

'''simple docstring'''



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase					:			str       =	logging.getLogger(__name__)







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     return (preds == labels).mean()







@dataclass
class 		lowerCAmelCase__  :



 """simple docstring"""



 lowerCAmelCase__       = field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
 lowerCAmelCase__       = field(
     default=a					,   metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
 lowerCAmelCase__       = field(
     default=a					,   metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
 lowerCAmelCase__       = field(
     default=a					,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,   )







@dataclass
class 		lowerCAmelCase__  :



 """simple docstring"""



 lowerCAmelCase__       = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()   )}   )
 lowerCAmelCase__       = field(metadata={"help": "Should contain the data files for the task."}   )
 lowerCAmelCase__       = 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__       = field(
     default=a					,   metadata={"help": "Overwrite the cached training and evaluation sets"}   )







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir		)
         and os.listdir(training_args.output_dir		)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
              """ --overwrite_output_dir to overcome."""		)

     # Setup logging
     logging.basicConfig(
         format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,       datefmt="""%m/%d/%Y %H:%M:%S""" ,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,       )
     logger.warning(
         """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" ,       training_args.local_rank ,       training_args.device ,       training_args.n_gpu ,       bool(training_args.local_rank != -1		) ,       training_args.fpaa ,       )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank		):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info("""Training/evaluation parameters %s""" ,       a__		)

     # Set seed
     set_seed(training_args.seed		)

     try:
          __SCREAMING_SNAKE_CASE      = processors[data_args.task_name]()
          __SCREAMING_SNAKE_CASE      = processor.get_labels()
          __SCREAMING_SNAKE_CASE      = len(a__		)
     except KeyError:
          raise ValueError("""Task not found: %s""" % (data_args.task_name)		)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     __SCREAMING_SNAKE_CASE      = AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,       num_labels=a__ ,       finetuning_task=data_args.task_name ,       cache_dir=model_args.cache_dir ,       )
     __SCREAMING_SNAKE_CASE      = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,       cache_dir=model_args.cache_dir ,       )
     __SCREAMING_SNAKE_CASE      = AutoModelForMultipleChoice.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 ,       )

     # Get datasets
     __SCREAMING_SNAKE_CASE      = (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir ,       tokenizer=a__ ,       task=data_args.task_name ,       max_seq_length=data_args.max_seq_length ,       overwrite_cache=data_args.overwrite_cache ,       mode=Split.train ,       )
         if training_args.do_train
         else None
     )
     __SCREAMING_SNAKE_CASE      = (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir ,       tokenizer=a__ ,       task=data_args.task_name ,       max_seq_length=data_args.max_seq_length ,       overwrite_cache=data_args.overwrite_cache ,       mode=Split.dev ,       )
         if training_args.do_eval
         else None
     )

     def compute_metrics(a__		) -> Dict:
          __SCREAMING_SNAKE_CASE      = np.argmax(p.predictions ,       axis=1		)
          return {"acc": simple_accuracy(a__ ,       p.label_ids		)}

     # Data collator
     __SCREAMING_SNAKE_CASE      = DataCollatorWithPadding(a__ ,       pad_to_multiple_of=8		) if training_args.fpaa else None

     # Initialize our Trainer
     __SCREAMING_SNAKE_CASE      = Trainer(
         model=a__ ,       args=a__ ,       train_dataset=a__ ,       eval_dataset=a__ ,       compute_metrics=a__ ,       data_collator=a__ ,       )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
     __SCREAMING_SNAKE_CASE      = {}
     if training_args.do_eval:
          logger.info("""*** Evaluate ***"""		)

          __SCREAMING_SNAKE_CASE      = trainer.evaluate()

          __SCREAMING_SNAKE_CASE      = os.path.join(training_args.output_dir ,       """eval_results.txt"""		)
          if trainer.is_world_master():
               with open(a__ ,       """w"""		) as writer:
                    logger.info("""***** Eval results *****"""		)
                    for key, value in result.items():
                         logger.info("""  %s = %s""" ,       a__ ,       a__		)
                         writer.write("""%s = %s\n""" % (key, value)		)

                    results.update(a__		)

     return results







def   a__			(    a__		):




     """simple docstring"""


     main()


if __name__ == "__main__":
  main()









'''simple docstring'''



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 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "LayoutLMv2ImageProcessor"
 lowerCAmelCase__       = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					List[str]:


      """simple docstring"""




      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Union[List[List[int]], List[List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[List[int], List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, PaddingStrategy] = False   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, TruncationStrategy] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					BatchEncoding:


      """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."""     )

      if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""     )

      # first, apply the image processor
      __SCREAMING_SNAKE_CASE      = self.image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = 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=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				padding=__SCREAMING_SNAKE_CASE   ,				truncation=__SCREAMING_SNAKE_CASE   ,				max_length=__SCREAMING_SNAKE_CASE   ,				stride=__SCREAMING_SNAKE_CASE   ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE   ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				return_overflowing_tokens=__SCREAMING_SNAKE_CASE   ,				return_special_tokens_mask=__SCREAMING_SNAKE_CASE   ,				return_offsets_mapping=__SCREAMING_SNAKE_CASE   ,				return_length=__SCREAMING_SNAKE_CASE   ,				verbose=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)

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

      return encoded_inputs
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




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

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

      return images_with_overflow
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      return ["input_ids", "bbox", "attention_mask", "image"]
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




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


 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Any:


      """simple docstring"""




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






