
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


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

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






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'conditional_detr'
				lowerCAmelCase 		= ['past_key_values']
				lowerCAmelCase 		= {
				    '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__				,   )	->				int:
					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."""   )
							A								=				CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]   )
						elif isinstance(a__				,   a__   ):
							A								=				backbone_config.get("""model_type"""   )
							A								=				CONFIG_MAPPING[backbone_model_type]
							A								=				config_class.from_dict(a__   )

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

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

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return self.d_model

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				copy.deepcopy(self.__dict__   )
					if self.backbone_config is not None:
						A								=				self.backbone_config.to_dict()
					A								=				self.__class__.model_type
					return output






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= version.parse('1.11'				)

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

				@property
				def      _UpperCAmelCase					(						self   )	->				float:
					return 1e-5

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







import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Tuple:





	"""simple docstring"""







	def wrapper(*UpperCamelCase__:     Union[str, Any]							,					**UpperCamelCase__:     List[str]						):
		A								=				timeit.default_timer()
		A								=				func(*UpperCamelCase__							,					**UpperCamelCase__						)
		A								=				timeit.default_timer() - starttime
		return delta

	A								=				func.__name__

	return wrapper





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     int=None						) ->   Any:





	"""simple docstring"""







	A								=				[]
	A								=				seq_shapes or {}
	for i in range(UpperCamelCase__						):
		A								=				{}
		for col_id, (k, v) in enumerate(features.items()						):
			if isinstance(UpperCamelCase__							,					_ArrayXD						):
				A								=				np.random.rand(*v.shape						).astype(v.dtype						)
			elif isinstance(UpperCamelCase__							,					datasets.Value						):
				if v.dtype == "string":
					A								=				"""The small grey turtle was surprisingly fast when challenged."""
				else:
					A								=				np.random.randint(10							,					size=1						).astype(v.dtype						).item()
			elif isinstance(UpperCamelCase__							,					datasets.Sequence						):
				while isinstance(UpperCamelCase__							,					datasets.Sequence						):
					A								=				v.feature
				A								=				seq_shapes[k]
				A								=				np.random.rand(*UpperCamelCase__						).astype(v.dtype						)
			A								=				data

		dummy_data.append((i, example)						)

	return dummy_data





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     str=None						) ->   Optional[int]:





	"""simple docstring"""







	A								=				generate_examples(UpperCamelCase__							,					num_examples=UpperCamelCase__							,					seq_shapes=UpperCamelCase__						)

	with ArrowWriter(features=UpperCamelCase__							,					path=UpperCamelCase__						) as writer:
		for key, record in dummy_data:
			A								=				features.encode_example(UpperCamelCase__						)
			writer.write(UpperCamelCase__						)

		A					,			A								=				writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'						)

	A								=				datasets.Dataset.from_file(filename=UpperCamelCase__							,					info=datasets.DatasetInfo(features=UpperCamelCase__						)						)

	return dataset



