






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")

_SCREAMING_SNAKE_CASE   =  logging.getLogger(__name__)






@dataclass
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       __lowerCAmelCase    :		Optional[int]					=field(
           default=1_2_8  ,						metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }  ,						)
       __lowerCAmelCase    :		bool					=field(
           default=_a  ,						metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
       __lowerCAmelCase    :		bool					=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.'''
               )
           }  ,						)
       __lowerCAmelCase    :		Optional[int]					=field(
           default=_a  ,						metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                   '''value if set.'''
               )
           }  ,						)
       __lowerCAmelCase    :		Optional[int]					=field(
           default=_a  ,						metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                   '''value if set.'''
               )
           }  ,						)
       __lowerCAmelCase    :		Optional[int]					=field(
           default=_a  ,						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"""


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





def        _snake_case   ()					->	Union[str, Any]:
   # 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.

   _lowercase              =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
   _lowercase       ,       _lowercase       ,       _lowercase              =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'      ,    _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()

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

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

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

   # Labels
   _lowercase              =len(_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.
   _lowercase              =AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,    num_labels=_snake_case      ,    idalabel={str(_snake_case): label for i, label in enumerate(_snake_case)}      ,    labelaid={label: i for i, label in enumerate(_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      ,    )
   _lowercase              =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      ,    )
   _lowercase              =AutoModelForSequenceClassification.from_pretrained(
       model_args.model_name_or_path      ,    from_tf=bool('.ckpt' in model_args.model_name_or_path)      ,    config=_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:
      _lowercase              ='max_length'
   else:
      # We will pad later, dynamically at batch creation, to the max sequence length in each batch
      _lowercase              =False

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

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

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

   # Initialize our Trainer
   _lowercase              =Trainer(
       model=_snake_case      ,    args=_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=_snake_case      ,    tokenizer=_snake_case      ,    data_collator=_snake_case      ,    )

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

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

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

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

      _lowercase              =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_snake_case)
      _lowercase              =min(_snake_case      ,    len(_snake_case))

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

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

      _lowercase              =(
          data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_snake_case)
      )
      _lowercase              =min(_snake_case      ,    len(_snake_case))

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

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


if __name__ == "__main__":
    main()










# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE   =  TypeVar("T")






class 					SCREAMING_SNAKE_CASE_  (       Generic[T] ):
       """simple docstring"""


       def __init__(				self	:List[Any],      snake_case	:bool = True):



          """simple docstring"""

          _lowercase              ={}  # dictionary of lists
          _lowercase              =directed





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:T,      snake_case	:T):



          """simple docstring"""

          if not self.directed:  # For undirected graphs
             # if both source vertex and destination vertex are both present in the
             # adjacency list, add destination vertex to source vertex list of adjacent
             # vertices and add source vertex to destination vertex list of adjacent
             # vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                self.adj_list[destination_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination vertex
             # to source vertex list of adjacent vertices, then create a new vertex with
             # destination vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[source_vertex]
             # if only destination vertex is present in adjacency list, add source vertex
             # to destination vertex list of adjacent vertices, then create a new vertex
             # with source vertex as key and assign a list containing the source vertex
             # as it's first adjacent vertex.
             elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(snake_case)
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and assign a list
             # containing the destination vertex as it's first adjacent vertex also
             # create a new vertex with destination vertex as key and assign a list
             # containing the source vertex as it's first adjacent vertex.
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[source_vertex]
          else:  # For directed graphs
             # if both source vertex and destination vertex are present in adjacency
             # list, add destination vertex to source vertex list of adjacent vertices.
             if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
             # if only source vertex is present in adjacency list, add destination
             # vertex to source vertex list of adjacent vertices and create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(snake_case)
                _lowercase              =[]
             # if only destination vertex is present in adjacency list, create a new
             # vertex with source vertex as key and assign a list containing destination
             # vertex as first adjacent vertex
             elif destination_vertex in self.adj_list:
                _lowercase              =[destination_vertex]
             # if both source vertex and destination vertex are not present in adjacency
             # list, create a new vertex with source vertex as key and a list containing
             # destination vertex as it's first adjacent vertex. Then create a new vertex
             # with destination vertex as key, which has no adjacent vertex
             else:
                _lowercase              =[destination_vertex]
                _lowercase              =[]

          return self





       def __repr__(				self	:Optional[int]):



          """simple docstring"""

          return pformat(self.adj_list)


