


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			:







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




@dataclass
class        _UpperCamelCase			:







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




def 				SCREAMING_SNAKE_CASE       (	)     ->	Tuple:
						_a     			=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
						_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'				,  _A					)

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

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

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

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

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

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

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

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

												_a     			=      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_A					)
												_a     			=      min(_A				,  len(_A					)					)

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

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

												_a     			=      (
												    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_A					)
												)
												_a     			=      min(_A				,  len(_A					)					)

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

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


if __name__ == "__main__":
		main()






'''simple docstring'''







import argparse
from collections import defaultdict

import yaml


lowerCAmelCase     :  Dict											=    """docs/source/en/_toctree.yml"""






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							defaultdict(_A       )
       _lowerCAmelCase     :				Tuple       =							[]
       _lowerCAmelCase     :				Dict       =							[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}       )
              else:
                     new_doc_list.append(_A       )

       _lowerCAmelCase     :				str       =							new_doc_list
       _lowerCAmelCase     :				List[Any]       =							[key for key, value in counts.items() if value > 1]

       _lowerCAmelCase     :				List[str]       =							[]
       for duplicate_key in duplicates:
              _lowerCAmelCase     :				Any       =							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}       )
              if len(_A       ) > 1:
                     raise ValueError(
                         f'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'       )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]       )
       _lowerCAmelCase     :				Any       =							sorted(_A ,    key=lambda _A       : s["title"].lower()       )

       # "overview" gets special treatment and is always first
       if len(_A       ) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'       )

       overview_doc.extend(_A       )

       # Sort
       return overview_doc






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Union[str, Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Tuple       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				str       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Dict       =							0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       _lowerCAmelCase     :				Optional[int]       =							api_doc[scheduler_idx]['sections']
       _lowerCAmelCase     :				Any       =							clean_doc_toc(_A       )

       _lowerCAmelCase     :				int       =							False
       if new_scheduler_doc != scheduler_doc:
              _lowerCAmelCase     :				str       =							True
              if overwrite:
                     _lowerCAmelCase     :				int       =							new_scheduler_doc

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				Dict       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Optional[Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Optional[Any]       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				Union[str, Any]       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Optional[int]       =							0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       _lowerCAmelCase     :				List[Any]       =							False
       _lowerCAmelCase     :				List[Any]       =							api_doc[pipeline_idx]['sections']
       _lowerCAmelCase     :				Union[str, Any]       =							[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     _lowerCAmelCase     :				Tuple       =							pipeline_doc['section']
                     _lowerCAmelCase     :				Optional[int]       =							clean_doc_toc(_A       )
                     if overwrite:
                            _lowerCAmelCase     :				str       =							new_sub_pipeline_doc
              new_pipeline_docs.append(_A       )

       # sort overall pipeline doc
       _lowerCAmelCase     :				str       =							clean_doc_toc(_A       )

       if new_pipeline_docs != pipeline_docs:
              _lowerCAmelCase     :				Any       =							True
              if overwrite:
                     _lowerCAmelCase     :				Optional[int]       =							new_pipeline_docs

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				str       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       lowerCAmelCase     :  List[Any]											=    parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

