
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'):
					SCREAMING_SNAKE_CASE							:List[str]										=    True
					from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE							:Any										=    logging.getLogger(__name__)





@dataclass
class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}  )
	snake_case_    =					field(
	    default=_lowerCamelCase      ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,      )
	snake_case_    =					field(
	    default=_lowerCamelCase      ,      metadata={"help": "Whether to freeze the feature extractor layers of the model."}  )
	snake_case_    =					field(
	    default=_lowerCamelCase      ,      metadata={"help": "Whether to log verbose messages or not."}      ,      )
	snake_case_    =					field(
	    default=2.0      ,      metadata={"help": "Maximum temperature for gumbel softmax."}  )
	snake_case_    =					field(
	    default=0.5      ,      metadata={"help": "Minimum temperature for gumbel softmax."}  )
	snake_case_    =					field(
	    default=0.99_99_95      ,      metadata={"help": "Decay of gumbel temperature during training."}  )


def 						UpperCAmelCase      (     a_		,       a_						)		->     str:







				"""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'''





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





@dataclass
class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					42
	snake_case_    =					"longest"
	snake_case_    =					None
	snake_case_    =					None






	def __call__(	self :			Tuple							,A :			List[Dict[str, Union[List[int], torch.Tensor]]]			):
					# reformat list to dict and set to pytorch format
					__A             =			self.feature_extractor.pad(
					    lowercase_							,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=lowercase_							,min_masks=2							,)

					return batch





class 							UpperCAmelCase				(  _lowerCamelCase  ):




	'''simple docstring'''





	def __init__(	self :			Any							,*A :			Any							,A :			Tuple=1							,A :			Optional[Any]=0							,A :			List[Any]=1.0							,**A :			Any			):
					super().__init__(*lowercase_							,**lowercase_			)
					__A             =			0
					__A             =			max_gumbel_temp
					__A             =			min_gumbel_temp
					__A             =			gumbel_temp_decay







	def     UpperCamelCase_  (	self :			Any							,A :			nn.Module							,A :			Dict[str, Union[torch.Tensor, Any]]			):
					model.train()
					__A             =			self._prepare_inputs(lowercase_			)

					if self.use_amp:
									with autocast():
													__A             =			self.compute_loss(lowercase_							,lowercase_			)
					else:
									__A             =			self.compute_loss(lowercase_							,lowercase_			)

					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(lowercase_			).backward()
					elif self.use_apex:
									with amp.scale_loss(lowercase_							,self.optimizer			) as scaled_loss:
													scaled_loss.backward()
					elif self.deepspeed:
									self.deepspeed.backward(lowercase_			)
					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 						UpperCAmelCase      (     )		->     Tuple:







				"""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(a_						):
								# 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 a_						: len(data["speech"]						) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate						)						)

				def normalize(a_						):
								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()


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__A             =			input_array

					# Random initial weights are assigned where first argument is the
					# number of nodes in previous layer and second argument is the
					# number of nodes in the next layer.

					# Random initial weights are assigned.
					# self.input_array.shape[1] is used to represent number of nodes in input layer.
					# First hidden layer consists of 4 nodes.
					__A             =			numpy.random.rand(
					    self.input_array.shape[1]							,4			)

					# Random initial values for the first hidden layer.
					# First hidden layer has 4 nodes.
					# Second hidden layer has 3 nodes.
					__A             =			numpy.random.rand(
					    4							,3			)

					# Random initial values for the second hidden layer.
					# Second hidden layer has 3 nodes.
					# Output layer has 1 node.
					__A             =			numpy.random.rand(3							,1			)

					# Real output values provided.
					__A             =			output_array

					# Predicted output values by the neural network.
					# Predicted_output array initially consists of zeroes.
					__A             =			numpy.zeros(output_array.shape			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			sigmoid(
					    numpy.dot(self.input_array							,self.input_layer_and_first_hidden_layer_weights			)			)

					# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
					# connecting the first hidden set of nodes with the second hidden set of nodes.
					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_input_and_first_hidden_layer							,self.first_hidden_layer_and_second_hidden_layer_weights							,)			)

					# layer_between_second_hidden_layer_and_output is the layer connecting
					# second hidden layer with the output node.
					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_first_hidden_layer_and_second_hidden_layer							,self.second_hidden_layer_and_output_layer_weights							,)			)

					return self.layer_between_second_hidden_layer_and_output






	def     UpperCamelCase_  (	self :			str			):
					__A             =			numpy.dot(
					    self.layer_between_first_hidden_layer_and_second_hidden_layer.T							,2
					    * (self.output_array - self.predicted_output)
					    * sigmoid_derivative(self.predicted_output			)							,)
					__A             =			numpy.dot(
					    self.layer_between_input_and_first_hidden_layer.T							,numpy.dot(
					        2
					        * (self.output_array - self.predicted_output)
					        * sigmoid_derivative(self.predicted_output			)							,self.second_hidden_layer_and_output_layer_weights.T							,)
					    * sigmoid_derivative(
					        self.layer_between_first_hidden_layer_and_second_hidden_layer			)							,)
					__A             =			numpy.dot(
					    self.input_array.T							,numpy.dot(
					        numpy.dot(
					            2
					            * (self.output_array - self.predicted_output)
					            * sigmoid_derivative(self.predicted_output			)							,self.second_hidden_layer_and_output_layer_weights.T							,)
					        * sigmoid_derivative(
					            self.layer_between_first_hidden_layer_and_second_hidden_layer			)							,self.first_hidden_layer_and_second_hidden_layer_weights.T							,)
					    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer			)							,)

					self.input_layer_and_first_hidden_layer_weights += (
					    updated_input_layer_and_first_hidden_layer_weights
					)
					self.first_hidden_layer_and_second_hidden_layer_weights += (
					    updated_first_hidden_layer_and_second_hidden_layer_weights
					)
					self.second_hidden_layer_and_output_layer_weights += (
					    updated_second_hidden_layer_and_output_layer_weights
					)






	def     UpperCamelCase_  (	self :			Tuple							,A :			numpy.ndarray							,A :			int							,A :			bool			):
					for iteration in range(1							,iterations + 1			):
									__A             =			self.feedforward()
									self.back_propagation()
									if give_loss:
													__A             =			numpy.mean(numpy.square(output - self.feedforward()			)			)
													print(f'''Iteration {iteration} Loss: {loss}'''			)







	def     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__A             =			input_arr

					__A             =			sigmoid(
					    numpy.dot(self.array							,self.input_layer_and_first_hidden_layer_weights			)			)

					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_input_and_first_hidden_layer							,self.first_hidden_layer_and_second_hidden_layer_weights							,)			)

					__A             =			sigmoid(
					    numpy.dot(
					        self.layer_between_first_hidden_layer_and_second_hidden_layer							,self.second_hidden_layer_and_output_layer_weights							,)			)

					return int(self.layer_between_second_hidden_layer_and_output > 0.6			)





def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return 1 / (1 + numpy.exp(-value						))


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return (value) * (1 - (value))


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			numpy.array(
				    (
				        [0, 0, 0],
				        [0, 0, 1],
				        [0, 1, 0],
				        [0, 1, 1],
				        [1, 0, 0],
				        [1, 0, 1],
				        [1, 1, 0],
				        [1, 1, 1],
				    )		,       dtype=numpy.floataa		,       )

				# True output values for the given input values.
				__A             =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,       dtype=numpy.floataa						)

				# Calling neural network class.
				__A             =			TwoHiddenLayerNeuralNetwork(
				    input_array=a_		,       output_array=a_						)

				# Calling training function.
				# Set give_loss to True if you want to see loss in every iteration.
				neural_network.train(output=a_		,       iterations=1_0		,       give_loss=a_						)

				return neural_network.predict(numpy.array(([1, 1, 1])		,       dtype=numpy.floataa						)						)


if __name__ == "__main__":
					example()
