






"""simple docstring"""



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


__A		:       str  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		{
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				int							    =   "table-transformer"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   ["past_key_values"]
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   {
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}




					def __init__(				self     :						Tuple    ,						A     :						Dict=True    ,						A     :						List[str]=None    ,						A     :						str=3    ,						A     :						int=1_00    ,						A     :						Optional[int]=6    ,						A     :						Union[str, Any]=20_48    ,						A     :						Union[str, Any]=8    ,						A     :						Optional[Any]=6    ,						A     :						Tuple=20_48    ,						A     :						List[Any]=8    ,						A     :						str=0.0    ,						A     :						Optional[int]=0.0    ,						A     :						str=True    ,						A     :						Optional[int]="relu"    ,						A     :						List[Any]=2_56    ,						A     :						Union[str, Any]=0.1    ,						A     :						Tuple=0.0    ,						A     :						int=0.0    ,						A     :						Any=0.02    ,						A     :						Optional[Any]=1.0    ,						A     :						List[Any]=False    ,						A     :						List[Any]="sine"    ,						A     :						Tuple="resnet50"    ,						A     :						Optional[Any]=True    ,						A     :						int=False    ,						A     :						List[Any]=1    ,						A     :						Optional[Any]=5    ,						A     :						Union[str, Any]=2    ,						A     :						Optional[int]=1    ,						A     :						Tuple=1    ,						A     :						List[str]=5    ,						A     :						Optional[Any]=2    ,						A     :						str=0.1    ,						**A     :						int    ,						)  ->  Dict:
							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.'''   )
											lowercase_ : Dict							       =   CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
									elif isinstance(A    ,						A   ):
											lowercase_ : Union[str, Any]							       =   backbone_config.get('''model_type'''   )
											lowercase_ : Any							       =   CONFIG_MAPPING[backbone_model_type]
											lowercase_ : List[str]							       =   config_class.from_dict(A   )
									# set timm attributes to None
									lowercase_       ,					lowercase_       ,					lowercase_ : Dict							       =   None, None, None

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




					@property
					def   A  (				self     :						Any   )  ->  int:
							return self.encoder_attention_heads






					@property
					def   A  (				self     :						List[Any]   )  ->  int:
							return self.d_model





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   version.parse("1.11"							)




					@property
					def   A  (				self     :						Dict   )  ->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							        ('''pixel_mask''', {0: '''batch'''}),
							    ]   )




					@property
					def   A  (				self     :						List[str]   )  ->  float:
							return 1e-5






					@property
					def   A  (				self     :						int   )  ->  int:
							return 12






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a__				=		logging.get_logger(__name__)

@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
							UpperCAmelCase__       :						str	    =    field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
							UpperCAmelCase__       :						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."
							        )
							    }				,       )
							UpperCAmelCase__       :						bool	    =    field(
							    default=__lowercase				,       metadata={"help": "Overwrite the cached training and evaluation sets"}   )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    List[Any]         =		self.task_name.lower()



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    "train"
							UpperCAmelCase__       :						List[str]	    =    "dev"
							UpperCAmelCase__       :						Union[str, Any]	    =    "test"



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						GlueDataTrainingArguments
							UpperCAmelCase__       :						str
							UpperCAmelCase__       :						List[InputFeatures]


							def __init__(			self       ,							_a       ,							_a       ,							_a = None       ,							_a = Split.train       ,							_a = None       ,							)    ->    str:
												warnings.warn(
												    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
												    '''library. You can have a look at this example script for pointers: '''
												    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''       ,							_a       ,							)
												_a	:    List[str]         =		args
												_a	:    List[str]         =		glue_processors[args.task_name]()
												_a	:    List[Any]         =		glue_output_modes[args.task_name]
												if isinstance(_a       ,							_a       ):
																	try:
																						_a	:    Any         =		Split[mode]
																	except KeyError:
																						raise KeyError('''mode is not a valid split name'''       )
        # Load data features from cache or dataset file
												_a	:    Union[str, Any]         =		os.path.join(
												    cache_dir if cache_dir is not None else args.data_dir       ,							F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""       ,							)
												_a	:    str         =		self.processor.get_labels()
												if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
												    "RobertaTokenizer",
												    "RobertaTokenizerFast",
												    "XLMRobertaTokenizer",
												    "BartTokenizer",
												    "BartTokenizerFast",
												):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	_a      ,					_a	:    Optional[int]         =		label_list[2], label_list[1]
												_a	:    Dict         =		label_list

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_a	:    Optional[int]         =		cached_features_file + '''.lock'''
												with FileLock(_a       ):
																	if os.path.exists(_a       ) and not args.overwrite_cache:
																						_a	:    List[Any]         =		time.time()
																						_a	:    str         =		torch.load(_a       )
																						logger.info(
																						    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""       ,							time.time() - start       )
																	else:
																						logger.info(F"""Creating features from dataset file at {args.data_dir}"""       )

																						if mode == Split.dev:
																											_a	:    str         =		self.processor.get_dev_examples(args.data_dir       )
																						elif mode == Split.test:
																											_a	:    Any         =		self.processor.get_test_examples(args.data_dir       )
																						else:
																											_a	:    List[str]         =		self.processor.get_train_examples(args.data_dir       )
																						if limit_length is not None:
																											_a	:    Dict         =		examples[:limit_length]
																						_a	:    Union[str, Any]         =		glue_convert_examples_to_features(
																						    _a       ,							_a       ,							max_length=args.max_seq_length       ,							label_list=_a       ,							output_mode=self.output_mode       ,							)
																						_a	:    Optional[int]         =		time.time()
																						torch.save(self.features       ,							_a       )
																						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																						logger.info(
																						    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""       )


							def __len__(			self       )    ->    Optional[int]:
												return len(self.features       )


							def __getitem__(			self       ,							_a       )    ->    InputFeatures:
												return self.features[i]




							def 				__lowercase				(			self       )    ->    Tuple:
												return self.label_list



