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'''):
							_lowerCamelCase   :				List[Any]     			=						True
							from torch.cuda.amp import autocast


_lowerCamelCase   :				Any     			=						logging.getLogger(__name__)


@dataclass
class   lowercase      :
							lowercase__  :    str									=							field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}       )
							lowercase__  :    Optional[str]									=							field(
							    default=a			,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,    )
							lowercase__  :    Optional[bool]									=							field(
							    default=a			,    metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}       )
							lowercase__  :    Optional[bool]									=							field(
							    default=a			,    metadata={"""help""": """Whether to log verbose messages or not."""}			,    )
							lowercase__  :    Optional[float]									=							field(
							    default=2.0			,    metadata={"""help""": """Maximum temperature for gumbel softmax."""}       )
							lowercase__  :    Optional[float]									=							field(
							    default=0.5			,    metadata={"""help""": """Minimum temperature for gumbel softmax."""}       )
							lowercase__  :    Optional[float]									=							field(
							    default=0.99_9995			,    metadata={"""help""": """Decay of gumbel temperature during training."""}       )





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


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



@dataclass
class   lowercase      :
							lowercase__  :    WavaVecaForPreTraining
							lowercase__  :    WavaVecaFeatureExtractor
							lowercase__  :    Union[bool, str]									=							"longest"
							lowercase__  :    Optional[int]									=							None
							lowercase__  :    Optional[int]									=							None




							def __call__(  self				:   Tuple		,							_UpperCamelCase				:   List[Dict[str, Union[List[int], torch.Tensor]]] )   ->			Dict[str, torch.Tensor]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.feature_extractor.pad(
												    _UpperCamelCase		,							max_length=self.max_length		,							padding=self.padding		,							pad_to_multiple_of=self.pad_to_multiple_of		,							return_tensors="pt"		,							)
												SCREAMING_SNAKE_CASE           =    self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )

												SCREAMING_SNAKE_CASE           =    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
																	SCREAMING_SNAKE_CASE           =    self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
																	    torch.long )

																	SCREAMING_SNAKE_CASE           =    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
																	SCREAMING_SNAKE_CASE           =    1
																	SCREAMING_SNAKE_CASE           =    attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()

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

												return batch



class   lowercase      (       a       ):




							def __init__(  self				:   int		,							*_UpperCamelCase				:   int		,							_UpperCamelCase				:   str=1		,							_UpperCamelCase				:   Union[str, Any]=0		,							_UpperCamelCase				:   Any=1.0		,							**_UpperCamelCase				:   List[str] )   ->			Tuple:



												'''simple docstring'''

												super().__init__(*_UpperCamelCase		,							**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    max_gumbel_temp
												SCREAMING_SNAKE_CASE           =    min_gumbel_temp
												SCREAMING_SNAKE_CASE           =    gumbel_temp_decay





							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   nn.Module		,							_UpperCamelCase				:   Dict[str, Union[torch.Tensor, Any]] )   ->			torch.Tensor:



												'''simple docstring'''

												model.train()
												SCREAMING_SNAKE_CASE           =    self._prepare_inputs(_UpperCamelCase )

												if self.use_amp:
																	with autocast():
																						SCREAMING_SNAKE_CASE           =    self.compute_loss(_UpperCamelCase		,							_UpperCamelCase )
												else:
																	SCREAMING_SNAKE_CASE           =    self.compute_loss(_UpperCamelCase		,							_UpperCamelCase )

												if self.args.n_gpu > 1 or self.deepspeed:
																	if model.module.config.ctc_loss_reduction == "mean":
																						SCREAMING_SNAKE_CASE           =    loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																						SCREAMING_SNAKE_CASE           =    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:
																	SCREAMING_SNAKE_CASE           =    loss / self.args.gradient_accumulation_steps

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

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

					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    parser.parse_args_into_dataclasses()
					configure_logger(UpperCAmelCase__ ,	UpperCAmelCase__					)

					# Downloading and loading a dataset from the hub.
					SCREAMING_SNAKE_CASE           =    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"
										SCREAMING_SNAKE_CASE           =    DatasetDict()
										SCREAMING_SNAKE_CASE           =    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 ,	)
										SCREAMING_SNAKE_CASE           =    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"
										SCREAMING_SNAKE_CASE           =    DatasetDict()
										SCREAMING_SNAKE_CASE           =    load_dataset(
										    data_args.dataset_name ,	data_args.dataset_config_name ,	split="validation" ,	cache_dir=model_args.cache_dir ,	)
										SCREAMING_SNAKE_CASE           =    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
					SCREAMING_SNAKE_CASE           =    WavaVecaFeatureExtractor.from_pretrained(
					    model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	do_normalize=UpperCAmelCase__					)

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

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

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

					def normalize(UpperCAmelCase__       :							str					):
										return feature_extractor(batch["speech"] ,	sampling_rate=feature_extractor.sampling_rate					)

					# normalize and transform to `BatchFeatures`
					SCREAMING_SNAKE_CASE           =    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
					SCREAMING_SNAKE_CASE           =    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'"					)

					SCREAMING_SNAKE_CASE           =    WavaVecaForPreTraining(UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    DataCollatorForWavaVecaPretraining(model=UpperCAmelCase__ ,	feature_extractor=UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    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()





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class   lowercase      :
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    int
							lowercase__  :    int
							lowercase__  :    float
							lowercase__  :    float
							lowercase__  :    Tuple[int]




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

												assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
												assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
												assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2




							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												return torch.from_numpy(np.array([self.width, self.height]		,							dtype=np.floataa ) )




							def 		__snake_case(  self				:   Tuple )   ->			List[str]:



												'''simple docstring'''

												return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,							dtype=np.floataa ) )




							def 		__snake_case(  self				:   Any )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    torch.arange(self.height * self.width )
												SCREAMING_SNAKE_CASE           =    torch.stack(
												    [
												        pixel_indices % self.width,
												        torch.div(_UpperCamelCase		,							self.width		,							rounding_mode="trunc" ),
												    ]		,							axis=1		,							)
												return coords




							@property
							def 		__snake_case(  self				:   Any )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE           =    self.shape
												SCREAMING_SNAKE_CASE           =    int(np.prod(_UpperCamelCase ) )

												SCREAMING_SNAKE_CASE           =    self.get_image_coords()
												SCREAMING_SNAKE_CASE           =    torch.broadcast_to(coords.unsqueeze(0 )		,							[batch_size * inner_batch_size, *coords.shape] )
												SCREAMING_SNAKE_CASE           =    self.get_camera_rays(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    rays.view(_UpperCamelCase		,							inner_batch_size * self.height * self.width		,							2		,							3 )

												return rays




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   torch.Tensor )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    coords.shape
												assert n_coords == 2
												assert batch_size == self.origin.shape[0]

												SCREAMING_SNAKE_CASE           =    coords.view(_UpperCamelCase		,							-1		,							2 )

												SCREAMING_SNAKE_CASE           =    self.resolution()
												SCREAMING_SNAKE_CASE           =    self.fov()

												SCREAMING_SNAKE_CASE           =    (flat.float() / (res - 1)) * 2 - 1
												SCREAMING_SNAKE_CASE           =    fracs * torch.tan(fov / 2 )

												SCREAMING_SNAKE_CASE           =    fracs.view(_UpperCamelCase		,							-1		,							2 )
												SCREAMING_SNAKE_CASE           =    (
												    self.z.view(_UpperCamelCase		,							1		,							3 )
												    + self.x.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, :1]
												    + self.y.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, 1:]
												)
												SCREAMING_SNAKE_CASE           =    directions / directions.norm(dim=-1		,							keepdim=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.stack(
												    [
												        torch.broadcast_to(self.origin.view(_UpperCamelCase		,							1		,							3 )		,							[batch_size, directions.shape[1], 3] ),
												        directions,
												    ]		,							dim=2		,							)
												return rays.view(_UpperCamelCase		,							*_UpperCamelCase		,							2		,							3 )





							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int )   ->			"DifferentiableProjectiveCamera":



												'''simple docstring'''

												assert width * self.height == height * self.width, "The aspect ratio should not change."
												return DifferentiableProjectiveCamera(
												    origin=self.origin		,							x=self.x		,							y=self.y		,							z=self.z		,							width=_UpperCamelCase		,							height=_UpperCamelCase		,							x_fov=self.x_fov		,							y_fov=self.y_fov		,							)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					for theta in np.linspace(0 ,	2 * np.pi ,	num=2_0					):
										SCREAMING_SNAKE_CASE           =    np.array([np.sin(UpperCAmelCase__					), np.cos(UpperCAmelCase__					), -0.5]					)
										z /= np.sqrt(np.sum(z**2					)					)
										SCREAMING_SNAKE_CASE           =    -z * 4
										SCREAMING_SNAKE_CASE           =    np.array([np.cos(UpperCAmelCase__					), -np.sin(UpperCAmelCase__					), 0.0]					)
										SCREAMING_SNAKE_CASE           =    np.cross(UpperCAmelCase__ ,	UpperCAmelCase__					)
										origins.append(UpperCAmelCase__					)
										xs.append(UpperCAmelCase__					)
										ys.append(UpperCAmelCase__					)
										zs.append(UpperCAmelCase__					)
					return DifferentiableProjectiveCamera(
					    origin=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	x=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	y=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	z=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	width=UpperCAmelCase__ ,	height=UpperCAmelCase__ ,	x_fov=0.7 ,	y_fov=0.7 ,	shape=(1, len(UpperCAmelCase__					)) ,	)




