





import copy
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


_lowerCamelCase             =      logging.get_logger(__name__)

_lowerCamelCase             =      {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      'conditional_detr'
      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__=300      ,							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__=2      ,							a__=5      ,							a__=2      ,							a__=1      ,							a__=1      ,							a__=2      ,							a__=5      ,							a__=2      ,							a__=0.25      ,							**a__      ,							):



             """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[int]	=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
                    elif isinstance(UpperCAmelCase__      ,							UpperCAmelCase__):
                           _lowerCamelCase							:     Tuple	=		backbone_config.get('''model_type''')
                           _lowerCamelCase							:     str	=		CONFIG_MAPPING[backbone_model_type]
                           _lowerCamelCase							:     str	=		config_class.from_dict(UpperCAmelCase__)

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


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return self.encoder_attention_heads


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return self.d_model



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		copy.deepcopy(self.__dict__)
             if self.backbone_config is not None:
                    _lowerCamelCase							:     Union[str, Any]	=		self.backbone_config.to_dict()
             _lowerCamelCase							:     List[str]	=		self.__class__.model_type
             return output







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      version.parse("""1.11"""  )


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return OrderedDict(
                 [
                     ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                     ('''pixel_mask''', {0: '''batch'''}),
                 ])


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return 1e-5



      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             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


_lowerCamelCase			       =      logging.get_logger(__name__)







@dataclass
class 			__A       :




      """simple docstring"""







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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.task_name.lower()







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      """train"""
      UpperCAmelCase__						=      """dev"""
      UpperCAmelCase__						=      """test"""







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42


      def __init__(		self      ,							a__      ,							a__      ,							a__ = None      ,							a__ = Split.train      ,							a__ = None      ,							):



             """simple docstring"""





             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__      ,							)
             _lowerCamelCase							:     Optional[Any]	=		args
             _lowerCamelCase							:     Tuple	=		glue_processors[args.task_name]()
             _lowerCamelCase							:     Any	=		glue_output_modes[args.task_name]
             if isinstance(a__      ,							a__):
                    try:
                           _lowerCamelCase							:     List[Any]	=		Split[mode]
                    except KeyError:
                           raise KeyError('''mode is not a valid split name''')
        # Load data features from cache or dataset file
             _lowerCamelCase							:     Tuple	=		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}"""      ,							)
             _lowerCamelCase							:     int	=		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)
                    _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		label_list[2], label_list[1]
             _lowerCamelCase							:     str	=		label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             _lowerCamelCase							:     Any	=		cached_features_file + '''.lock'''
             with FileLock(a__):
                    if os.path.exists(a__) and not args.overwrite_cache:
                           _lowerCamelCase							:     Any	=		time.time()
                           _lowerCamelCase							:     Optional[Any]	=		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:
                                  _lowerCamelCase							:     List[str]	=		self.processor.get_dev_examples(args.data_dir)
                           elif mode == Split.test:
                                  _lowerCamelCase							:     str	=		self.processor.get_test_examples(args.data_dir)
                           else:
                                  _lowerCamelCase							:     List[Any]	=		self.processor.get_train_examples(args.data_dir)
                           if limit_length is not None:
                                  _lowerCamelCase							:     List[Any]	=		examples[:limit_length]
                           _lowerCamelCase							:     List[str]	=		glue_convert_examples_to_features(
                               a__      ,							a__      ,							max_length=args.max_seq_length      ,							label_list=a__      ,							output_mode=self.output_mode      ,							)
                           _lowerCamelCase							:     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):



             """simple docstring"""





             return len(self.features)


      def __getitem__(		self      ,							a__):



             """simple docstring"""





             return self.features[i]



      def 				__snake_case			(		self):



             """simple docstring"""





             return self.label_list

