






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCamelCase	:							str				=namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Union[str, Any]:
     return (abs(source - target			) / target) < 0.0_1







@pytest.mark.integration
def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[Any]:
     UpperCamelCase__			:   Dict       				=  _TestCommandArgs(dataset=__lowerCAmelCase					,    all_configs=__lowerCAmelCase					,    save_infos=__lowerCAmelCase			)
     UpperCamelCase__			:   Optional[Any]       				=  TestCommand(*__lowerCAmelCase			)
     test_command.run()
     UpperCamelCase__			:   List[Any]       				=  os.path.join(__lowerCAmelCase					,    "README.md"			)
     assert os.path.exists(__lowerCAmelCase			)
     UpperCamelCase__			:   Dict       				=  DatasetInfosDict.from_directory(__lowerCAmelCase			)
     UpperCamelCase__			:   Optional[int]       				=  DatasetInfosDict(
         {
             "default": DatasetInfo(
                 features=Features(
                     {
                         "tokens": Sequence(Value("string"			)			),
                         "ner_tags": Sequence(
                             ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]			)			),
                         "langs": Sequence(Value("string"			)			),
                         "spans": Sequence(Value("string"			)			),
                     }			)					,    splits=[
                     {
                         "name": "train",
                         "num_bytes": 235_1563,
                         "num_examples": 1_0000,
                     },
                     {
                         "name": "validation",
                         "num_bytes": 23_8418,
                         "num_examples": 1000,
                     },
                 ]					,    download_size=394_0680					,    dataset_size=258_9981					,    )
         }			)
     assert dataset_infos.keys() == expected_dataset_infos.keys()
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          UpperCamelCase__				,     UpperCamelCase__			:   Optional[int]       				=  getattr(dataset_infos["default"]					,    __lowerCAmelCase			), getattr(expected_dataset_infos["default"]					,    __lowerCAmelCase			)
          if key == "num_bytes":
               assert is_apercent_close(__lowerCAmelCase					,    __lowerCAmelCase			)
          elif key == "splits":
               assert list(__lowerCAmelCase			) == list(__lowerCAmelCase			)
               for split in result:
                    assert result[split].name == expected[split].name
                    assert result[split].num_examples == expected[split].num_examples
                    assert is_apercent_close(result[split].num_bytes					,    expected[split].num_bytes			)
          else:
               result == expected







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	:							Tuple				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = '''conditional_detr'''
 _lowerCAmelCase       :       List[Any]        = ['''past_key_values''']
 _lowerCAmelCase       :       List[str]        = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=3							,   SCREAMING_SNAKE_CASE					:   Any=3_00							,   SCREAMING_SNAKE_CASE					:   Tuple=6							,   SCREAMING_SNAKE_CASE					:   int=20_48							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=8							,   SCREAMING_SNAKE_CASE					:   int=6							,   SCREAMING_SNAKE_CASE					:   Dict=20_48							,   SCREAMING_SNAKE_CASE					:   Optional[int]=8							,   SCREAMING_SNAKE_CASE					:   Dict=0.0							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   int="relu"							,   SCREAMING_SNAKE_CASE					:   Any=2_56							,   SCREAMING_SNAKE_CASE					:   int=0.1							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.0							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Optional[int]=1.0							,   SCREAMING_SNAKE_CASE					:   List[str]=False							,   SCREAMING_SNAKE_CASE					:   int="sine"							,   SCREAMING_SNAKE_CASE					:   str="resnet50"							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   Tuple=False							,   SCREAMING_SNAKE_CASE					:   Tuple=2							,   SCREAMING_SNAKE_CASE					:   Tuple=5							,   SCREAMING_SNAKE_CASE					:   List[str]=2							,   SCREAMING_SNAKE_CASE					:   List[str]=1							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=1							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   Any=5							,   SCREAMING_SNAKE_CASE					:   int=2							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.2_5							,   **SCREAMING_SNAKE_CASE					:   Union[str, 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."						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Union[str, Any]       				=  backbone_config.get("model_type"						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase__			:   Any       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

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







 @property
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return self.encoder_attention_heads







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.d_model







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






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







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = version.parse('''1.11'''       )







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return 1e-5







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return 12