks=2						,	)

				return batch






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	*SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=1.0						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				super().__init__(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				A__											=					0
				A__											=					max_gumbel_temp
				A__											=					min_gumbel_temp
				A__											=					gumbel_temp_decay







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	torch.Tensor:
				model.train()
				A__											=					self._prepare_inputs(SCREAMING_SNAKE_CASE__		)

				if self.use_amp:
					with autocast():
						A__											=					self.compute_loss(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				else:
					A__											=					self.compute_loss(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				if self.args.n_gpu > 1 or self.deepspeed:
					if model.module.config.ctc_loss_reduction == "mean":
						A__											=					loss.mean()
					elif model.module.config.ctc_loss_reduction == "sum":
						A__											=					loss.sum() / (inputs["mask_time_indices"]).sum()
					else:
						raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""		)

				if self.args.gradient_accumulation_steps > 1:
					A__											=					loss / self.args.gradient_accumulation_steps

				if self.use_amp:
					self.scaler.scale(SCREAMING_SNAKE_CASE__		).backward()
				elif self.use_apex:
					with amp.scale_loss(SCREAMING_SNAKE_CASE__						,	self.optimizer		) as scaled_loss:
						scaled_loss.backward()
				elif self.deepspeed:
					self.deepspeed.backward(SCREAMING_SNAKE_CASE__		)
				else:
					loss.backward()

				self.num_update_step += 1
				# make sure gumbel softmax temperature is decayed
				if self.args.n_gpu > 1 or self.deepspeed:
					model.module.set_gumbel_temperature(
					    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step						,	self.min_gumbel_temp		)		)
				else:
					model.set_gumbel_temperature(
					    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step						,	self.min_gumbel_temp		)		)

				return loss.detach()



def       _lowerCamelCase				(							)	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

	A__							,							A__							,							A__											=					parser.parse_args_into_dataclasses()
	configure_logger(UpperCAmelCase_,	UpperCAmelCase_     )

	# Downloading and loading a dataset from the hub.
	A__											=					load_dataset(data_args.dataset_name,	data_args.dataset_config_name,	cache_dir=model_args.cache_dir     )

	if "validation" not in datasets.keys():
		# make sure only "validation" and "train" keys remain"
		A__											=					DatasetDict()
		A__											=					load_dataset(
		    data_args.dataset_name,	data_args.dataset_config_name,	split=F"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]""",	cache_dir=model_args.cache_dir,	)
		A__											=					load_dataset(
		    data_args.dataset_name,	data_args.dataset_config_name,	split=F"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]""",	cache_dir=model_args.cache_dir,	)
	else:
		# make sure only "validation" and "train" keys remain"
		A__											=					DatasetDict()
		A__											=					load_dataset(
		    data_args.dataset_name,	data_args.dataset_config_name,	split="validation",	cache_dir=model_args.cache_dir,	)
		A__											=					load_dataset(
		    data_args.dataset_name,	data_args.dataset_config_name,	split=F"""{data_args.train_split_name}""",	cache_dir=model_args.cache_dir,	)

	# only normalized-inputs-training is supported
	A__											=					WavaVecaFeatureExtractor.from_pretrained(
	    model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	do_normalize=UpperCAmelCase_     )

	def prepare_dataset(UpperCAmelCase_ :       str     ):
		# check that all files have the correct sampling rate
		A__							,							A__											=					librosa.load(batch[data_args.speech_file_column],	sr=feature_extractor.sampling_rate     )
		return batch

	# load audio files into numpy arrays
	A__											=					datasets.map(
	    UpperCAmelCase_,	num_proc=data_args.preprocessing_num_workers,	remove_columns=datasets["train"].column_names     )

	# filter audio files that are too long
	A__											=					vectorized_datasets.filter(
	    lambda UpperCAmelCase_     : len(data["speech"]     ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate     )     )

	def normalize(UpperCAmelCase_ :       List[Any]     ):
		return feature_extractor(batch["speech"],	sampling_rate=feature_extractor.sampling_rate     )

	# normalize and transform to `BatchFeatures`
	A__											=					vectorized_datasets.map(
	    UpperCAmelCase_,	batched=UpperCAmelCase_,	num_proc=data_args.preprocessing_num_workers,	load_from_cache_file=not data_args.overwrite_cache,	remove_columns=vectorized_datasets["train"].column_names,	)

	# pretraining is only supported for "newer" stable layer norm architecture
	# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
	A__											=					WavaVecaConfig.from_pretrained(
	    model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	gradient_checkpointing=training_args.gradient_checkpointing,	)

	if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
		raise ValueError(
		    "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
		    " ``config.feat_extract_norm='layer'"     )

	A__											=					WavaVecaForPreTraining(UpperCAmelCase_     )

	A__											=					DataCollatorForWavaVecaPretraining(model=UpperCAmelCase_,	feature_extractor=UpperCAmelCase_     )

	A__											=					WavaVecaPreTrainer(
	    model=UpperCAmelCase_,	data_collator=UpperCAmelCase_,	args=UpperCAmelCase_,	train_dataset=vectorized_datasets["train"],	eval_dataset=vectorized_datasets["validation"],	tokenizer=UpperCAmelCase_,	max_gumbel_temp=model_args.max_gumbel_temperature,	min_gumbel_temp=model_args.min_gumbel_temperature,	gumbel_temp_decay=model_args.gumbel_temperature_decay,	)
	trainer.train()


if __name__ == "__main__":
			main()












'''simple docstring'''



import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
 # Initialising the CNN
 # (Sequential- Building the model layer by layer)
 __UpperCamelCase   :							Union[str, Any]         =   models.Sequential()

 # Step 1 - Convolution
 # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
 # (3,3) is the kernel size (filter matrix)
 classifier.add(
     layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="""relu""")
 )

 # Step 2 - Pooling
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Adding a second convolutional layer
 classifier.add(layers.ConvaD(32, (3, 3), activation="""relu"""))
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Step 3 - Flattening
 classifier.add(layers.Flatten())

 # Step 4 - Full connection
 classifier.add(layers.Dense(units=128, activation="""relu"""))
 classifier.add(layers.Dense(units=1, activation="""sigmoid"""))

 # Compiling the CNN
 classifier.compile(
     optimizer="""adam""", loss="""binary_crossentropy""", metrics=["""accuracy"""]
 )

 # Part 2 - Fitting the CNN to the images

 # Load Trained model weights

 # from keras.models import load_model
 # regressor=load_model('cnn.h5')

 __UpperCamelCase   :							Optional[Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 __UpperCamelCase   :							Any         =   train_datagen.flow_from_directory(
     """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
 )

 __UpperCamelCase   :							str         =   test_datagen.flow_from_directory(
     """dataset/test_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
 )

 classifier.fit_generator(
     training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
 )

 classifier.save("""cnn.h5""")

 # Part 3 - Making new predictions

 __UpperCamelCase   :							Optional[Any]         =   tf.keras.preprocessing.image.load_img(
     """dataset/single_prediction/image.png""", target_size=(64, 64)
 )
 __UpperCamelCase   :							List[Any]         =   tf.keras.preprocessing.image.img_to_array(test_image)
 __UpperCamelCase   :							Dict         =   np.expand_dims(test_image, axis=0)
 __UpperCamelCase   :							Optional[Any]         =   classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  __UpperCamelCase   :							Optional[int]         =   """Normal"""
 if result[0][0] == 1:
  __UpperCamelCase   :							Optional[int]         =   """Abnormality detected"""