




from importlib import import_module

from .logging import get_logger


_lowerCamelCase			:	Tuple							 =     get_logger(__name__)


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			int      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			int=None)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					attrs or []
           if module is not None:
                  for key in module.__dict__:
                         if key in attrs or not key.startswith('''__'''):
                                setattr(self      ,				UpperCAmelCase__      ,				getattr(UpperCAmelCase__      ,				UpperCAmelCase__))
           A__   				=					module._original_module if isinstance(UpperCAmelCase__      ,				_PatchedModuleObj) else module


class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   []


    def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]=None)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					obj
           A__   				=					target
           A__   				=					new
           A__   				=					target.split('''.''')[0]
           A__   				=					{}
           A__   				=					attrs or []


    def __enter__(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

           *A__				,							A__   				=					self.target.split('''.''')

           # Patch modules:
           # it's used to patch attributes of submodules like "os.path.join";
           # in this case we need to patch "os" and "os.path"

           for i in range(len(UpperCAmelCase__)):
                  try:
                         A__   				=					import_module('''.'''.join(submodules[: i + 1]))
                  except ModuleNotFoundError:
                         continue
                  # We iterate over all the globals in self.obj in case we find "os" or "os.path"
                  for attr in self.obj.__dir__():
                         A__   				=					getattr(self.obj      ,				UpperCAmelCase__)
                         # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                         # This allows to patch renamed modules like "from os import path as ospath".
                         if obj_attr is submodule or (
                             (isinstance(UpperCAmelCase__      ,				_PatchedModuleObj) and obj_attr._original_module is submodule)
                         ):
                                A__   				=					obj_attr
                                # patch at top level
                                setattr(self.obj      ,				UpperCAmelCase__      ,				_PatchedModuleObj(UpperCAmelCase__      ,				attrs=self.attrs))
                                A__   				=					getattr(self.obj      ,				UpperCAmelCase__)
                                # construct lower levels patches
                                for key in submodules[i + 1 :]:
                                       setattr(UpperCAmelCase__      ,				UpperCAmelCase__      ,				_PatchedModuleObj(getattr(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)      ,				attrs=self.attrs))
                                       A__   				=					getattr(UpperCAmelCase__      ,				UpperCAmelCase__)
                                # finally set the target attribute
                                setattr(UpperCAmelCase__      ,				UpperCAmelCase__      ,				self.new)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

           if submodules:  # if it's an attribute of a submodule like "os.path.join"
                  try:
                         A__   				=					getattr(import_module('''.'''.join(UpperCAmelCase__))      ,				UpperCAmelCase__)
                  except (AttributeError, ModuleNotFoundError):
                         return
                  # We iterate over all the globals in self.obj in case we find "os.path.join"
                  for attr in self.obj.__dir__():
                         # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                         # This allows to patch renamed attributes like "from os.path import join as pjoin".
                         if getattr(self.obj      ,				UpperCAmelCase__) is attr_value:
                                A__   				=					getattr(self.obj      ,				UpperCAmelCase__)
                                setattr(self.obj      ,				UpperCAmelCase__      ,				self.new)
           elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
                  A__   				=					globals()['''__builtins__'''][target_attr]
                  setattr(self.obj      ,				UpperCAmelCase__      ,				self.new)
           else:
                  raise RuntimeError(f"""Tried to patch attribute {target_attr} instead of a submodule.""")


    def __exit__(    self   :			Any      ,				*UpperCAmelCase__   :			List[Any])		->Optional[Any]:





           '''simple docstring'''

           for attr in list(self.original):
                  setattr(self.obj      ,				UpperCAmelCase__      ,				self.original.pop(UpperCAmelCase__))


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Any:





           '''simple docstring'''

           self.__enter__()
           self._active_patches.append(self)





    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[int]:





           '''simple docstring'''

           try:
                  self._active_patches.remove(self)
           except ValueError:
                  # If the patch hasn't been started this will fail
                  return None

           return self.__exit__()








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""")

_lowerCamelCase			:	Any							 =     logging.getLogger(__name__)


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






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


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Train language if it is different from the evaluation language.'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      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=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                '''with private models).'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}	,      )







def SCREAMING_SNAKE_CASE       ( )     -> str:



       """simple docstring"""

       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'''				,       lowercase_		)

       # 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(lowercase_		)
       datasets.utils.logging.set_verbosity(lowercase_		)
       transformers.utils.logging.set_verbosity(lowercase_		)
       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(lowercase_		)

       # 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=lowercase_				,       idalabel={str(lowercase_		): label for i, label in enumerate(lowercase_		)}				,       labelaid={label: i for i, label in enumerate(lowercase_		)}				,       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=lowercase_				,       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(lowercase_		):
              # Tokenize the texts
              return tokenizer(
                  examples['''premise''']				,       examples['''hypothesis''']				,       padding=lowercase_				,       max_length=data_args.max_seq_length				,       truncation=lowercase_				,       )

       if training_args.do_train:
              if data_args.max_train_samples is not None:
                     A__   				=					min(len(lowercase_		)				,       data_args.max_train_samples		)
                     A__   				=					train_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''train dataset map pre-processing'''		):
                     A__   				=					train_dataset.map(
                         lowercase_				,       batched=lowercase_				,       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(lowercase_		)		)				,       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(lowercase_		)				,       data_args.max_eval_samples		)
                     A__   				=					eval_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''validation dataset map pre-processing'''		):
                     A__   				=					eval_dataset.map(
                         lowercase_				,       batched=lowercase_				,       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(lowercase_		)				,       data_args.max_predict_samples		)
                     A__   				=					predict_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''prediction dataset map pre-processing'''		):
                     A__   				=					predict_dataset.map(
                         lowercase_				,       batched=lowercase_				,       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(lowercase_		):
              A__   				=					p.predictions[0] if isinstance(p.predictions				,       lowercase_		) else p.predictions
              A__   				=					np.argmax(lowercase_				,       axis=1		)
              return metric.compute(predictions=lowercase_				,       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(lowercase_				,       pad_to_multiple_of=8		)
       else:
              A__   				=					None

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

       # 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=lowercase_		)
              A__   				=					train_result.metrics
              A__   				=					(
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_		)
              )
              A__   				=					min(lowercase_				,       len(lowercase_		)		)

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
  main()


