
"""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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Any						   =		'''conditional_detr'''
		__lowercase							:  str						   =		['''past_key_values''']
		__lowercase							:  Union[str, Any]						   =		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		}




		def __init__(     self							,  lowerCAmelCase__=True							,  lowerCAmelCase__=None							,  lowerCAmelCase__=3							,  lowerCAmelCase__=3_0_0							,  lowerCAmelCase__=6							,  lowerCAmelCase__=2_0_4_8							,  lowerCAmelCase__=8							,  lowerCAmelCase__=6							,  lowerCAmelCase__=2_0_4_8							,  lowerCAmelCase__=8							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=True							,  lowerCAmelCase__="relu"							,  lowerCAmelCase__=2_5_6							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1.0							,  lowerCAmelCase__=False							,  lowerCAmelCase__="sine"							,  lowerCAmelCase__="resnet50"							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=2							,  lowerCAmelCase__=5							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1							,  lowerCAmelCase__=2							,  lowerCAmelCase__=5							,  lowerCAmelCase__=2							,  lowerCAmelCase__=0.25							,  **lowerCAmelCase__							,  ):
				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.""")
								__SCREAMING_SNAKE_CASE            = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""])
						elif isinstance(lowerCAmelCase__							,  lowerCAmelCase__):
								__SCREAMING_SNAKE_CASE            = backbone_config.get("""model_type""")
								__SCREAMING_SNAKE_CASE            = CONFIG_MAPPING[backbone_model_type]
								__SCREAMING_SNAKE_CASE            = config_class.from_dict(lowerCAmelCase__)

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




		@property
		def 	snake_case_     (     self):
				return self.encoder_attention_heads




		@property
		def 	snake_case_     (     self):
				return self.d_model




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



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		version.parse('''1.11''' )




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




		@property
		def 	snake_case_     (     self):
				return 1E-5




		@property
		def 	snake_case_     (     self):
				return 1_2






"""simple docstring"""





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''blip_2_vision_model'''




		def __init__(     self							,  lowerCAmelCase__=1_4_0_8							,  lowerCAmelCase__=6_1_4_4							,  lowerCAmelCase__=3_9							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2_2_4							,  lowerCAmelCase__=1_4							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.0_00_01							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=1E-10							,  lowerCAmelCase__=True							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = patch_size
				__SCREAMING_SNAKE_CASE            = image_size
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = attention_dropout
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = qkv_bias




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				cls._set_token_in_kwargs(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				# get the vision config dict if we are loading from Blip2Config
				if config_dict.get("""model_type""") == "blip-2":
						__SCREAMING_SNAKE_CASE            = config_dict["""vision_config"""]

				if "model_type" in config_dict and hasattr(cls							,  """model_type""") and config_dict["model_type"] != cls.model_type:
						logger.warning(
						    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
						    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")

				return cls.from_dict(lowerCAmelCase__							,  **lowerCAmelCase__)



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''blip_2_qformer'''




		def __init__(     self							,  lowerCAmelCase__=3_0_5_2_2							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=3_0_7_2							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_2							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1E-12							,  lowerCAmelCase__=0							,  lowerCAmelCase__="absolute"							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1_4_0_8							,  **lowerCAmelCase__							,  ):
				super().__init__(pad_token_id=lowerCAmelCase__							,  **lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = position_embedding_type
				__SCREAMING_SNAKE_CASE            = cross_attention_frequency
				__SCREAMING_SNAKE_CASE            = encoder_hidden_size




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				cls._set_token_in_kwargs(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				# get the qformer config dict if we are loading from Blip2Config
				if config_dict.get("""model_type""") == "blip-2":
						__SCREAMING_SNAKE_CASE            = config_dict["""qformer_config"""]

				if "model_type" in config_dict and hasattr(cls							,  """model_type""") and config_dict["model_type"] != cls.model_type:
						logger.warning(
						    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
						    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")

				return cls.from_dict(lowerCAmelCase__							,  **lowerCAmelCase__)



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''blip-2'''
		__lowercase							:  Any						   =		True




		def __init__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=3_2							,  **lowerCAmelCase__):
				super().__init__(**lowerCAmelCase__)

				if vision_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values.""")

				if qformer_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values.""")

				if text_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""")

				__SCREAMING_SNAKE_CASE            = BlipaVisionConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = BlipaQFormerConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
				__SCREAMING_SNAKE_CASE            = CONFIG_MAPPING[text_model_type](**lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.text_config.tie_word_embeddings
				__SCREAMING_SNAKE_CASE            = self.text_config.is_encoder_decoder

				__SCREAMING_SNAKE_CASE            = num_query_tokens
				__SCREAMING_SNAKE_CASE            = self.vision_config.hidden_size
				__SCREAMING_SNAKE_CASE            = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
				__SCREAMING_SNAKE_CASE            = 1.0
				__SCREAMING_SNAKE_CASE            = 0.02




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  **lowerCAmelCase__							,  ):
				return cls(
				    vision_config=vision_config.to_dict()							,  qformer_config=qformer_config.to_dict()							,  text_config=text_config.to_dict()							,  **lowerCAmelCase__							,  )




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = copy.deepcopy(self.__dict__)
				__SCREAMING_SNAKE_CASE            = self.vision_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.qformer_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.text_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.__class__.model_type
				return output




