
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''')

A		: Dict								=     logging.getLogger(__name__)



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







def 	UpperCamelCase__  (     )  ->     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.

						_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"""		,   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()

						_lowercase					       =					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.
						_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(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.
						_lowercase					       =					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		,   )
						_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=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:
												_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(SCREAMING_SNAKE_CASE_	:	Union[str, Any]       ):
												# 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:
																		_lowercase					       =					min(len(SCREAMING_SNAKE_CASE_       )		,   data_args.max_train_samples       )
																		_lowercase					       =					train_dataset.select(range(SCREAMING_SNAKE_CASE_       )       )
												with training_args.main_process_first(desc="""train dataset map pre-processing"""       ):
																		_lowercase					       =					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:
																		_lowercase					       =					min(len(SCREAMING_SNAKE_CASE_       )		,   data_args.max_eval_samples       )
																		_lowercase					       =					eval_dataset.select(range(SCREAMING_SNAKE_CASE_       )       )
												with training_args.main_process_first(desc="""validation dataset map pre-processing"""       ):
																		_lowercase					       =					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:
																		_lowercase					       =					min(len(SCREAMING_SNAKE_CASE_       )		,   data_args.max_predict_samples       )
																		_lowercase					       =					predict_dataset.select(range(SCREAMING_SNAKE_CASE_       )       )
												with training_args.main_process_first(desc="""prediction dataset map pre-processing"""       ):
																		_lowercase					       =					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
						_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(SCREAMING_SNAKE_CASE_	:	EvalPrediction       ):
												_lowercase					       =					p.predictions[0] if isinstance(p.predictions		,   SCREAMING_SNAKE_CASE_       ) else p.predictions
												_lowercase					       =					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:
												_lowercase					       =					default_data_collator
						elif training_args.fpaa:
												_lowercase					       =					DataCollatorWithPadding(SCREAMING_SNAKE_CASE_		,   pad_to_multiple_of=8       )
						else:
												_lowercase					       =					None

						# Initialize our Trainer
						_lowercase					       =					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:
												_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=SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					train_result.metrics
												_lowercase					       =					(
												    data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE_       )
												)
												_lowercase					       =					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 ***"""       )
												_lowercase					       =					trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE_       )

												_lowercase					       =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					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 ***"""       )
												_lowercase       ,						_lowercase       ,						_lowercase					       =					trainer.predict(SCREAMING_SNAKE_CASE_		,   metric_key_prefix="""predict"""       )

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

												trainer.log_metrics("""predict"""		,   SCREAMING_SNAKE_CASE_       )
												trainer.save_metrics("""predict"""		,   SCREAMING_SNAKE_CASE_       )

												_lowercase					       =					np.argmax(SCREAMING_SNAKE_CASE_		,   axis=1       )
												_lowercase					       =					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_       ):
																														_lowercase					       =					label_list[item]
																														writer.write(f"""{index}\t{item}\n"""       )


if __name__ == "__main__":
		main()

from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching




def 	UpperCamelCase__  (     )  ->     List[str]:
						import os as original_os
						from os import path as original_path
						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join

						_lowercase					       =					"""__test_patch_submodule_mock__"""
						with patch_submodule(_test_patching		,   """os.path.join"""		,   SCREAMING_SNAKE_CASE_       ):
												# Every way to access os.path.join must be patched, and the rest must stay untouched

												# check os.path.join
												assert isinstance(_test_patching.os		,   _PatchedModuleObj       )
												assert isinstance(_test_patching.os.path		,   _PatchedModuleObj       )
												assert _test_patching.os.path.join is mock

												# check path.join
												assert isinstance(_test_patching.path		,   _PatchedModuleObj       )
												assert _test_patching.path.join is mock

												# check join
												assert _test_patching.join is mock

												# check that the other attributes are untouched
												assert _test_patching.os.rename is original_rename
												assert _test_patching.path.dirname is original_dirname
												assert _test_patching.os.path.dirname is original_dirname

												# Even renamed modules or objects must be patched

												# check renamed_os.path.join
												assert isinstance(_test_patching.renamed_os		,   _PatchedModuleObj       )
												assert isinstance(_test_patching.renamed_os.path		,   _PatchedModuleObj       )
												assert _test_patching.renamed_os.path.join is mock

												# check renamed_path.join
												assert isinstance(_test_patching.renamed_path		,   _PatchedModuleObj       )
												assert _test_patching.renamed_path.join is mock

												# check renamed_join
												assert _test_patching.renamed_join is mock

												# check that the other attributes are untouched
												assert _test_patching.renamed_os.rename is original_rename
												assert _test_patching.renamed_path.dirname is original_dirname
												assert _test_patching.renamed_os.path.dirname is original_dirname

						# check that everthing is back to normal when the patch is over

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join




def 	UpperCamelCase__  (     )  ->     int:
						assert _test_patching.open is open

						_lowercase					       =					"""__test_patch_submodule_builtin_mock__"""
						# _test_patching has "open" in its globals
						assert _test_patching.open is open
						with patch_submodule(_test_patching		,   """open"""		,   SCREAMING_SNAKE_CASE_       ):
												assert _test_patching.open is mock

						# check that everthing is back to normal when the patch is over

						assert _test_patching.open is open




def 	UpperCamelCase__  (     )  ->     List[Any]:
						# pandas.read_csv is not present in _test_patching
						_lowercase					       =					"""__test_patch_submodule_missing_mock__"""
						with patch_submodule(_test_patching		,   """pandas.read_csv"""		,   SCREAMING_SNAKE_CASE_       ):
												pass




def 	UpperCamelCase__  (     )  ->     Tuple:
						# builtin should always be mocked even if they're not in the globals
						# in case they're loaded at one point
						_lowercase					       =					"""__test_patch_submodule_missing_builtin_mock__"""
						# _test_patching doesn't have "len" in its globals
						assert getattr(_test_patching		,   """len"""		,   SCREAMING_SNAKE_CASE_       ) is None
						with patch_submodule(_test_patching		,   """len"""		,   SCREAMING_SNAKE_CASE_       ):
												assert _test_patching.len is mock
						assert _test_patching.len is len




def 	UpperCamelCase__  (     )  ->     List[str]:
						_lowercase					       =					"""__test_patch_submodule_start_and_stop_mock__"""
						_lowercase					       =					patch_submodule(_test_patching		,   """open"""		,   SCREAMING_SNAKE_CASE_       )
						assert _test_patching.open is open
						patch.start()
						assert _test_patching.open is mock
						patch.stop()
						assert _test_patching.open is open




def 	UpperCamelCase__  (     )  ->     Union[str, Any]:
						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						_lowercase					       =					"""__test_patch_submodule_successive_join__"""
						_lowercase					       =					"""__test_patch_submodule_successive_dirname__"""
						_lowercase					       =					"""__test_patch_submodule_successive_rename__"""
						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename

						with patch_submodule(_test_patching		,   """os.path.join"""		,   SCREAMING_SNAKE_CASE_       ):
												with patch_submodule(_test_patching		,   """os.rename"""		,   SCREAMING_SNAKE_CASE_       ):
																		with patch_submodule(_test_patching		,   """os.path.dirname"""		,   SCREAMING_SNAKE_CASE_       ):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

    # try another order
						with patch_submodule(_test_patching		,   """os.rename"""		,   SCREAMING_SNAKE_CASE_       ):
												with patch_submodule(_test_patching		,   """os.path.join"""		,   SCREAMING_SNAKE_CASE_       ):
																		with patch_submodule(_test_patching		,   """os.path.dirname"""		,   SCREAMING_SNAKE_CASE_       ):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename




def 	UpperCamelCase__  (     )  ->     List[Any]:
						_lowercase					       =					"""__test_patch_submodule_doesnt_exist_mock__"""
						with patch_submodule(_test_patching		,   """__module_that_doesn_exist__.__attribute_that_doesn_exist__"""		,   SCREAMING_SNAKE_CASE_       ):
												pass
						with patch_submodule(_test_patching		,   """os.__attribute_that_doesn_exist__"""		,   SCREAMING_SNAKE_CASE_       ):
												pass