

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 			=		logging.getLogger(__name__)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      return (preds == labels).mean()







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







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







def 		_lowercase (     ):
      # 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.

      __lowerCAmelCase					:						Union[str, Any]					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
      __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Union[str, Any]					=					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'''   ,	lowercase__						)

      # Set seed
      set_seed(training_args.seed						)

      try:
            __lowerCAmelCase					:						int					=					processors[data_args.task_name]()
            __lowerCAmelCase					:						int					=					processor.get_labels()
            __lowerCAmelCase					:						Tuple					=					len(lowercase__						)
      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.

      __lowerCAmelCase					:						Optional[Any]					=					AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=lowercase__   ,	finetuning_task=data_args.task_name   ,	cache_dir=model_args.cache_dir   ,	)
      __lowerCAmelCase					:						List[str]					=					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   ,	)
      __lowerCAmelCase					:						Optional[Any]					=					AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path   ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)   ,	config=lowercase__   ,	cache_dir=model_args.cache_dir   ,	)

      # Get datasets
      __lowerCAmelCase					:						Dict					=					(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,	tokenizer=lowercase__   ,	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
      )
      __lowerCAmelCase					:						List[str]					=					(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,	tokenizer=lowercase__   ,	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(lowercase__						) -> Dict:
            __lowerCAmelCase					:						int					=					np.argmax(p.predictions   ,	axis=1						)
            return {"acc": simple_accuracy(lowercase__   ,	p.label_ids						)}

      # Data collator
      __lowerCAmelCase					:						Any					=					DataCollatorWithPadding(lowercase__   ,	pad_to_multiple_of=8						) if training_args.fpaa else None

      # Initialize our Trainer
      __lowerCAmelCase					:						Dict					=					Trainer(
          model=lowercase__   ,	args=lowercase__   ,	train_dataset=lowercase__   ,	eval_dataset=lowercase__   ,	compute_metrics=lowercase__   ,	data_collator=lowercase__   ,	)

      # 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
      __lowerCAmelCase					:						Tuple					=					{}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''						)

            __lowerCAmelCase					:						Any					=					trainer.evaluate()

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

                        results.update(lowercase__						)

      return results







def 		_lowercase (     lowercase__						):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
 main()





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_="relu"      ,							A_=256      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=1.0      ,							A_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->Any:



       '''simple docstring'''





       if backbone_config is not None and use_timm_backbone:
             raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''					)

       if not use_timm_backbone:
             if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''					)
                   __lowerCAmelCase					:						Optional[Any]					=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
             elif isinstance(A_      ,							A_					):
                   __lowerCAmelCase					:						int					=					backbone_config.get('''model_type'''					)
                   __lowerCAmelCase					:						List[str]					=					CONFIG_MAPPING[backbone_model_type]
                   __lowerCAmelCase					:						Any					=					config_class.from_dict(A_					)
             # set timm attributes to None
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					None, None, None

       __lowerCAmelCase					:						Tuple					=					use_timm_backbone
       __lowerCAmelCase					:						Optional[Any]					=					backbone_config
       __lowerCAmelCase					:						List[str]					=					num_channels
       __lowerCAmelCase					:						Tuple					=					num_queries
       __lowerCAmelCase					:						int					=					d_model
       __lowerCAmelCase					:						List[Any]					=					encoder_ffn_dim
       __lowerCAmelCase					:						Optional[int]					=					encoder_layers
       __lowerCAmelCase					:						List[str]					=					encoder_attention_heads
       __lowerCAmelCase					:						str					=					decoder_ffn_dim
       __lowerCAmelCase					:						Union[str, Any]					=					decoder_layers
       __lowerCAmelCase					:						Any					=					decoder_attention_heads
       __lowerCAmelCase					:						Optional[int]					=					dropout
       __lowerCAmelCase					:						Any					=					attention_dropout
       __lowerCAmelCase					:						Tuple					=					activation_dropout
       __lowerCAmelCase					:						Optional[Any]					=					activation_function
       __lowerCAmelCase					:						List[str]					=					init_std
       __lowerCAmelCase					:						Tuple					=					init_xavier_std
       __lowerCAmelCase					:						Any					=					encoder_layerdrop
       __lowerCAmelCase					:						List[Any]					=					decoder_layerdrop
       __lowerCAmelCase					:						Optional[Any]					=					encoder_layers
       __lowerCAmelCase					:						Optional[Any]					=					auxiliary_loss
       __lowerCAmelCase					:						Optional[Any]					=					position_embedding_type
       __lowerCAmelCase					:						Tuple					=					backbone
       __lowerCAmelCase					:						Any					=					use_pretrained_backbone
       __lowerCAmelCase					:						int					=					dilation
       # Hungarian matcher
       __lowerCAmelCase					:						Dict					=					class_cost
       __lowerCAmelCase					:						List[str]					=					bbox_cost
       __lowerCAmelCase					:						int					=					giou_cost
       # Loss coefficients
       __lowerCAmelCase					:						Optional[Any]					=					mask_loss_coefficient
       __lowerCAmelCase					:						Tuple					=					dice_loss_coefficient
       __lowerCAmelCase					:						int					=					bbox_loss_coefficient
       __lowerCAmelCase					:						List[Any]					=					giou_loss_coefficient
       __lowerCAmelCase					:						int					=					eos_coefficient
       super().__init__(is_encoder_decoder=A_      ,							**A_					)
 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)
 @property
 def 	UpperCamelCase__  (							self					)					->float:



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


