

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

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
	from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
	A_ :				Optional[Any]						 =    True
	from torch.cuda.amp import autocast

A_ :				int						 =    logging.getLogger(__name__)

def 		UpperCamelCase      (lowercase_:				Union[str, Any]=None					,   lowercase_:				Tuple=None      )  ->   Union[str, Any]:
	return field(default_factory=lambda: default					,   metadata=lowercase_      )



@dataclass
class 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     str													= field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
					UpperCAmelCase__:     Optional[str]													= field(
					    default=__magic_name__	,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,     )
					UpperCAmelCase__:     Optional[bool]													= field(
					    default=__magic_name__	,     metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}       )
					UpperCAmelCase__:     Optional[float]													= field(
					    default=0.1	,     metadata={'''help''': '''The dropout ratio for the attention probabilities.'''}       )
					UpperCAmelCase__:     Optional[float]													= field(
					    default=0.1	,     metadata={'''help''': '''The dropout ratio for activations inside the fully connected layer.'''}       )
					UpperCAmelCase__:     Optional[float]													= field(
					    default=0.1	,     metadata={
					        '''help''': '''The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'''
					    }	,     )
					UpperCAmelCase__:     Optional[float]													= field(
					    default=0.1	,     metadata={'''help''': '''The dropout probabilitiy for all 1D convolutional layers in feature extractor.'''}	,     )
					UpperCAmelCase__:     Optional[float]													= field(
					    default=0.05	,     metadata={
					        '''help''': (
					            '''Propability of each feature vector along the time axis to be chosen as the start of the vector'''
					            '''span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'''
					            '''vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'''
					        )
					    }	,     )
					UpperCAmelCase__:     Optional[float]													= field(default=0.0	,     metadata={'''help''': '''The LayerDrop probability.'''}       )



@dataclass
class 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     Optional[str]													= field(
					    default=__magic_name__	,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
					UpperCAmelCase__:     Optional[str]													= field(
					    default='''train+validation'''	,     metadata={
					        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
					    }	,     )
					UpperCAmelCase__:     bool													= field(
					    default=__magic_name__	,     metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}       )
					UpperCAmelCase__:     Optional[int]													= field(
					    default=__magic_name__	,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''}	,     )
					UpperCAmelCase__:     Optional[int]													= field(
					    default=__magic_name__	,     metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
					            '''value if set.'''
					        )
					    }	,     )
					UpperCAmelCase__:     Optional[int]													= field(
					    default=__magic_name__	,     metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of validation examples to this '''
					            '''value if set.'''
					        )
					    }	,     )
					UpperCAmelCase__:     List[str]													= list_field(
					    default=[''',''', '''?''', '''.''', '''!''', '''-''', ''';''', ''':''', '''""''', '''%''', '''\'''', '''"''', '''�''']	,     metadata={'''help''': '''A list of characters to remove from the transcripts.'''}	,     )



@dataclass
class 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     WavaVecaProcessor
					UpperCAmelCase__:     Union[bool, str]													= True
					UpperCAmelCase__:     Optional[int]													= None
					UpperCAmelCase__:     Optional[int]													= None
					UpperCAmelCase__:     Optional[int]													= None
					UpperCAmelCase__:     Optional[int]													= None






					def __call__(				self					, A__    ):
						# split inputs and labels since they have to be of different lenghts and need
						# different padding methods
						A__							:					Tuple       =	[{"""input_values""": feature["""input_values"""]} for feature in features]
						A__							:					List[str]       =	[{"""input_ids""": feature["""labels"""]} for feature in features]

						A__							:					List[Any]       =	self.processor.pad(
						    A__					, padding=self.padding					, max_length=self.max_length					, pad_to_multiple_of=self.pad_to_multiple_of					, return_tensors="""pt"""					, )
						A__							:					Optional[Any]       =	self.processor.pad(
						    labels=A__					, padding=self.padding					, max_length=self.max_length_labels					, pad_to_multiple_of=self.pad_to_multiple_of_labels					, return_tensors="""pt"""					, )

						# replace padding with -100 to ignore loss correctly
						A__							:					Optional[Any]       =	labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )					, -100    )

						A__							:					str       =	labels

						return batch



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self					, A__					, A__    ):
						model.train()
						A__							:					Optional[Any]       =	self._prepare_inputs(A__    )

						if self.use_amp:
							with autocast():
								A__							:					Any       =	self.compute_loss(A__					, A__    )
						else:
							A__							:					Union[str, Any]       =	self.compute_loss(A__					, A__    )

						if self.args.n_gpu > 1:
							if model.module.config.ctc_loss_reduction == "mean":
								A__							:					int       =	loss.mean()
							elif model.module.config.ctc_loss_reduction == "sum":
								A__							:					List[Any]       =	loss.sum() / (inputs["""labels"""] >= 0).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__							:					Optional[Any]       =	loss / self.args.gradient_accumulation_steps

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

						return loss.detach()



def 		UpperCamelCase      ()  ->   Any:
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	A__							:					str       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
	if len(sys.argv      ) == 2 and sys.argv[1].endswith(""".json"""      ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		A__     ,     A__     ,     A__							:					Dict       =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
	else:
		A__     ,     A__     ,     A__							:					Dict       =	parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	A__							:					List[Any]       =	None
	if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
		A__							:					Tuple       =	get_last_checkpoint(training_args.output_dir      )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
			raise ValueError(
			    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
			    """Use --overwrite_output_dir to overcome."""      )
		elif last_checkpoint is not None:
			logger.info(
			    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
			    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""      )

    # Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,   datefmt="""%m/%d/%Y %H:%M:%S"""					,   handlers=[logging.StreamHandler(sys.stdout      )]					,   )
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank      ) else logging.WARN      )

	# Log on each process the small summary:
	logger.warning(
	    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
	    + f"""distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}"""      )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank      ):
		transformers.utils.logging.set_verbosity_info()
	logger.info("""Training/evaluation parameters %s"""					,   lowercase_      )

	# Set seed before initializing model.
	set_seed(training_args.seed      )

	# Get the datasets:
	A__							:					Optional[int]       =	datasets.load_dataset(
	    """common_voice"""					,   data_args.dataset_config_name					,   split=data_args.train_split_name      )
	A__							:					Optional[Any]       =	datasets.load_dataset("""common_voice"""					,   data_args.dataset_config_name					,   split="""test"""      )

	# Create and save tokenizer
	A__							:					Dict       =	f"""[{''.join(data_args.chars_to_ignore      )}]"""

	def remove_special_characters(lowercase_:				List[str]      ):
		A__							:					int       =	re.sub(lowercase_					,   """"""					,   batch["""sentence"""]      ).lower() + """ """
		return batch

	A__							:					Any       =	train_dataset.map(lowercase_					,   remove_columns=["""sentence"""]      )
	A__							:					Tuple       =	eval_dataset.map(lowercase_					,   remove_columns=["""sentence"""]      )

	def extract_all_chars(lowercase_:				Optional[Any]      ):
		A__							:					Optional[int]       =	""" """.join(batch["""text"""]      )
		A__							:					Union[str, Any]       =	list(set(lowercase_      )      )
		return {"vocab": [vocab], "all_text": [all_text]}

	A__							:					List[str]       =	train_dataset.map(
	    lowercase_					,   batched=lowercase_					,   batch_size=-1					,   keep_in_memory=lowercase_					,   remove_columns=train_dataset.column_names					,   )
	A__							:					Optional[Any]       =	train_dataset.map(
	    lowercase_					,   batched=lowercase_					,   batch_size=-1					,   keep_in_memory=lowercase_					,   remove_columns=eval_dataset.column_names					,   )

	A__							:					List[Any]       =	list(set(vocab_train["""vocab"""][0]      ) | set(vocab_test["""vocab"""][0]      )      )
	A__							:					Optional[int]       =	{v: k for k, v in enumerate(lowercase_      )}
	A__							:					List[Any]       =	vocab_dict[""" """]
	del vocab_dict[" "]
	A__							:					List[str]       =	len(lowercase_      )
	A__							:					Optional[int]       =	len(lowercase_      )

	with open("""vocab.json"""					,   """w"""      ) as vocab_file:
		json.dump(lowercase_					,   lowercase_      )

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	A__							:					Optional[int]       =	WavaVecaCTCTokenizer(
	    """vocab.json"""					,   unk_token="""[UNK]"""					,   pad_token="""[PAD]"""					,   word_delimiter_token="""|"""					,   )
	A__							:					Union[str, Any]       =	WavaVecaFeatureExtractor(
	    feature_size=1					,   sampling_rate=16000					,   padding_value=0.0					,   do_normalize=lowercase_					,   return_attention_mask=lowercase_      )
	A__							:					Optional[int]       =	WavaVecaProcessor(feature_extractor=lowercase_					,   tokenizer=lowercase_      )
	A__							:					Optional[int]       =	WavaVecaForCTC.from_pretrained(
	    model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   activation_dropout=model_args.activation_dropout					,   attention_dropout=model_args.attention_dropout					,   hidden_dropout=model_args.hidden_dropout					,   feat_proj_dropout=model_args.feat_proj_dropout					,   mask_time_prob=model_args.mask_time_prob					,   gradient_checkpointing=training_args.gradient_checkpointing					,   layerdrop=model_args.layerdrop					,   ctc_loss_reduction="""mean"""					,   pad_token_id=processor.tokenizer.pad_token_id					,   vocab_size=len(processor.tokenizer      )					,   )

	if data_args.max_train_samples is not None:
		A__							:					Tuple       =	min(len(lowercase_      )					,   data_args.max_train_samples      )
		A__							:					Dict       =	train_dataset.select(range(lowercase_      )      )

	if data_args.max_val_samples is not None:
		A__							:					str       =	eval_dataset.select(range(data_args.max_val_samples      )      )

	A__							:					Union[str, Any]       =	torchaudio.transforms.Resample(48000					,   16000      )

	# Preprocessing the datasets.
	# We need to read the aduio files as arrays and tokenize the targets.
	def speech_file_to_array_fn(lowercase_:				Any      ):
		A__     ,     A__							:					Any       =	torchaudio.load(batch["""path"""]      )
		A__							:					List[Any]       =	resampler(lowercase_      ).squeeze().numpy()
		A__							:					Tuple       =	16000
		A__							:					Any       =	batch["""text"""]
		return batch

	A__							:					Optional[Any]       =	train_dataset.map(
	    lowercase_					,   remove_columns=train_dataset.column_names					,   num_proc=data_args.preprocessing_num_workers					,   )
	A__							:					Optional[Any]       =	eval_dataset.map(
	    lowercase_					,   remove_columns=eval_dataset.column_names					,   num_proc=data_args.preprocessing_num_workers					,   )

	def prepare_dataset(lowercase_:				str      ):
		# check that all files have the correct sampling rate
		assert (
		    len(set(batch["""sampling_rate"""]      )      ) == 1
		), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

		A__							:					int       =	processor(
		    audio=batch["""speech"""]					,   text=batch["""target_text"""]					,   sampling_rate=batch["""sampling_rate"""][0]      )
		batch.update(lowercase_      )
		return batch

	A__							:					Any       =	train_dataset.map(
	    lowercase_					,   remove_columns=train_dataset.column_names					,   batch_size=training_args.per_device_train_batch_size					,   batched=lowercase_					,   num_proc=data_args.preprocessing_num_workers					,   )
	A__							:					Union[str, Any]       =	eval_dataset.map(
	    lowercase_					,   remove_columns=eval_dataset.column_names					,   batch_size=training_args.per_device_train_batch_size					,   batched=lowercase_					,   num_proc=data_args.preprocessing_num_workers					,   )

	# Metric
	A__							:					int       =	datasets.load_metric("""wer"""      )

	def compute_metrics(lowercase_:				str      ):
		A__							:					Any       =	pred.predictions
		A__							:					Optional[Any]       =	np.argmax(lowercase_					,   axis=-1      )

		A__							:					List[str]       =	processor.tokenizer.pad_token_id

		A__							:					List[Any]       =	processor.batch_decode(lowercase_      )
		# we do not want to group tokens when computing the metrics
		A__							:					List[str]       =	processor.batch_decode(pred.label_ids					,   group_tokens=lowercase_      )

		A__							:					int       =	wer_metric.compute(predictions=lowercase_					,   references=lowercase_      )

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	# Data collator
	A__							:					int       =	DataCollatorCTCWithPadding(processor=lowercase_					,   padding=lowercase_      )

	# Initialize our Trainer
	A__							:					int       =	CTCTrainer(
	    model=lowercase_					,   data_collator=lowercase_					,   args=lowercase_					,   compute_metrics=lowercase_					,   train_dataset=train_dataset if training_args.do_train else None					,   eval_dataset=eval_dataset if training_args.do_eval else None					,   tokenizer=processor.feature_extractor					,   )

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			A__							:					int       =	last_checkpoint
		elif os.path.isdir(model_args.model_name_or_path      ):
			A__							:					Tuple       =	model_args.model_name_or_path
		else:
			A__							:					str       =	None

		# Save the feature_extractor and the tokenizer
		if is_main_process(training_args.local_rank      ):
			processor.save_pretrained(training_args.output_dir      )

		A__							:					List[Any]       =	trainer.train(resume_from_checkpoint=lowercase_      )
		trainer.save_model()

		A__							:					Tuple       =	train_result.metrics
		A__							:					Dict       =	(
		    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_      )
		)
		A__							:					List[Any]       =	min(lowercase_					,   len(lowercase_      )      )

		trainer.log_metrics("""train"""					,   lowercase_      )
		trainer.save_metrics("""train"""					,   lowercase_      )
		trainer.save_state()

	# Evaluation
	A__							:					str       =	{}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""      )
		A__							:					Optional[int]       =	trainer.evaluate()
		A__							:					str       =	data_args.max_val_samples if data_args.max_val_samples is not None else len(lowercase_      )
		A__							:					Tuple       =	min(lowercase_					,   len(lowercase_      )      )

		trainer.log_metrics("""eval"""					,   lowercase_      )
		trainer.save_metrics("""eval"""					,   lowercase_      )

	return results


if __name__ == "__main__":
	main()






from __future__ import annotations

from collections.abc import Callable

A_ :				List[Any]						 =    list[list[float | int]]

def 		UpperCamelCase      (lowercase_:				Matrix					,   lowercase_:				Matrix      )  ->   Matrix:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(size + 1      )] for _ in range(lowercase_      )]
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					float

	for row in range(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					List[str]       =	matrix[row][col]

		A__							:					int       =	vector[row][0]

	A__							:					Optional[int]       =	0
	A__							:					str       =	0
	while row < size and col < size:
		# pivoting
		A__							:					int       =	max((abs(augmented[rowa][col]      ), rowa) for rowa in range(lowercase_					,   lowercase_      )      )[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			A__     ,     A__							:					Union[str, Any]       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1					,   lowercase_      ):
			A__							:					List[Any]       =	augmented[rowa][col] / augmented[row][col]
			A__							:					Dict       =	0
			for cola in range(col + 1					,   size + 1      ):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1					,   lowercase_      ):
		for row in range(lowercase_      ):
			A__							:					List[str]       =	augmented[row][col] / augmented[col][col]
			for cola in range(lowercase_					,   size + 1      ):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]					,   10      )] for row in range(lowercase_      )
	]

def 		UpperCamelCase      (lowercase_:				list[int]      )  ->   Callable[[int], int]:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(lowercase_      )] for _ in range(lowercase_      )]
	A__							:					Matrix       =	[[0] for _ in range(lowercase_      )]
	A__							:					Matrix
	A__							:					int
	A__							:					int
	A__							:					int

	for x_val, y_val in enumerate(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					Dict       =	(x_val + 1) ** (size - col - 1)
		A__							:					Any       =	y_val

	A__							:					Union[str, Any]       =	solve(lowercase_					,   lowercase_      )

	def interpolated_func(lowercase_:				int      ) -> int:
		return sum(
		    round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
		    for x_val in range(lowercase_      )      )

	return interpolated_func

def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)

def 		UpperCamelCase      (lowercase_:				Callable[[int], int] = question_function					,   lowercase_:				int = 10      )  ->   int:
	A__							:					list[int]       =	[func(lowercase_      ) for x_val in range(1					,   order + 1      )]

	A__							:					list[Callable[[int], int]]       =	[
	    interpolate(data_points[:max_coeff]      ) for max_coeff in range(1					,   order + 1      )
	]

	A__							:					int       =	0
	A__							:					Callable[[int], int]
	A__							:					int

	for poly in polynomials:
		A__							:					List[str]       =	1
		while func(lowercase_      ) == poly(lowercase_      ):
			x_val += 1

		ret += poly(lowercase_      )

	return ret


if __name__ == "__main__":
	print(f'''{solution() = }''')



