
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

lowercase__							:							List[Any]									=			logging.getLogger(__name__)






@dataclass
class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							_snake_case     		=  field(
							    default=128   ,		metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}    )
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={
							        'help': (
							            'Whether to pad all samples to `max_seq_length`. '
							            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
							        )
							    }   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
							            'value if set.'
							        )
							    }   ,		)






@dataclass
class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'}    )
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Train language if it is different from the evaluation language.'}    )
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}   ,		)
							_snake_case     		=  field(
							    default='main'   ,		metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }   ,		)
							_snake_case     		=  field(
							    default=snake_case__   ,		metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}   ,		)





def 	A_ (							)      ->    Union[str, Any]:
						__UpperCamelCase       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
						__UpperCamelCase       =			parser.parse_args_into_dataclasses()

						# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
						# information sent is the one passed as arguments along with your Python/PyTorch versions.
						send_example_telemetry('''run_xnli''' ,      __SCREAMING_SNAKE_CASE )

						# 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 )] ,      )

						if training_args.should_log:
												# The default of training_args.log_level is passive, so we set log level at info here to have that default.
												transformers.utils.logging.set_verbosity_info()

						__UpperCamelCase       =			training_args.get_process_log_level()
						logger.setLevel(__SCREAMING_SNAKE_CASE )
						datasets.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE )
						transformers.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

						# 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}" )
						logger.info(f"Training/evaluation parameters {training_args}" )

						# Detecting last checkpoint.
						__UpperCamelCase       =			None
						if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
												__UpperCamelCase       =			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.''' )

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

						# In distributed training, the load_dataset function guarantees that only one local process can concurrently
						# download the dataset.
						# Downloading and loading xnli dataset from the hub.
						if training_args.do_train:
												if model_args.train_language is None:
																		__UpperCamelCase       =			load_dataset(
																		    '''xnli''' ,      model_args.language ,      split='''train''' ,      cache_dir=model_args.cache_dir ,      use_auth_token=True if model_args.use_auth_token else None ,      )
												else:
																		__UpperCamelCase       =			load_dataset(
																		    '''xnli''' ,      model_args.train_language ,      split='''train''' ,      cache_dir=model_args.cache_dir ,      use_auth_token=True if model_args.use_auth_token else None ,      )
												__UpperCamelCase       =			train_dataset.features["label"].names

						if training_args.do_eval:
												__UpperCamelCase       =			load_dataset(
												    '''xnli''' ,      model_args.language ,      split='''validation''' ,      cache_dir=model_args.cache_dir ,      use_auth_token=True if model_args.use_auth_token else None ,      )
												__UpperCamelCase       =			eval_dataset.features["label"].names

						if training_args.do_predict:
												__UpperCamelCase       =			load_dataset(
												    '''xnli''' ,      model_args.language ,      split='''test''' ,      cache_dir=model_args.cache_dir ,      use_auth_token=True if model_args.use_auth_token else None ,      )
												__UpperCamelCase       =			predict_dataset.features["label"].names

						# Labels
						__UpperCamelCase       =			len(__SCREAMING_SNAKE_CASE )

						# Load pretrained model and tokenizer
						# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						__UpperCamelCase       =			AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,      num_labels=__SCREAMING_SNAKE_CASE ,      idalabel={str(__SCREAMING_SNAKE_CASE ): label for i, label in enumerate(__SCREAMING_SNAKE_CASE )} ,      labelaid={label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE )} ,      finetuning_task='''xnli''' ,      cache_dir=model_args.cache_dir ,      revision=model_args.model_revision ,      use_auth_token=True if model_args.use_auth_token else None ,      )
						__UpperCamelCase       =			AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,      do_lower_case=model_args.do_lower_case ,      cache_dir=model_args.cache_dir ,      use_fast=model_args.use_fast_tokenizer ,      revision=model_args.model_revision ,      use_auth_token=True if model_args.use_auth_token else None ,      )
						__UpperCamelCase       =			AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) ,      config=__SCREAMING_SNAKE_CASE ,      cache_dir=model_args.cache_dir ,      revision=model_args.model_revision ,      use_auth_token=True if model_args.use_auth_token else None ,      ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,      )

						# Preprocessing the datasets
						# Padding strategy
						if data_args.pad_to_max_length:
												__UpperCamelCase       =			"max_length"
						else:
												# We will pad later, dynamically at batch creation, to the max sequence length in each batch
												__UpperCamelCase       =			False

						def preprocess_function(snake_case							:   Dict ):
												# Tokenize the texts
												return tokenizer(
												    examples['''premise'''] ,      examples['''hypothesis'''] ,      padding=__SCREAMING_SNAKE_CASE ,      max_length=data_args.max_seq_length ,      truncation=__SCREAMING_SNAKE_CASE ,      )

						if training_args.do_train:
												if data_args.max_train_samples is not None:
																		__UpperCamelCase       =			min(len(__SCREAMING_SNAKE_CASE ) ,      data_args.max_train_samples )
																		__UpperCamelCase       =			train_dataset.select(range(__SCREAMING_SNAKE_CASE ) )
												with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
																		__UpperCamelCase       =			train_dataset.map(
																		    __SCREAMING_SNAKE_CASE ,      batched=__SCREAMING_SNAKE_CASE ,      load_from_cache_file=not data_args.overwrite_cache ,      desc='''Running tokenizer on train dataset''' ,      )
												# Log a few random samples from the training set:
												for index in random.sample(range(len(__SCREAMING_SNAKE_CASE ) ) ,      3 ):
																		logger.info(f"Sample {index} of the training set: {train_dataset[index]}." )

						if training_args.do_eval:
												if data_args.max_eval_samples is not None:
																		__UpperCamelCase       =			min(len(__SCREAMING_SNAKE_CASE ) ,      data_args.max_eval_samples )
																		__UpperCamelCase       =			eval_dataset.select(range(__SCREAMING_SNAKE_CASE ) )
												with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
																		__UpperCamelCase       =			eval_dataset.map(
																		    __SCREAMING_SNAKE_CASE ,      batched=__SCREAMING_SNAKE_CASE ,      load_from_cache_file=not data_args.overwrite_cache ,      desc='''Running tokenizer on validation dataset''' ,      )

						if training_args.do_predict:
												if data_args.max_predict_samples is not None:
																		__UpperCamelCase       =			min(len(__SCREAMING_SNAKE_CASE ) ,      data_args.max_predict_samples )
																		__UpperCamelCase       =			predict_dataset.select(range(__SCREAMING_SNAKE_CASE ) )
												with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
																		__UpperCamelCase       =			predict_dataset.map(
																		    __SCREAMING_SNAKE_CASE ,      batched=__SCREAMING_SNAKE_CASE ,      load_from_cache_file=not data_args.overwrite_cache ,      desc='''Running tokenizer on prediction dataset''' ,      )

    # Get the metric function
						__UpperCamelCase       =			evaluate.load('''xnli''' )

						# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
						# predictions and label_ids field) and has to return a dictionary string to float.
						def compute_metrics(snake_case							:   List[str] ):
												__UpperCamelCase       =			p.predictions[0] if isinstance(p.predictions ,      __SCREAMING_SNAKE_CASE ) else p.predictions
												__UpperCamelCase       =			np.argmax(__SCREAMING_SNAKE_CASE ,      axis=1 )
												return metric.compute(predictions=__SCREAMING_SNAKE_CASE ,      references=p.label_ids )

						# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
						if data_args.pad_to_max_length:
												__UpperCamelCase       =			default_data_collator
						elif training_args.fpaa:
												__UpperCamelCase       =			DataCollatorWithPadding(__SCREAMING_SNAKE_CASE ,      pad_to_multiple_of=8 )
						else:
												__UpperCamelCase       =			None

						# Initialize our Trainer
						__UpperCamelCase       =			Trainer(
						    model=__SCREAMING_SNAKE_CASE ,      args=__SCREAMING_SNAKE_CASE ,      train_dataset=train_dataset if training_args.do_train else None ,      eval_dataset=eval_dataset if training_args.do_eval else None ,      compute_metrics=__SCREAMING_SNAKE_CASE ,      tokenizer=__SCREAMING_SNAKE_CASE ,      data_collator=__SCREAMING_SNAKE_CASE ,      )

						# Training
						if training_args.do_train:
												__UpperCamelCase       =			None
												if training_args.resume_from_checkpoint is not None:
																		__UpperCamelCase       =			training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		__UpperCamelCase       =			last_checkpoint
												__UpperCamelCase       =			trainer.train(resume_from_checkpoint=__SCREAMING_SNAKE_CASE )
												__UpperCamelCase       =			train_result.metrics
												__UpperCamelCase       =			(
												    data_args.max_train_samples if data_args.max_train_samples is not None else len(__SCREAMING_SNAKE_CASE )
												)
												__UpperCamelCase       =			min(__SCREAMING_SNAKE_CASE ,      len(__SCREAMING_SNAKE_CASE ) )

												trainer.save_model()  # Saves the tokenizer too for easy upload

												trainer.log_metrics('''train''' ,      __SCREAMING_SNAKE_CASE )
												trainer.save_metrics('''train''' ,      __SCREAMING_SNAKE_CASE )
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												logger.info('''*** Evaluate ***''' )
												__UpperCamelCase       =			trainer.evaluate(eval_dataset=__SCREAMING_SNAKE_CASE )

												__UpperCamelCase       =			data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__SCREAMING_SNAKE_CASE )
												__UpperCamelCase       =			min(__SCREAMING_SNAKE_CASE ,      len(__SCREAMING_SNAKE_CASE ) )

												trainer.log_metrics('''eval''' ,      __SCREAMING_SNAKE_CASE )
												trainer.save_metrics('''eval''' ,      __SCREAMING_SNAKE_CASE )

						# Prediction
						if training_args.do_predict:
												logger.info('''*** Predict ***''' )
												__UpperCamelCase       =			trainer.predict(__SCREAMING_SNAKE_CASE ,      metric_key_prefix='''predict''' )

												__UpperCamelCase       =			(
												    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__SCREAMING_SNAKE_CASE )
												)
												__UpperCamelCase       =			min(__SCREAMING_SNAKE_CASE ,      len(__SCREAMING_SNAKE_CASE ) )

												trainer.log_metrics('''predict''' ,      __SCREAMING_SNAKE_CASE )
												trainer.save_metrics('''predict''' ,      __SCREAMING_SNAKE_CASE )

												__UpperCamelCase       =			np.argmax(__SCREAMING_SNAKE_CASE ,      axis=1 )
												__UpperCamelCase       =			os.path.join(training_args.output_dir ,      '''predictions.txt''' )
												if trainer.is_world_process_zero():
																		with open(__SCREAMING_SNAKE_CASE ,      '''w''' ) as writer:
																								writer.write('''index\tprediction\n''' )
																								for index, item in enumerate(__SCREAMING_SNAKE_CASE ):
																														__UpperCamelCase       =			label_list[item]
																														writer.write(f"{index}\t{item}\n" )


if __name__ == "__main__":
				main()






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


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							Union[str, Any]									=			{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'mctct'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=8065     ,	SCREAMING_SNAKE_CASE_=1536     ,	SCREAMING_SNAKE_CASE_=36     ,	SCREAMING_SNAKE_CASE_=6144     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=384     ,	SCREAMING_SNAKE_CASE_=920     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=(7,)     ,	SCREAMING_SNAKE_CASE_=(3,)     ,	SCREAMING_SNAKE_CASE_=80     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="sum"     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_     ,	pad_token_id=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			vocab_size
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			attention_head_dim
													__UpperCamelCase       =			max_position_embeddings
													__UpperCamelCase       =			layer_norm_eps
													__UpperCamelCase       =			layerdrop
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			pad_token_id
													__UpperCamelCase       =			bos_token_id
													__UpperCamelCase       =			eos_token_id
													__UpperCamelCase       =			conv_glu_dim
													__UpperCamelCase       =			conv_dropout
													__UpperCamelCase       =			num_conv_layers
													__UpperCamelCase       =			input_feat_per_channel
													__UpperCamelCase       =			input_channels
													__UpperCamelCase       =			conv_channels
													__UpperCamelCase       =			ctc_loss_reduction
													__UpperCamelCase       =			ctc_zero_infinity

													# prevents config testing fail with exporting to json
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)

													if len(self.conv_kernel			) != self.num_conv_layers:
																			raise ValueError(
																			    '''Configuration for convolutional module is incorrect. '''
																			    '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
																			    F"but is `len(config.conv_kernel) = {len(self.conv_kernel			)}`, "
																			    F"`config.num_conv_layers = {self.num_conv_layers}`."			)




