



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


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

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



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'conditional_detr'
   _UpperCAmelCase     	=			['past_key_values']
   _UpperCAmelCase     	=			{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Tuple=True							,   __lowerCAmelCase			:       List[Any]=None							,   __lowerCAmelCase			:       Tuple=3							,   __lowerCAmelCase			:       Optional[Any]=300							,   __lowerCAmelCase			:       Dict=6							,   __lowerCAmelCase			:       str=2048							,   __lowerCAmelCase			:       Optional[Any]=8							,   __lowerCAmelCase			:       Any=6							,   __lowerCAmelCase			:       Optional[Any]=2048							,   __lowerCAmelCase			:       Tuple=8							,   __lowerCAmelCase			:       int=0.0							,   __lowerCAmelCase			:       List[Any]=0.0							,   __lowerCAmelCase			:       Optional[Any]=True							,   __lowerCAmelCase			:       Optional[int]="relu"							,   __lowerCAmelCase			:       List[str]=256							,   __lowerCAmelCase			:       int=0.1							,   __lowerCAmelCase			:       List[Any]=0.0							,   __lowerCAmelCase			:       str=0.0							,   __lowerCAmelCase			:       Optional[int]=0.0_2							,   __lowerCAmelCase			:       Dict=1.0							,   __lowerCAmelCase			:       Union[str, Any]=False							,   __lowerCAmelCase			:       List[Any]="sine"							,   __lowerCAmelCase			:       Any="resnet50"							,   __lowerCAmelCase			:       Dict=True							,   __lowerCAmelCase			:       List[Any]=False							,   __lowerCAmelCase			:       Optional[Any]=2							,   __lowerCAmelCase			:       int=5							,   __lowerCAmelCase			:       Tuple=2							,   __lowerCAmelCase			:       Optional[int]=1							,   __lowerCAmelCase			:       int=1							,   __lowerCAmelCase			:       Dict=2							,   __lowerCAmelCase			:       List[Any]=5							,   __lowerCAmelCase			:       Optional[Any]=2							,   __lowerCAmelCase			:       List[str]=0.2_5							,   **__lowerCAmelCase			:       Union[str, Any]							,   )       ->				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."							)
      a						=		CONFIG_MAPPING["resnet"](out_features=["stage4"]							)
     elif isinstance(__lowerCAmelCase							,   __lowerCAmelCase							):
      a						=		backbone_config.get("model_type"							)
      a						=		CONFIG_MAPPING[backbone_model_type]
      a						=		config_class.from_dict(__lowerCAmelCase							)

    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=__lowerCAmelCase							,   **__lowerCAmelCase							)

   @property
   def 		A							(      self			:       Optional[int]							)       ->				int:


    """simple docstring"""






    return self.encoder_attention_heads

   @property
   def 		A							(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    return self.d_model




   def 		A							(      self			:       Tuple							)       ->				Optional[Any]:


    """simple docstring"""






    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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			version.parse('''1.11'''	)

   @property
   def 		A							(      self			:       List[str]							)       ->				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 		A							(      self			:       Any							)       ->				float:


    """simple docstring"""






    return 1E-5




   @property
   def 		A							(      self			:       List[Any]							)       ->				int:


    """simple docstring"""






    return 12




import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(UpperCAmelCase__	),   '''Tatoeba directory does not exist.'''	)
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    return TatoebaConverter(save_dir=__lowerCAmelCase							)

   @slow
   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    self.resolver.convert_models(["heb-eng"]							)




   @slow
   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.resolver.write_model_card("opus-mt-he-en"							,   dry_run=__lowerCAmelCase							)
    assert mmeta["long_pair"] == "heb-eng"

