



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						  =		logging.getLogger(__name__)



@dataclass
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				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.'''
             )
         }			,      )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}     )
     UpperCamelCase__		    =				field(
         default=A__			,      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.'''
             )
         }			,      )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                 '''value if set.'''
             )
         }			,      )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                 '''value if set.'''
             )
         }			,      )
     UpperCamelCase__		    =				field(
         default=A__			,      metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
                 '''value if set.'''
             )
         }			,      )







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







def 		lowerCAmelCase__    (					)->  Optional[int]:
    # 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__		 =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
    A__ ,	A__ ,	A__		 =					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''' ,       UpperCamelCase_	)

    # 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()

    A__		 =					training_args.get_process_log_level()
    logger.setLevel(UpperCamelCase_	)
    datasets.utils.logging.set_verbosity(UpperCamelCase_	)
    transformers.utils.logging.set_verbosity(UpperCamelCase_	)
    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.
    A__		 =					None
    if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
        A__		 =					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:
            A__		 =					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:
            A__		 =					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 ,       )
        A__		 =					train_dataset.features['''label'''].names

    if training_args.do_eval:
        A__		 =					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 ,       )
        A__		 =					eval_dataset.features['''label'''].names

    if training_args.do_predict:
        A__		 =					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 ,       )
        A__		 =					predict_dataset.features['''label'''].names

    # Labels
    A__		 =					len(UpperCamelCase_	)

    # Load pretrained model and tokenizer
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    A__		 =					AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path ,       num_labels=UpperCamelCase_ ,       idalabel={str(UpperCamelCase_	): label for i, label in enumerate(UpperCamelCase_	)} ,       labelaid={label: i for i, label in enumerate(UpperCamelCase_	)} ,       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 ,       )
    A__		 =					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 ,       )
    A__		 =					AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path ,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path	) ,       config=UpperCamelCase_ ,       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:
        A__		 =					'''max_length'''
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        A__		 =					False

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

    if training_args.do_train:
        if data_args.max_train_samples is not None:
            A__		 =					min(len(UpperCamelCase_	) ,       data_args.max_train_samples	)
            A__		 =					train_dataset.select(range(UpperCamelCase_	)	)
        with training_args.main_process_first(desc='''train dataset map pre-processing'''	):
            A__		 =					train_dataset.map(
                UpperCamelCase_ ,       batched=UpperCamelCase_ ,       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(UpperCamelCase_	)	) ,       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:
            A__		 =					min(len(UpperCamelCase_	) ,       data_args.max_eval_samples	)
            A__		 =					eval_dataset.select(range(UpperCamelCase_	)	)
        with training_args.main_process_first(desc='''validation dataset map pre-processing'''	):
            A__		 =					eval_dataset.map(
                UpperCamelCase_ ,       batched=UpperCamelCase_ ,       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:
            A__		 =					min(len(UpperCamelCase_	) ,       data_args.max_predict_samples	)
            A__		 =					predict_dataset.select(range(UpperCamelCase_	)	)
        with training_args.main_process_first(desc='''prediction dataset map pre-processing'''	):
            A__		 =					predict_dataset.map(
                UpperCamelCase_ ,       batched=UpperCamelCase_ ,       load_from_cache_file=not data_args.overwrite_cache ,       desc='''Running tokenizer on prediction dataset''' ,       )

    # Get the metric function
    A__		 =					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(UpperCamelCase_				:       EvalPrediction	):
        A__		 =					p.predictions[0] if isinstance(p.predictions ,       UpperCamelCase_	) else p.predictions
        A__		 =					np.argmax(UpperCamelCase_ ,       axis=1	)
        return metric.compute(predictions=UpperCamelCase_ ,       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:
        A__		 =					default_data_collator
    elif training_args.fpaa:
        A__		 =					DataCollatorWithPadding(UpperCamelCase_ ,       pad_to_multiple_of=8	)
    else:
        A__		 =					None

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

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

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

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

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

        A__		 =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase_	)
        A__		 =					min(UpperCamelCase_ ,       len(UpperCamelCase_	)	)

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

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

        A__		 =					(
            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCamelCase_	)
        )
        A__		 =					min(UpperCamelCase_ ,       len(UpperCamelCase_	)	)

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

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


if __name__ == "__main__":
   main()









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		{
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''bert'''




     def __init__(				self ,						a__=3_0_5_2_2 ,						a__=7_6_8 ,						a__=1_2 ,						a__=1_2 ,						a__=3_0_7_2 ,						a__="gelu" ,						a__=0.1 ,						a__=0.1 ,						a__=5_1_2 ,						a__=2 ,						a__=0.0_2 ,						a__=1e-12 ,						a__=0 ,						a__="absolute" ,						a__=True ,						a__=None ,						**a__ ,						):
         super().__init__(pad_token_id=a__ ,						**a__)

         A__		 =					vocab_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_attention_heads
         A__		 =					hidden_act
         A__		 =					intermediate_size
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					type_vocab_size
         A__		 =					initializer_range
         A__		 =					layer_norm_eps
         A__		 =					position_embedding_type
         A__		 =					use_cache
         A__		 =					classifier_dropout







class     _UpperCAmelCase			(       A__     ):




     @property
     def 		snake_case_		(				self):
         if self.task == "multiple-choice":
             A__		 =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__		 =					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ])
