





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_    =				logging.getLogger(__name__)



@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     Optional[int]       			=      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.'''
			        )
			    },     )
			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 					__lowerCAmelCase       :
			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      lowerCamelCase_						(				)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:    List[Any]				=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				snake_case_			,							snake_case_			,							snake_case_						:    Optional[int]				=  parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('''run_xnli''' ,				_UpperCamelCase )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,				datefmt='''%m/%d/%Y %H:%M:%S''' ,				handlers=[logging.StreamHandler(sys.stdout )] ,				)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				snake_case_						:    Dict				=  training_args.get_process_log_level()
				logger.setLevel(_UpperCamelCase )
				datasets.utils.logging.set_verbosity(_UpperCamelCase )
				transformers.utils.logging.set_verbosity(_UpperCamelCase )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
				logger.info(f'''Training/evaluation parameters {training_args}''' )

				# Detecting last checkpoint.
				snake_case_						:    Optional[int]				=  None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								snake_case_						:    List[str]				=  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:
												snake_case_						:    Union[str, Any]				=  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:
												snake_case_						:    Union[str, Any]				=  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 ,				)
								snake_case_						:    Optional[Any]				=  train_dataset.features['''label'''].names

				if training_args.do_eval:
								snake_case_						:    str				=  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 ,				)
								snake_case_						:    Union[str, Any]				=  eval_dataset.features['''label'''].names

				if training_args.do_predict:
								snake_case_						:    Dict				=  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 ,				)
								snake_case_						:    str				=  predict_dataset.features['''label'''].names

				# Labels
				snake_case_						:    Optional[Any]				=  len(_UpperCamelCase )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				snake_case_						:    Optional[int]				=  AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,				num_labels=_UpperCamelCase ,				idalabel={str(_UpperCamelCase ): label for i, label in enumerate(_UpperCamelCase )} ,				labelaid={label: i for i, label in enumerate(_UpperCamelCase )} ,				finetuning_task='''xnli''' ,				cache_dir=model_args.cache_dir ,				revision=model_args.model_revision ,				use_auth_token=True if model_args.use_auth_token else None ,				)
				snake_case_						:    Optional[Any]				=  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 ,				)
				snake_case_						:    int				=  AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path ,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) ,				config=_UpperCamelCase ,				cache_dir=model_args.cache_dir ,				revision=model_args.model_revision ,				use_auth_token=True if model_args.use_auth_token else None ,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,				)

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								snake_case_						:    Dict				=  '''max_length'''
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								snake_case_						:    Any				=  False

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

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												snake_case_						:    int				=  min(len(_UpperCamelCase ) ,				data_args.max_train_samples )
												snake_case_						:    Tuple				=  train_dataset.select(range(_UpperCamelCase ) )
								with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
												snake_case_						:    str				=  train_dataset.map(
												    _UpperCamelCase ,				batched=_UpperCamelCase ,				load_from_cache_file=not data_args.overwrite_cache ,				desc='''Running tokenizer on train dataset''' ,				)
								# Log a few random samples from the training set:
								for index in random.sample(range(len(_UpperCamelCase ) ) ,				3 ):
												logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.''' )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												snake_case_						:    Tuple				=  min(len(_UpperCamelCase ) ,				data_args.max_eval_samples )
												snake_case_						:    Union[str, Any]				=  eval_dataset.select(range(_UpperCamelCase ) )
								with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
												snake_case_						:    Optional[Any]				=  eval_dataset.map(
												    _UpperCamelCase ,				batched=_UpperCamelCase ,				load_from_cache_file=not data_args.overwrite_cache ,				desc='''Running tokenizer on validation dataset''' ,				)

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												snake_case_						:    List[str]				=  min(len(_UpperCamelCase ) ,				data_args.max_predict_samples )
												snake_case_						:    Any				=  predict_dataset.select(range(_UpperCamelCase ) )
								with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
												snake_case_						:    List[Any]				=  predict_dataset.map(
												    _UpperCamelCase ,				batched=_UpperCamelCase ,				load_from_cache_file=not data_args.overwrite_cache ,				desc='''Running tokenizer on prediction dataset''' ,				)

    # Get the metric function
				snake_case_						:    int				=  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 ):
								snake_case_						:    Optional[Any]				=  p.predictions[0] if isinstance(p.predictions ,				_UpperCamelCase ) else p.predictions
								snake_case_						:    Optional[Any]				=  np.argmax(_UpperCamelCase ,				axis=1 )
								return metric.compute(predictions=_UpperCamelCase ,				references=p.label_ids )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								snake_case_						:    Dict				=  default_data_collator
				elif training_args.fpaa:
								snake_case_						:    Optional[Any]				=  DataCollatorWithPadding(_UpperCamelCase ,				pad_to_multiple_of=8 )
				else:
								snake_case_						:    str				=  None

				# Initialize our Trainer
				snake_case_						:    Union[str, Any]				=  Trainer(
				    model=_UpperCamelCase ,				args=_UpperCamelCase ,				train_dataset=train_dataset if training_args.do_train else None ,				eval_dataset=eval_dataset if training_args.do_eval else None ,				compute_metrics=_UpperCamelCase ,				tokenizer=_UpperCamelCase ,				data_collator=_UpperCamelCase ,				)

				# Training
				if training_args.do_train:
								snake_case_						:    Union[str, Any]				=  None
								if training_args.resume_from_checkpoint is not None:
												snake_case_						:    Optional[Any]				=  training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case_						:    List[Any]				=  last_checkpoint
								snake_case_						:    Any				=  trainer.train(resume_from_checkpoint=_UpperCamelCase )
								snake_case_						:    Union[str, Any]				=  train_result.metrics
								snake_case_						:    Dict				=  (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCamelCase )
								)
								snake_case_						:    int				=  min(_UpperCamelCase ,				len(_UpperCamelCase ) )

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

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

				# Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''' )
								snake_case_						:    Optional[Any]				=  trainer.evaluate(eval_dataset=_UpperCamelCase )

								snake_case_						:    List[Any]				=  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCamelCase )
								snake_case_						:    Any				=  min(_UpperCamelCase ,				len(_UpperCamelCase ) )

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

				# Prediction
				if training_args.do_predict:
								logger.info('''*** Predict ***''' )
								snake_case_			,							snake_case_			,							snake_case_						:    List[Any]				=  trainer.predict(_UpperCamelCase ,				metric_key_prefix='''predict''' )

								snake_case_						:    str				=  (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCamelCase )
								)
								snake_case_						:    Optional[int]				=  min(_UpperCamelCase ,				len(_UpperCamelCase ) )

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

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


if __name__ == "__main__":
	main()







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Optional[int]				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Dict				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
