import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     List[str]       =  {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Tuple			 =						'''detr'''
			__a  : Optional[int]			 =						['''past_key_values''']
			__a  : Optional[Any]			 =						{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}





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

							__lowercase 			=							use_timm_backbone
							__lowercase 			=							backbone_config
							__lowercase 			=							num_channels
							__lowercase 			=							num_queries
							__lowercase 			=							d_model
							__lowercase 			=							encoder_ffn_dim
							__lowercase 			=							encoder_layers
							__lowercase 			=							encoder_attention_heads
							__lowercase 			=							decoder_ffn_dim
							__lowercase 			=							decoder_layers
							__lowercase 			=							decoder_attention_heads
							__lowercase 			=							dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							activation_function
							__lowercase 			=							init_std
							__lowercase 			=							init_xavier_std
							__lowercase 			=							encoder_layerdrop
							__lowercase 			=							decoder_layerdrop
							__lowercase 			=							encoder_layers
							__lowercase 			=							auxiliary_loss
							__lowercase 			=							position_embedding_type
							__lowercase 			=							backbone
							__lowercase 			=							use_pretrained_backbone
							__lowercase 			=							dilation
							# Hungarian matcher
							__lowercase 			=							class_cost
							__lowercase 			=							bbox_cost
							__lowercase 			=							giou_cost
							# Loss coefficients
							__lowercase 			=							mask_loss_coefficient
							__lowercase 			=							dice_loss_coefficient
							__lowercase 			=							bbox_loss_coefficient
							__lowercase 			=							giou_loss_coefficient
							__lowercase 			=							eos_coefficient
							super().__init__(is_encoder_decoder=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return self.d_model





			@classmethod
			def        _SCREAMING_SNAKE_CASE			(   cls      ,   lowerCAmelCase__      ,   **lowerCAmelCase__						)     ->					Optional[int]:

							'''simple docstring'''


							return cls(backbone_config=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict[str, any]:

							'''simple docstring'''


							__lowercase 			=							copy.deepcopy(self.__dict__						)
							if output["backbone_config"] is not None:
											__lowercase 			=							self.backbone_config.to_dict()
							__lowercase 			=							self.__class__.model_type
							return output




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Union[str, Any]			 =						version.parse('''1.11''' )





			@property
			def        _SCREAMING_SNAKE_CASE			(   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        _SCREAMING_SNAKE_CASE			(   self						)     ->					float:

							'''simple docstring'''


							return 1E-5





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return 12
from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__a  :     Optional[Any]       =  logging.get_logger(__name__)


__a  :     List[str]       =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = "first_exhausted"							,			):







				"""simple docstring"""



				from .arrow_dataset import Dataset
				from .iterable_dataset import IterableDataset

				if not datasets:
								raise ValueError('''Unable to interleave an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
								raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
				if dataset_type is Dataset:
								return _interleave_map_style_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)
				else:
								return _interleave_iterable_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = 0							,			):







				"""simple docstring"""



				if not dsets:
								raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if dataset_type is Dataset:
								return _concatenate_map_style_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)
				else:
								return _concatenate_iterable_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)