




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_	:				Union[str, Any]  				= logging.get_logger(__name__)

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






class     __lowerCAmelCase (       _lowerCamelCase ):
			snake_case_      : Optional[int]      =     "conditional_detr"
			snake_case_      : Optional[Any]      =     ["past_key_values"]
			snake_case_      : Dict      =     {
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}







			def __init__(       self    :			Any    ,    snake_case__    :			List[Any]=True    ,    snake_case__    :			List[Any]=None    ,    snake_case__    :			Tuple=3    ,    snake_case__    :			int=300    ,    snake_case__    :			Union[str, Any]=6    ,    snake_case__    :			Optional[int]=2_048    ,    snake_case__    :			List[Any]=8    ,    snake_case__    :			Union[str, Any]=6    ,    snake_case__    :			str=2_048    ,    snake_case__    :			Optional[Any]=8    ,    snake_case__    :			Tuple=0.0    ,    snake_case__    :			int=0.0    ,    snake_case__    :			int=True    ,    snake_case__    :			Union[str, Any]="relu"    ,    snake_case__    :			str=256    ,    snake_case__    :			Optional[int]=0.1    ,    snake_case__    :			Tuple=0.0    ,    snake_case__    :			List[Any]=0.0    ,    snake_case__    :			int=0.02    ,    snake_case__    :			Any=1.0    ,    snake_case__    :			Dict=False    ,    snake_case__    :			Optional[Any]="sine"    ,    snake_case__    :			List[str]="resnet50"    ,    snake_case__    :			Tuple=True    ,    snake_case__    :			Tuple=False    ,    snake_case__    :			List[str]=2    ,    snake_case__    :			Optional[Any]=5    ,    snake_case__    :			Union[str, Any]=2    ,    snake_case__    :			Optional[int]=1    ,    snake_case__    :			List[str]=1    ,    snake_case__    :			int=2    ,    snake_case__    :			Union[str, Any]=5    ,    snake_case__    :			Optional[int]=2    ,    snake_case__    :			Optional[int]=0.25    ,    **snake_case__    :			Tuple    ,    ):




									"""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													=     CONFIG_MAPPING["resnet"](out_features=["stage4"]      )
															elif isinstance(_UpperCamelCase    ,    _UpperCamelCase      ):
																					_UpperCAmelCase													=     backbone_config.get("model_type"      )
																					_UpperCAmelCase													=     CONFIG_MAPPING[backbone_model_type]
																					_UpperCAmelCase													=     config_class.from_dict(_UpperCamelCase      )

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







			@property
			def UpperCamelCase			(       self    :			Optional[int]      ):




									"""simple docstring"""


									return self.encoder_attention_heads







			@property
			def UpperCamelCase			(       self    :			int      ):




									"""simple docstring"""


									return self.d_model







			def UpperCamelCase			(       self    :			List[Any]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     copy.deepcopy(self.__dict__      )
									if self.backbone_config is not None:
															_UpperCAmelCase													=     self.backbone_config.to_dict()
									_UpperCAmelCase													=     self.__class__.model_type
									return output






class     __lowerCAmelCase (       _lowerCamelCase ):
			snake_case_      : Optional[Any]      =     version.parse("1.11" )







			@property
			def UpperCamelCase			(       self    :			int      ):




									"""simple docstring"""


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







			@property
			def UpperCamelCase			(       self    :			Optional[Any]      ):




									"""simple docstring"""


									return 1e-5







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




									"""simple docstring"""


									return 12











import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(lowerCamelCase					,				'''wb''' ) as f:
            f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()





