

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  :							List[Any]	   =							logging.getLogger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE__       :







						"""simple docstring"""




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







@dataclass
class 		SCREAMING_SNAKE_CASE__       :







						"""simple docstring"""




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







def 			UpperCAmelCase_ ():
			# 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.

			__UpperCamelCase     :     List[str]										=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
			__UpperCamelCase     :     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()

			__UpperCamelCase     :     int										=   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.
			__UpperCamelCase     :     Union[str, Any]										=   None
			if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
						__UpperCamelCase     :     Dict										=   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:
									__UpperCamelCase     :     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:
									__UpperCamelCase     :     List[str]										=   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  ,      )
						__UpperCamelCase     :     List[Any]										=   train_dataset.features["label"].names

			if training_args.do_eval:
						__UpperCamelCase     :     Tuple										=   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  ,      )
						__UpperCamelCase     :     int										=   eval_dataset.features["label"].names

			if training_args.do_predict:
						__UpperCamelCase     :     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  ,      )
						__UpperCamelCase     :     Optional[Any]										=   predict_dataset.features["label"].names

			# Labels
			__UpperCamelCase     :     List[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.
			__UpperCamelCase     :     List[Any]										=   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  ,      )
			__UpperCamelCase     :     Union[str, 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  ,      )
			__UpperCamelCase     :     Optional[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:
						__UpperCamelCase     :     Dict										=   "max_length"
			else:
						# We will pad later, dynamically at batch creation, to the max sequence length in each batch
						__UpperCamelCase     :     Optional[Any]										=   False

			def preprocess_function(_lowerCAmelCase			:		Optional[int]		):
						# 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:
									__UpperCamelCase     :     int										=   min(len(__UpperCamelCase		)  ,      data_args.max_train_samples		)
									__UpperCamelCase     :     Tuple										=   train_dataset.select(range(__UpperCamelCase		)		)
						with training_args.main_process_first(desc="train dataset map pre-processing"		):
									__UpperCamelCase     :     Optional[int]										=   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:
									__UpperCamelCase     :     Optional[int]										=   min(len(__UpperCamelCase		)  ,      data_args.max_eval_samples		)
									__UpperCamelCase     :     Optional[Any]										=   eval_dataset.select(range(__UpperCamelCase		)		)
						with training_args.main_process_first(desc="validation dataset map pre-processing"		):
									__UpperCamelCase     :     List[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:
									__UpperCamelCase     :     int										=   min(len(__UpperCamelCase		)  ,      data_args.max_predict_samples		)
									__UpperCamelCase     :     List[str]										=   predict_dataset.select(range(__UpperCamelCase		)		)
						with training_args.main_process_first(desc="prediction dataset map pre-processing"		):
									__UpperCamelCase     :     Union[str, 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
			__UpperCamelCase     :     List[Any]										=   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(_lowerCAmelCase			:		List[str]		):
						__UpperCamelCase     :     Dict										=   p.predictions[0] if isinstance(p.predictions  ,      __UpperCamelCase		) else p.predictions
						__UpperCamelCase     :     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:
						__UpperCamelCase     :     List[Any]										=   default_data_collator
			elif training_args.fpaa:
						__UpperCamelCase     :     List[Any]										=   DataCollatorWithPadding(__UpperCamelCase  ,      pad_to_multiple_of=8		)
			else:
						__UpperCamelCase     :     Tuple										=   None

			# Initialize our Trainer
			__UpperCamelCase     :     List[str]										=   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:
						__UpperCamelCase     :     str										=   None
						if training_args.resume_from_checkpoint is not None:
									__UpperCamelCase     :     int										=   training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									__UpperCamelCase     :     Optional[int]										=   last_checkpoint
						__UpperCamelCase     :     Any										=   trainer.train(resume_from_checkpoint=__UpperCamelCase		)
						__UpperCamelCase     :     Tuple										=   train_result.metrics
						__UpperCamelCase     :     Union[str, Any]										=   (
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(__UpperCamelCase		)
						)
						__UpperCamelCase     :     Optional[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 ***"		)
						__UpperCamelCase     :     Optional[int]										=   trainer.evaluate(eval_dataset=__UpperCamelCase		)

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

						trainer.log_metrics("eval"  ,      __UpperCamelCase		)
						trainer.save_metrics("eval"  ,      __UpperCamelCase		)

			# Prediction
			if training_args.do_predict:
						logger.info("*** Predict ***"		)
						__UpperCamelCase     :     int										=   trainer.predict(__UpperCamelCase  ,      metric_key_prefix="predict"		)

						__UpperCamelCase     :     int										=   (
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__UpperCamelCase		)
						)
						__UpperCamelCase     :     Union[str, Any]										=   min(__UpperCamelCase  ,      len(__UpperCamelCase		)		)

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

						__UpperCamelCase     :     Dict										=   np.argmax(__UpperCamelCase  ,      axis=1		)
						__UpperCamelCase     :     str										=   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		):
															__UpperCamelCase     :     int										=   label_list[item]
															writer.write(F'''{index}\t{item}\n'''		)


if __name__ == "__main__":
					main()




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:int =       ["image_processor", "tokenizer"]
							A				:Any =       "LayoutLMv3ImageProcessor"
							A				:str =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



							def __init__(							self ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							str      						= None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead." ,					__UpperCAmelCase ,					)
															a__	:							List[str]      						= kwargs.pop("feature_extractor"				)

											a__	:							Optional[int]      						= image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."				)
											if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."				)

											super().__init__(__UpperCAmelCase ,					__UpperCAmelCase				)



							def __call__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 0 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											if self.image_processor.apply_ocr and (boxes is not None):
															raise ValueError(
															    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."				)

											if self.image_processor.apply_ocr and (word_labels is not None):
															raise ValueError(
															    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."				)

											# first, apply the image processor
											a__	:							List[str]      						= self.image_processor(images=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase				)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				):
																			a__	:							Union[str, Any]      						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
															a__	:							Optional[Any]      						= features["words"]

											a__	:							Union[str, Any]      						= self.tokenizer(
											    text=text if text is not None else features["words"] ,					text_pair=text_pair if text_pair is not None else None ,					boxes=boxes if boxes is not None else features["boxes"] ,					word_labels=__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					max_length=__UpperCAmelCase ,					stride=__UpperCAmelCase ,					pad_to_multiple_of=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase ,					return_attention_mask=__UpperCAmelCase ,					return_overflowing_tokens=__UpperCAmelCase ,					return_special_tokens_mask=__UpperCAmelCase ,					return_offsets_mapping=__UpperCAmelCase ,					return_length=__UpperCAmelCase ,					verbose=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

											# add pixel values
											a__	:							Dict      						= features.pop("pixel_values"				)
											if return_overflowing_tokens is True:
															a__	:							str      						= self.get_overflowing_images(__UpperCAmelCase ,					encoded_inputs["overflow_to_sample_mapping"]				)
											a__	:							Tuple      						= images

											return encoded_inputs



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= []
											for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]				)

											if len(__UpperCAmelCase				) != len(__UpperCAmelCase				):
															raise ValueError(
															    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
															    f' {len(__UpperCAmelCase				)} and {len(__UpperCAmelCase				)}'				)

											return images_with_overflow



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.batch_decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return ["input_ids", "bbox", "attention_mask", "pixel_values"]



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor_class




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor


