



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__    =	logging.get_logger(__name__)

a__    =	{
    """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 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Optional[Any]          =		"""detr"""
   snake_case_					: Any          =		["""past_key_values"""]
   snake_case_					: Tuple          =		{
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }

   def __init__(					self							:			Dict				,		lowerCAmelCase							:			Optional[int]=True				,		lowerCAmelCase							:			List[Any]=None				,		lowerCAmelCase							:			Optional[int]=3				,		lowerCAmelCase							:			int=100				,		lowerCAmelCase							:			Optional[Any]=6				,		lowerCAmelCase							:			List[Any]=2048				,		lowerCAmelCase							:			List[Any]=8				,		lowerCAmelCase							:			List[Any]=6				,		lowerCAmelCase							:			Tuple=2048				,		lowerCAmelCase							:			Union[str, Any]=8				,		lowerCAmelCase							:			Union[str, Any]=0.0				,		lowerCAmelCase							:			str=0.0				,		lowerCAmelCase							:			List[str]=True				,		lowerCAmelCase							:			List[str]="relu"				,		lowerCAmelCase							:			Union[str, Any]=256				,		lowerCAmelCase							:			List[Any]=0.1				,		lowerCAmelCase							:			List[str]=0.0				,		lowerCAmelCase							:			Union[str, Any]=0.0				,		lowerCAmelCase							:			Dict=0.02				,		lowerCAmelCase							:			List[str]=1.0				,		lowerCAmelCase							:			Union[str, Any]=False				,		lowerCAmelCase							:			Any="sine"				,		lowerCAmelCase							:			str="resnet50"				,		lowerCAmelCase							:			Optional[Any]=True				,		lowerCAmelCase							:			List[str]=False				,		lowerCAmelCase							:			Optional[int]=1				,		lowerCAmelCase							:			List[str]=5				,		lowerCAmelCase							:			int=2				,		lowerCAmelCase							:			Optional[Any]=1				,		lowerCAmelCase							:			Optional[int]=1				,		lowerCAmelCase							:			Optional[int]=5				,		lowerCAmelCase							:			Optional[Any]=2				,		lowerCAmelCase							:			Union[str, Any]=0.1				,		**lowerCAmelCase							:			int				,		)				->				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.""")
      _snake_case    :  Tuple					=					CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""])
     elif isinstance(lowerCAmelCase				,		lowerCAmelCase):
      _snake_case    :  Optional[int]					=					backbone_config.get("""model_type""")
      _snake_case    :  List[Any]					=					CONFIG_MAPPING[backbone_model_type]
      _snake_case    :  Dict					=					config_class.from_dict(lowerCAmelCase)
     # set timm attributes to None
     _snake_case    :  Dict					=					None, None, None

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

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

    """simple docstring"""







    return self.encoder_attention_heads

   @property
   def      UpperCamelCase_  (					self							:			Dict)				->				int:

    """simple docstring"""







    return self.d_model

   @classmethod
   def      UpperCamelCase_  (					cls							:			List[Any]				,		lowerCAmelCase							:			PretrainedConfig				,		**lowerCAmelCase							:			Tuple)				->				Dict:

    """simple docstring"""







    return cls(backbone_config=lowerCAmelCase				,		**lowerCAmelCase)


   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Dict[str, any]:

    """simple docstring"""







    _snake_case    :  Tuple					=					copy.deepcopy(self.__dict__)
    if output["backbone_config"] is not None:
     _snake_case    :  List[Any]					=					self.backbone_config.to_dict()
    _snake_case    :  Dict					=					self.__class__.model_type
    return output







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: str          =		version.parse("""1.11"""  )

   @property
   def      UpperCamelCase_  (					self							:			int)				->				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      UpperCamelCase_  (					self							:			Any)				->				float:

    """simple docstring"""







    return 1E-5


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

    """simple docstring"""







    return 12





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Optional[Any]          =		CTRLTokenizer
   snake_case_					: Optional[Any]          =		False
   snake_case_					: List[Any]          =		False

   def      UpperCamelCase_  (					self							:			int)				->				Dict:

    """simple docstring"""







    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    _snake_case    :  Dict					=					["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
    _snake_case    :  Optional[int]					=					dict(zip(lowerCAmelCase				,		range(len(lowerCAmelCase))))
    _snake_case    :  int					=					["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
    _snake_case    :  List[Any]					=					{"""unk_token""": """<unk>"""}

    _snake_case    :  Dict					=					os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["""vocab_file"""])
    _snake_case    :  Any					=					os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["""merges_file"""])
    with open(self.vocab_file				,		"""w"""				,		encoding="""utf-8""") as fp:
     fp.write(json.dumps(lowerCAmelCase) + """\n""")
    with open(self.merges_file				,		"""w"""				,		encoding="""utf-8""") as fp:
     fp.write("""\n""".join(lowerCAmelCase))

   def      UpperCamelCase_  (					self							:			Dict				,		**lowerCAmelCase							:			int)				->				Optional[Any]:

    """simple docstring"""







    kwargs.update(self.special_tokens_map)
    return CTRLTokenizer.from_pretrained(self.tmpdirname				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			Optional[int])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Tuple					=					"""adapt react readapt apt"""
    _snake_case    :  Tuple					=					"""adapt react readapt apt"""
    return input_text, output_text


   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				str:

    """simple docstring"""







    _snake_case    :  str					=					CTRLTokenizer(self.vocab_file				,		self.merges_file				,		**self.special_tokens_map)
    _snake_case    :  Dict					=					"""adapt react readapt apt"""
    _snake_case    :  int					=					"""adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
    _snake_case    :  Tuple					=					tokenizer.tokenize(lowerCAmelCase)
    self.assertListEqual(lowerCAmelCase				,		lowerCAmelCase)

    _snake_case    :  str					=					tokens + [tokenizer.unk_token]

    _snake_case    :  Union[str, Any]					=					[0, 1, 2, 4, 5, 1, 0, 3, 6]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)				,		lowerCAmelCase)
