"""simple docstring"""






import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__  :  Optional[int]						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  Optional[Any]						     =	{
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "owlvit_text_model"







					def __init__(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]=49_408    ,lowerCamelCase__   :	Optional[Any]=512    ,lowerCamelCase__   :	List[str]=2_048    ,lowerCamelCase__   :	Tuple=12    ,lowerCamelCase__   :	Optional[Any]=8    ,lowerCamelCase__   :	str=16    ,lowerCamelCase__   :	int="quick_gelu"    ,lowerCamelCase__   :	int=1e-5    ,lowerCamelCase__   :	int=0.0    ,lowerCamelCase__   :	Optional[int]=0.0_2    ,lowerCamelCase__   :	List[str]=1.0    ,lowerCamelCase__   :	int=0    ,lowerCamelCase__   :	Optional[int]=49_406    ,lowerCamelCase__   :	Dict=49_407    ,**lowerCamelCase__   :	Optional[Any]    ,):
									super().__init__(pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,**lowerCamelCase__			)

									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							hidden_size
									UpperCAmelCase__             =							intermediate_size
									UpperCAmelCase__             =							num_hidden_layers
									UpperCAmelCase__             =							num_attention_heads
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							layer_norm_eps
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							initializer_factor







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	Tuple    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,**lowerCamelCase__   :	Any			):
									cls._set_token_in_kwargs(lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							cls.get_config_dict(lowerCamelCase__    ,**lowerCamelCase__			)

									# get the text config dict if we are loading from OwlViTConfig
									if config_dict.get('model_type'			) == "owlvit":
													UpperCAmelCase__             =							config_dict['text_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 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "owlvit_vision_model"







					def __init__(				self   :	str    ,lowerCamelCase__   :	int=768    ,lowerCamelCase__   :	List[str]=3_072    ,lowerCamelCase__   :	Union[str, Any]=12    ,lowerCamelCase__   :	Any=12    ,lowerCamelCase__   :	str=3    ,lowerCamelCase__   :	Optional[Any]=768    ,lowerCamelCase__   :	Any=32    ,lowerCamelCase__   :	Tuple="quick_gelu"    ,lowerCamelCase__   :	Optional[Any]=1e-5    ,lowerCamelCase__   :	Optional[int]=0.0    ,lowerCamelCase__   :	Optional[int]=0.0_2    ,lowerCamelCase__   :	Any=1.0    ,**lowerCamelCase__   :	Dict    ,):
									super().__init__(**lowerCamelCase__			)

									UpperCAmelCase__             =							hidden_size
									UpperCAmelCase__             =							intermediate_size
									UpperCAmelCase__             =							num_hidden_layers
									UpperCAmelCase__             =							num_attention_heads
									UpperCAmelCase__             =							num_channels
									UpperCAmelCase__             =							image_size
									UpperCAmelCase__             =							patch_size
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							layer_norm_eps
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							initializer_factor







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	int    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,**lowerCamelCase__   :	Union[str, Any]			):
									cls._set_token_in_kwargs(lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							cls.get_config_dict(lowerCamelCase__    ,**lowerCamelCase__			)

									# get the vision config dict if we are loading from OwlViTConfig
									if config_dict.get('model_type'			) == "owlvit":
													UpperCAmelCase__             =							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 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "owlvit"
					snake_case__				    = True







					def __init__(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Union[str, Any]=None    ,lowerCamelCase__   :	str=None    ,lowerCamelCase__   :	Optional[int]=512    ,lowerCamelCase__   :	Union[str, Any]=2.6_5_9_2    ,lowerCamelCase__   :	Tuple=True    ,**lowerCamelCase__   :	List[str]    ,):
									super().__init__(**lowerCamelCase__			)

									if text_config is None:
													UpperCAmelCase__             =							{}
													logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'			)

									if vision_config is None:
													UpperCAmelCase__             =							{}
													logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'			)

									UpperCAmelCase__             =							OwlViTTextConfig(**lowerCamelCase__			)
									UpperCAmelCase__             =							OwlViTVisionConfig(**lowerCamelCase__			)

									UpperCAmelCase__             =							projection_dim
									UpperCAmelCase__             =							logit_scale_init_value
									UpperCAmelCase__             =							return_dict
									UpperCAmelCase__             =							1.0







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	Dict    ,lowerCamelCase__   :	Union[str, os.PathLike]    ,**lowerCamelCase__   :	str			):
									cls._set_token_in_kwargs(lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							cls.get_config_dict(lowerCamelCase__    ,**lowerCamelCase__			)

									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__			)







					@classmethod
					def 		__lowerCAmelCase						(				cls   :	Optional[int]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Dict    ,**lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							{}
									UpperCAmelCase__             =							text_config
									UpperCAmelCase__             =							vision_config

									return cls.from_dict(lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							copy.deepcopy(self.__dict__			)
									UpperCAmelCase__             =							self.text_config.to_dict()
									UpperCAmelCase__             =							self.vision_config.to_dict()
									UpperCAmelCase__             =							self.__class__.model_type
									return output






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(				self   :	Any			):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]			)







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return OrderedDict(
									    [
									        ('logits_per_image', {0: 'batch'}),
									        ('logits_per_text', {0: 'batch'}),
									        ('text_embeds', {0: 'batch'}),
									        ('image_embeds', {0: 'batch'}),
									    ]			)







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									return 1e-4







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	"ProcessorMixin"    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	Optional["TensorType"] = None    ,):
									UpperCAmelCase__             =							super().generate_dummy_inputs(
									    processor.tokenizer    ,batch_size=lowerCamelCase__    ,seq_length=lowerCamelCase__    ,framework=lowerCamelCase__			)
									UpperCAmelCase__             =							super().generate_dummy_inputs(
									    processor.image_processor    ,batch_size=lowerCamelCase__    ,framework=lowerCamelCase__			)
									return {**text_input_dict, **image_input_dict}







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return 14




"""simple docstring"""






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = None
					snake_case__				    = None






lowerCAmelCase__  :  Union[str, Any]						     =	namedtuple('CoinsDistribResult', 'moves excess')



def   a_						(					lowerCamelCase       ):

				if root is None:
								return 0

				# Validation
				def count_nodes(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_nodes(node.left       ) + count_nodes(node.right       ) + 1

				def count_coins(lowerCamelCase       ) -> int:
								if node is None:
												return 0

								return count_coins(node.left       ) + count_coins(node.right       ) + node.data

				if count_nodes(lowerCamelCase       ) != count_coins(lowerCamelCase       ):
								raise ValueError('The nodes number should be same as the number of coins'       )

				# Main calculation
				def get_distrib(lowerCamelCase       ) -> CoinsDistribResult:

								if node is None:
												return CoinsDistribResult(0					,			1       )

								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.left       )
								UpperCAmelCase__      ,	UpperCAmelCase__             =							get_distrib(node.right       )

								UpperCAmelCase__             =							1 - left_distrib_excess
								UpperCAmelCase__             =							1 - right_distrib_excess

								UpperCAmelCase__             =							(
								    left_distrib_moves
								    + right_distrib_moves
								    + abs(lowerCamelCase       )
								    + abs(lowerCamelCase       )
								)
								UpperCAmelCase__             =							node.data - coins_to_left - coins_to_right

								return CoinsDistribResult(lowerCamelCase					,			lowerCamelCase       )

				return get_distrib(lowerCamelCase       )[0]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



