



'''simple docstring'''
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


snake_case_			:     str  	=  logging.get_logger(__name__)

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






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """conditional_detr"""
	lowercase__      =      ["""past_key_values"""]
	lowercase__      =      {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





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

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





	@property
	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							return self.encoder_attention_heads





	@property
	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							return self.d_model





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       copy.deepcopy(self.__dict__			)
							if self.backbone_config is not None:
													_UpperCamelCase :		Tuple			    =       self.backbone_config.to_dict()
							_UpperCamelCase :		Any			    =       self.__class__.model_type
							return output






class 							lowercase__     (							lowercase       ):
	lowercase__      =      version.parse("""1.11"""       )





	@property
	def      UpperCamelCase_  (			self :    Union[str, Any]			):







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





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return 1E-5





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







							'''simple docstring'''
							return 12







'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case_			:     int  	=  logging.get_logger(__name__)

snake_case_			:     List[str]  	=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """gptj"""
	lowercase__      =      {
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(			self :    Any			,lowerCamelCase__ :    Optional[Any]=50400			,lowerCamelCase__ :    Tuple=2048			,lowerCamelCase__ :    Tuple=4096			,lowerCamelCase__ :    int=28			,lowerCamelCase__ :    Optional[Any]=16			,lowerCamelCase__ :    Optional[Any]=64			,lowerCamelCase__ :    List[Any]=None			,lowerCamelCase__ :    List[Any]="gelu_new"			,lowerCamelCase__ :    Optional[Any]=0.0			,lowerCamelCase__ :    List[str]=0.0			,lowerCamelCase__ :    List[Any]=0.0			,lowerCamelCase__ :    Tuple=1E-5			,lowerCamelCase__ :    int=0.0_2			,lowerCamelCase__ :    Any=True			,lowerCamelCase__ :    str=50256			,lowerCamelCase__ :    Any=50256			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       n_positions
							_UpperCamelCase :		Union[str, Any]			    =       n_embd
							_UpperCamelCase :		Any			    =       n_layer
							_UpperCamelCase :		Optional[int]			    =       n_head
							_UpperCamelCase :		List[str]			    =       n_inner
							_UpperCamelCase :		List[Any]			    =       rotary_dim
							_UpperCamelCase :		int			    =       activation_function
							_UpperCamelCase :		Dict			    =       resid_pdrop
							_UpperCamelCase :		Any			    =       embd_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       attn_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       layer_norm_epsilon
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		str			    =       use_cache

							_UpperCamelCase :		Union[str, Any]			    =       bos_token_id
							_UpperCamelCase :		Any			    =       eos_token_id

							super().__init__(
							    bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,tie_word_embeddings=lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Tuple			,lowerCamelCase__ :    PretrainedConfig			,lowerCamelCase__ :    str = "default"			,lowerCamelCase__ :    List[PatchingSpec] = None			,lowerCamelCase__ :    bool = False			,):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			,task=lowerCamelCase__			,patching_specs=lowerCamelCase__			,use_past=lowerCamelCase__			)
							if not getattr(self._config			,'pad_token_id'			,lowerCamelCase__			):
													# TODO: how to do that better?
													_UpperCamelCase :		int			    =       0





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







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
							if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__			,direction='inputs'			)
													_UpperCamelCase :		Tuple			    =       {0: 'batch', 1: 'past_sequence + sequence'}
							else:
													_UpperCamelCase :		Any			    =       {0: 'batch', 1: 'sequence'}

							return common_inputs





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return self._config.n_layer





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







							'''simple docstring'''
							return self._config.n_head





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional[TensorType] = None			,):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       super(lowerCamelCase__			,self			).generate_dummy_inputs(
							    lowerCamelCase__			,batch_size=lowerCamelCase__			,seq_length=lowerCamelCase__			,is_pair=lowerCamelCase__			,framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							_UpperCamelCase :		Tuple			    =       OrderedDict({'input_ids': common_inputs['input_ids']}			)

							# Need to add the past_keys
							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
													else:
																			import torch

																			_UpperCamelCase    ,      _UpperCamelCase :		str			    =       common_inputs['input_ids'].shape
																			# Not using the same length for past_key_values
																			_UpperCamelCase :		Optional[int]			    =       seqlen + 2
																			_UpperCamelCase :		List[Any]			    =       (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			_UpperCamelCase :		Optional[Any]			    =       [
																			    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																			]

							_UpperCamelCase :		Union[str, Any]			    =       common_inputs['attention_mask']
							if self.use_past:
													_UpperCamelCase :		Any			    =       ordered_inputs['attention_mask'].dtype
													_UpperCamelCase :		List[str]			    =       torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__			,lowerCamelCase__			,dtype=lowerCamelCase__			)]			,dim=1			)

							return ordered_inputs





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return 13


