import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


__lowercase											=      logging.getLogger(__name__)

__lowercase											=      'pytorch_model.bin'


@dataclasses.dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		Any      			=   dataclasses.field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}				)
    a__			:		Any      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}		,			)


@dataclasses.dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		int      			=   dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}				)
    a__			:		str      			=   dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}				)
    a__			:		int      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """A csv or a json file containing the validation data."""}				)
    a__			:		List[Any]      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """The name of the task to train on."""}		,			)
    a__			:		int      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """The list of labels for the task."""}				)


@dataclasses.dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		Optional[Any]      			=   dataclasses.field(
        metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}				)
    a__			:		str      			=   dataclasses.field(
        default="""accuracy"""		,			metadata={"""help""": """The evaluation metric used for the task."""}				)
    a__			:		Optional[int]      			=   dataclasses.field(
        default="""no"""		,			metadata={
            """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
        }		,			)
    a__			:		int      			=   dataclasses.field(
        default=1_0		,			metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}		,			)
    a__			:		Union[str, Any]      			=   dataclasses.field(
        default=0.0		,			metadata={
            """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
        }		,			)
    a__			:		Any      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}		,			)
    a__			:		List[Any]      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}		,			)
    a__			:		Dict      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}		,			)
    a__			:		Any      			=   dataclasses.field(
        default=0.0		,			metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}		,			)
    a__			:		Dict      			=   dataclasses.field(
        default=1_0_0		,			metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}		,			)
    a__			:		Tuple      			=   dataclasses.field(
        default=lowerCamelCase__		,			metadata={"""help""": """Random seed for initialization."""}		,			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    __UpperCamelCase  :Optional[int]    						=	datasets.concatenate_datasets([infer_input, infer_output]						,    axis=1			)

    if args.do_filter_by_confidence:
        __UpperCamelCase  :Union[str, Any]    						=	dataset.filter(lambda SCREAMING_SNAKE_CASE			: example["probability"] > args.confidence_threshold			)

    if args.do_filter_by_val_performance:
        assert eval_result >= 0.0 and eval_result <= 1.0
        __UpperCamelCase  :List[str]    						=	int(eval_result * len(lowerCamelCase_			)			)
        print(lowerCamelCase_			)
        __UpperCamelCase  :Any    						=	dataset.sort('''probability'''						,    reverse=lowerCamelCase_			)
        __UpperCamelCase  :Any    						=	dataset.select(range(lowerCamelCase_			)			)

    __UpperCamelCase  :Tuple    						=	dataset.remove_columns(['''label''', '''probability''']			)
    __UpperCamelCase  :str    						=	dataset.rename_column('''prediction'''						,    '''label'''			)
    __UpperCamelCase  :List[str]    						=	dataset.map(lambda SCREAMING_SNAKE_CASE			: {"label": idalabel[example["label"]]}			)
    __UpperCamelCase  :Dict    						=	dataset.shuffle(seed=args.seed			)

    __UpperCamelCase  :Union[str, Any]    						=	os.path.join(lowerCamelCase_						,    f"""train_pseudo.{args.data_file_extension}"""			)
    if args.data_file_extension == "csv":
        dataset.to_csv(lowerCamelCase_						,    index=lowerCamelCase_			)
    else:
        dataset.to_json(lowerCamelCase_			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    **SCREAMING_SNAKE_CASE			):
    __UpperCamelCase  :Any    						=	Accelerator()
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,    datefmt='''%m/%d/%Y %H:%M:%S'''						,    level=logging.INFO						,    )
    logger.info(accelerator.state			)

    # Setup logging, we only want one process per machine to log things on the
    # screen. accelerator.is_local_main_process is only True for one process per
    # machine.
    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR			)

    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    __UpperCamelCase  :Tuple    						=	STModelArguments(model_name_or_path=lowerCamelCase_			)
    __UpperCamelCase  :Tuple    						=	STDataArguments(train_file=lowerCamelCase_						,    infer_file=lowerCamelCase_			)
    __UpperCamelCase  :Any    						=	STTrainingArguments(output_dir=lowerCamelCase_			)
    __UpperCamelCase  :Optional[int]    						=	argparse.Namespace()

    for arg_class in (model_args, data_args, training_args):
        for key, value in vars(lowerCamelCase_			).items():
            setattr(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)

    for key, value in kwargs.items():
        if hasattr(lowerCamelCase_						,    lowerCamelCase_			):
            setattr(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)

    # Sanity checks
    __UpperCamelCase  :str    						=	{}
    __UpperCamelCase  :Optional[int]    						=	None

    # You need to provide the training data and the data to predict on
    assert args.train_file is not None
    assert args.infer_file is not None
    __UpperCamelCase  :List[Any]    						=	args.train_file
    __UpperCamelCase  :int    						=	args.infer_file

    if args.evaluation_strategy != IntervalStrategy.NO.value:
        assert args.eval_file is not None
        __UpperCamelCase  :str    						=	args.eval_file

    for key in data_files:
        __UpperCamelCase  :List[Any]    						=	data_files[key].split('''.'''			)[-1]
        assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
        if args.data_file_extension is None:
            __UpperCamelCase  :Optional[int]    						=	extension
        else:
            assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

    assert (
        args.eval_metric in datasets.list_metrics()
    ), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed			)

    logger.info('''Creating the initial data directory for self-training...'''			)
    __UpperCamelCase  :str    						=	f"""{args.output_dir}/self-train_iter-{{}}""".format
    __UpperCamelCase  :Union[str, Any]    						=	data_dir_format(0			)

    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir						,    exist_ok=lowerCamelCase_			)
            os.makedirs(lowerCamelCase_						,    exist_ok=lowerCamelCase_			)
    accelerator.wait_for_everyone()

    __UpperCamelCase  :List[Any]    						=	None
    __UpperCamelCase  :Union[str, Any]    						=	None
    __UpperCamelCase  :Tuple    						=	0
    __UpperCamelCase  :int    						=	False
    # Show the progress bar
    __UpperCamelCase  :Dict    						=	tqdm(range(args.max_selftrain_iterations			)						,    disable=not accelerator.is_local_main_process			)

    # Self-train
    for iteration in range(0						,    int(args.max_selftrain_iterations			)			):
        __UpperCamelCase  :int    						=	data_dir_format(lowerCamelCase_			)
        assert os.path.exists(lowerCamelCase_			)

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        __UpperCamelCase  :Optional[int]    						=	os.path.join(lowerCamelCase_						,    '''stage-1'''			)
        __UpperCamelCase  :List[Any]    						=	{
            '''accelerator''': accelerator,
            '''model_name_or_path''': args.model_name_or_path,
            '''cache_dir''': args.cache_dir,
            '''do_train''': True,
            '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
            '''do_eval''': True if args.eval_file is not None else False,
            '''eval_file''': data_files['''eval'''],
            '''do_predict''': True,
            '''infer_file''': data_files['''infer'''],
            '''task_name''': args.task_name,
            '''label_list''': args.label_list,
            '''output_dir''': current_output_dir,
            '''eval_metric''': args.eval_metric,
            '''evaluation_strategy''': args.evaluation_strategy,
            '''early_stopping_patience''': args.early_stopping_patience,
            '''early_stopping_threshold''': args.early_stopping_threshold,
            '''seed''': args.seed,
        }
        # Add additional training arguments
        for key, value in kwargs.items():
            if key not in arguments_dict and not hasattr(lowerCamelCase_						,    lowerCamelCase_			):
                arguments_dict.update({key: value}			)

        __UpperCamelCase  :Dict    						=	os.path.join(lowerCamelCase_						,    '''best-checkpoint'''						,    lowerCamelCase_			)
        if os.path.exists(lowerCamelCase_			):
            logger.info(
                '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''						,    lowerCamelCase_						,    lowerCamelCase_						,    )
        else:
            logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''						,    lowerCamelCase_			)
            finetune(**lowerCamelCase_			)
            accelerator.wait_for_everyone()
            assert os.path.exists(lowerCamelCase_			)
            logger.info('''Self-training job completed: iteration: %d, stage: 1.'''						,    lowerCamelCase_			)

        if iteration > 0 and args.finetune_on_labeled_data:
            # Stage 2 (optional): fine-tuning on the original labeled data
            __UpperCamelCase  :str    						=	os.path.join(lowerCamelCase_						,    '''best-checkpoint'''			)
            __UpperCamelCase  :List[str]    						=	os.path.join(lowerCamelCase_						,    '''stage-2'''			)
            # Update arguments_dict
            __UpperCamelCase  :Optional[Any]    						=	model_path
            __UpperCamelCase  :List[str]    						=	data_files['''train''']
            __UpperCamelCase  :Tuple    						=	current_output_dir

            __UpperCamelCase  :List[str]    						=	os.path.join(lowerCamelCase_						,    '''best-checkpoint'''						,    lowerCamelCase_			)
            if os.path.exists(lowerCamelCase_			):
                logger.info(
                    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''						,    lowerCamelCase_						,    lowerCamelCase_						,    )
            else:
                logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''						,    lowerCamelCase_			)
                finetune(**lowerCamelCase_			)
                accelerator.wait_for_everyone()
                assert os.path.exists(lowerCamelCase_			)
                logger.info('''Self-training job completed: iteration: %d, stage: 2.'''						,    lowerCamelCase_			)

        __UpperCamelCase  :List[str]    						=	iteration
        __UpperCamelCase  :Dict    						=	data_dir_format(iteration + 1			)

        __UpperCamelCase  :Optional[Any]    						=	AutoConfig.from_pretrained(os.path.join(lowerCamelCase_						,    '''best-checkpoint'''			)			)
        __UpperCamelCase  :str    						=	config.idalabel
        __UpperCamelCase  :int    						=	os.path.join(lowerCamelCase_						,    '''eval_results_best-checkpoint.json'''			)
        __UpperCamelCase  :Any    						=	os.path.join(lowerCamelCase_						,    '''test_results_best-checkpoint.json'''			)
        assert os.path.exists(lowerCamelCase_			)

        with open(lowerCamelCase_						,    '''r'''			) as f:
            __UpperCamelCase  :List[str]    						=	float(json.load(lowerCamelCase_			)[args.eval_metric]			)
        __UpperCamelCase  :Optional[Any]    						=	os.path.join(lowerCamelCase_						,    '''infer_output_best-checkpoint.csv'''			)
        assert os.path.exists(lowerCamelCase_			)
        # Loading the dataset from local csv or json files.
        __UpperCamelCase  :Optional[int]    						=	load_dataset(args.data_file_extension						,    data_files={'''data''': data_files['''infer''']}			)['''data''']
        __UpperCamelCase  :Tuple    						=	load_dataset('''csv'''						,    data_files={'''data''': infer_output_file}			)['''data''']

        if accelerator.is_main_process:
            os.makedirs(lowerCamelCase_						,    exist_ok=lowerCamelCase_			)
            shutil.copy(lowerCamelCase_						,    os.path.join(lowerCamelCase_						,    f"""eval_results_iter-{iteration}.json"""			)			)
            if os.path.exists(lowerCamelCase_			):
                shutil.copy(lowerCamelCase_						,    os.path.join(lowerCamelCase_						,    f"""test_results_iter-{iteration}.json"""			)			)
            create_pseudo_labeled_data(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
        accelerator.wait_for_everyone()

        __UpperCamelCase  :Union[str, Any]    						=	os.path.join(lowerCamelCase_						,    f"""train_pseudo.{args.data_file_extension}"""			)

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            __UpperCamelCase  :Optional[Any]    						=	eval_result

            if best_iteration is None:
                __UpperCamelCase  :Dict    						=	new_iteration
                __UpperCamelCase  :Union[str, Any]    						=	new_eval_result
            else:
                if new_eval_result - best_eval_result > args.early_stopping_threshold:
                    __UpperCamelCase  :List[str]    						=	new_iteration
                    __UpperCamelCase  :Union[str, Any]    						=	new_eval_result
                    __UpperCamelCase  :Optional[int]    						=	0
                else:
                    if new_eval_result == best_eval_result:
                        __UpperCamelCase  :Dict    						=	new_iteration
                        __UpperCamelCase  :Any    						=	new_eval_result
                    early_stopping_patience_counter += 1

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    __UpperCamelCase  :Union[str, Any]    						=	True

        progress_bar.update(1			)

        if should_training_stop:
            break

    if best_iteration is not None:
        # Save the best iteration
        logger.info('''Best iteration: %d'''						,    lowerCamelCase_			)
        logger.info('''Best evaluation result: %s = %f'''						,    args.eval_metric						,    lowerCamelCase_			)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(lowerCamelCase_						,    f"""eval_results_iter-{iteration}.json"""			)						,    os.path.join(lowerCamelCase_						,    '''eval_results_best-iteration.json'''			)						,    )
    else:
        # Assume that the last iteration is the best
        logger.info('''Best iteration: %d'''						,    args.max_selftrain_iterations - 1			)
        logger.info('''Best evaluation result: %s = %f'''						,    args.eval_metric						,    lowerCamelCase_			)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(lowerCamelCase_						,    f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""			)						,    os.path.join(lowerCamelCase_						,    '''eval_results_best-iteration.json'''			)						,    )






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Dict      			=   """unispeech"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=0				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=0.5				,				**__lowercase				,				) ->			Optional[int]:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :str    						=	hidden_size
        __UpperCamelCase  :List[str]    						=	feat_extract_norm
        __UpperCamelCase  :str    						=	feat_extract_activation
        __UpperCamelCase  :str    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Any    						=	conv_bias
        __UpperCamelCase  :List[Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Tuple    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :Optional[int]    						=	len(self.conv_dim)
        __UpperCamelCase  :Optional[int]    						=	num_hidden_layers
        __UpperCamelCase  :Union[str, Any]    						=	intermediate_size
        __UpperCamelCase  :Tuple    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :Any    						=	hidden_dropout
        __UpperCamelCase  :List[str]    						=	attention_dropout
        __UpperCamelCase  :int    						=	activation_dropout
        __UpperCamelCase  :int    						=	feat_proj_dropout
        __UpperCamelCase  :Any    						=	final_dropout
        __UpperCamelCase  :Optional[Any]    						=	layerdrop
        __UpperCamelCase  :Any    						=	layer_norm_eps
        __UpperCamelCase  :List[str]    						=	initializer_range
        __UpperCamelCase  :Tuple    						=	num_ctc_classes
        __UpperCamelCase  :Union[str, Any]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :Dict    						=	use_weighted_layer_sum
        __UpperCamelCase  :str    						=	classifier_proj_size

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase  :List[Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[int]    						=	mask_time_prob
        __UpperCamelCase  :int    						=	mask_time_length
        __UpperCamelCase  :Any    						=	mask_time_min_masks
        __UpperCamelCase  :Any    						=	mask_feature_prob
        __UpperCamelCase  :str    						=	mask_feature_length
        __UpperCamelCase  :Tuple    						=	mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :Dict    						=	num_codevector_groups
        __UpperCamelCase  :Optional[int]    						=	contrastive_logits_temperature
        __UpperCamelCase  :Union[str, Any]    						=	feat_quantizer_dropout
        __UpperCamelCase  :List[str]    						=	num_negatives
        __UpperCamelCase  :Union[str, Any]    						=	codevector_dim
        __UpperCamelCase  :int    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :List[Any]    						=	ctc_loss_reduction
        __UpperCamelCase  :int    						=	ctc_zero_infinity

        # pretraining loss
        __UpperCamelCase  :Optional[Any]    						=	replace_prob







    @property
    def     UpperCamelCase__     (			self) ->			Dict:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





