

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'):
		A									=  True
		from torch.cuda.amp import autocast


A									=  logging.getLogger(__name__)


@dataclass
class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




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







def a(lowercase__      ,			lowercase__						):





	'''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						)]      ,			)
	snake_case_   		=			logging.WARNING
	if model_args.verbose_logging:
		snake_case_   		=			logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank						):
		snake_case_   		=			logging.INFO
	logger.setLevel(lowercase__						)


@dataclass
class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




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







@dataclass
class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			__A    	=	42
			__A    	=	42
			__A    	=	"longest"
			__A    	=	None
			__A    	=	None


			def __call__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				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'			,					)
				snake_case_   		=			self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]				)

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

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

				# sample randomly masked indices
				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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					*__UpperCamelCase			,					__UpperCamelCase=1			,					__UpperCamelCase=0			,					__UpperCamelCase=1.0			,					**__UpperCamelCase				):

				"""simple docstring"""


				super().__init__(*__UpperCamelCase			,					**__UpperCamelCase				)
				snake_case_   		=			0
				snake_case_   		=			max_gumbel_temp
				snake_case_   		=			min_gumbel_temp
				snake_case_   		=			gumbel_temp_decay







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				model.train()
				snake_case_   		=			self._prepare_inputs(__UpperCamelCase				)

				if self.use_amp:
					with autocast():
						snake_case_   		=			self.compute_loss(__UpperCamelCase			,					__UpperCamelCase				)
				else:
					snake_case_   		=			self.compute_loss(__UpperCamelCase			,					__UpperCamelCase				)

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





	'''simple docstring'''
	# 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.

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

	snake_case_		,							snake_case_		,							snake_case_   		=			parser.parse_args_into_dataclasses()
	configure_logger(lowercase__      ,			lowercase__						)

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

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

	# load audio files into numpy arrays
	snake_case_   		=			datasets.map(
	    lowercase__      ,			num_proc=data_args.preprocessing_num_workers      ,			remove_columns=datasets['train'].column_names						)

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

	def normalize(lowercase__						):
		return feature_extractor(batch['speech']      ,			sampling_rate=feature_extractor.sampling_rate						)

	# normalize and transform to `BatchFeatures`
	snake_case_   		=			vectorized_datasets.map(
	    lowercase__      ,			batched=lowercase__      ,			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
	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\''						)

	snake_case_   		=			WavaVecaForPreTraining(lowercase__						)

	snake_case_   		=			DataCollatorForWavaVecaPretraining(model=lowercase__      ,			feature_extractor=lowercase__						)

	snake_case_   		=			WavaVecaPreTrainer(
	    model=lowercase__      ,			data_collator=lowercase__      ,			args=lowercase__      ,			train_dataset=vectorized_datasets['train']      ,			eval_dataset=vectorized_datasets['validation']      ,			tokenizer=lowercase__      ,			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()



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
