


'''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


a_					:    str          =    logging.get_logger(__name__)

a_					:    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  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""owlvit_text_model"""
 def __init__( self      ,				__magic_name__=4_94_08      ,				__magic_name__=5_12      ,				__magic_name__=20_48      ,				__magic_name__=12      ,				__magic_name__=8      ,				__magic_name__=16      ,				__magic_name__="quick_gelu"      ,				__magic_name__=1e-5      ,				__magic_name__=0.0      ,				__magic_name__=0.0_2      ,				__magic_name__=1.0      ,				__magic_name__=0      ,				__magic_name__=4_94_06      ,				__magic_name__=4_94_07      ,				**__magic_name__      ,				)					->							Tuple:
      super().__init__(pad_token_id=__magic_name__      ,				bos_token_id=__magic_name__      ,				eos_token_id=__magic_name__      ,				**__magic_name__						)

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




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				__magic_name__      ,				**__magic_name__						)					->							"PretrainedConfig":
      cls._set_token_in_kwargs(__magic_name__						)

      _a      ,   _a			      =							cls.get_config_dict(__magic_name__      ,				**__magic_name__						)

      # get the text config dict if we are loading from OwlViTConfig
      if config_dict.get('model_type'						) == "owlvit":
           _a			      =							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(__magic_name__      ,				**__magic_name__						)





class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""owlvit_vision_model"""
 def __init__( self      ,				__magic_name__=7_68      ,				__magic_name__=30_72      ,				__magic_name__=12      ,				__magic_name__=12      ,				__magic_name__=3      ,				__magic_name__=7_68      ,				__magic_name__=32      ,				__magic_name__="quick_gelu"      ,				__magic_name__=1e-5      ,				__magic_name__=0.0      ,				__magic_name__=0.0_2      ,				__magic_name__=1.0      ,				**__magic_name__      ,				)					->							Dict:
      super().__init__(**__magic_name__						)

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




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				__magic_name__      ,				**__magic_name__						)					->							"PretrainedConfig":
      cls._set_token_in_kwargs(__magic_name__						)

      _a      ,   _a			      =							cls.get_config_dict(__magic_name__      ,				**__magic_name__						)

      # get the vision config dict if we are loading from OwlViTConfig
      if config_dict.get('model_type'						) == "owlvit":
           _a			      =							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(__magic_name__      ,				**__magic_name__						)





class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""owlvit"""
 _lowerCAmelCase 					=	True
 def __init__( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=5_12      ,				__magic_name__=2.6_5_9_2      ,				__magic_name__=True      ,				**__magic_name__      ,				)					->							Tuple:
      super().__init__(**__magic_name__						)

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

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

      _a			      =							OwlViTTextConfig(**__magic_name__						)
      _a			      =							OwlViTVisionConfig(**__magic_name__						)

      _a			      =							projection_dim
      _a			      =							logit_scale_init_value
      _a			      =							return_dict
      _a			      =							1.0
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				__magic_name__      ,				**__magic_name__						)					->							"PretrainedConfig":
      cls._set_token_in_kwargs(__magic_name__						)

      _a      ,   _a			      =							cls.get_config_dict(__magic_name__      ,				**__magic_name__						)

      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(__magic_name__      ,				**__magic_name__						)
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				__magic_name__      ,				__magic_name__      ,				**__magic_name__						)					->							Optional[int]:
      _a			      =							{}
      _a			      =							text_config
      _a			      =							vision_config

      return cls.from_dict(__magic_name__      ,				**__magic_name__						)




 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							copy.deepcopy(self.__dict__						)
      _a			      =							self.text_config.to_dict()
      _a			      =							self.vision_config.to_dict()
      _a			      =							self.__class__.model_type
      return output





class  a     (     _SCREAMING_SNAKE_CASE       ):
 @property
 def 							__UpperCAmelCase		( self						)					->							Mapping[str, Mapping[int, str]]:
      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 							__UpperCAmelCase		( self						)					->							Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ('logits_per_image', {0: 'batch'}),
              ('logits_per_text', {0: 'batch'}),
              ('text_embeds', {0: 'batch'}),
              ('image_embeds', {0: 'batch'}),
          ]						)
 @property
 def 							__UpperCAmelCase		( self						)					->							float:
      return 1e-4
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = -1      ,				__magic_name__ = -1      ,				__magic_name__ = None      ,				)					->							Mapping[str, Any]:
      _a			      =							super().generate_dummy_inputs(
          processor.tokenizer      ,				batch_size=__magic_name__      ,				seq_length=__magic_name__      ,				framework=__magic_name__						)
      _a			      =							super().generate_dummy_inputs(
          processor.image_processor      ,				batch_size=__magic_name__      ,				framework=__magic_name__						)
      return {**text_input_dict, **image_input_dict}




 @property
 def 							__UpperCAmelCase		( self						)					->							int:
      return 14





'''simple docstring'''






class  a     :
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							n
      _a			      =							[None] * self.n
      _a			      =							0  # index of the first element
      _a			      =							0
      _a			      =							0
 def __len__( self						)					->							int:
      return self.size
 def 							__UpperCAmelCase		( self						)					->							bool:
      return self.size == 0
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return False if self.is_empty() else self.array[self.front]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if self.size >= self.n:
           raise Exception('QUEUE IS FULL'						)

      _a			      =							data
      _a			      =							(self.rear + 1) % self.n
      self.size += 1
      return self




 def 							__UpperCAmelCase		( self						)					->							Any:
      if self.size == 0:
           raise Exception('UNDERFLOW'						)

      _a			      =							self.array[self.front]
      _a			      =							None
      _a			      =							(self.front + 1) % self.n
      self.size -= 1
      return temp

