

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


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """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    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       List[str]  					=    'owlvit_text_model'

   def __init__(				self	:    Union[str, Any]			,						lowerCamelCase__	:    List[str]=4_9_4_0_8			,						lowerCamelCase__	:    int=5_1_2			,						lowerCamelCase__	:    Any=2_0_4_8			,						lowerCamelCase__	:    Optional[int]=1_2			,						lowerCamelCase__	:    Dict=8			,						lowerCamelCase__	:    Dict=1_6			,						lowerCamelCase__	:    Optional[int]="quick_gelu"			,						lowerCamelCase__	:    List[str]=1e-5			,						lowerCamelCase__	:    Tuple=0.0			,						lowerCamelCase__	:    Union[str, Any]=0.02			,						lowerCamelCase__	:    int=1.0			,						lowerCamelCase__	:    List[Any]=0			,						lowerCamelCase__	:    Optional[int]=4_9_4_0_6			,						lowerCamelCase__	:    List[str]=4_9_4_0_7			,						**lowerCamelCase__	:    Optional[Any]			,						) ->			Optional[int]:
         """simple docstring"""





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

         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	:    Tuple			,						lowerCamelCase__	:    Union[str, os.PathLike]			,						**lowerCamelCase__	:    Optional[Any]       ) ->			"PretrainedConfig":
         """simple docstring"""





         cls._set_token_in_kwargs(lowerCamelCase__       )

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

         # 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(lowerCamelCase__			,						**lowerCamelCase__       )



class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Dict  					=    'owlvit_vision_model'

   def __init__(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Tuple=7_6_8			,						lowerCamelCase__	:    Optional[int]=3_0_7_2			,						lowerCamelCase__	:    Optional[Any]=1_2			,						lowerCamelCase__	:    List[Any]=1_2			,						lowerCamelCase__	:    Optional[Any]=3			,						lowerCamelCase__	:    str=7_6_8			,						lowerCamelCase__	:    Optional[Any]=3_2			,						lowerCamelCase__	:    Union[str, Any]="quick_gelu"			,						lowerCamelCase__	:    Any=1e-5			,						lowerCamelCase__	:    Optional[int]=0.0			,						lowerCamelCase__	:    Any=0.02			,						lowerCamelCase__	:    List[str]=1.0			,						**lowerCamelCase__	:    Tuple			,						) ->			Union[str, Any]:
         """simple docstring"""





         super().__init__(**lowerCamelCase__       )

         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	:    str			,						lowerCamelCase__	:    Union[str, os.PathLike]			,						**lowerCamelCase__	:    List[str]       ) ->			"PretrainedConfig":
         """simple docstring"""





         cls._set_token_in_kwargs(lowerCamelCase__       )

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

         # 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(lowerCamelCase__			,						**lowerCamelCase__       )



class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Optional[int]  					=    'owlvit'
   _lowercase     :       List[str]  					=    True

   def __init__(				self	:    Dict			,						lowerCamelCase__	:    Optional[int]=None			,						lowerCamelCase__	:    Any=None			,						lowerCamelCase__	:    Union[str, Any]=5_1_2			,						lowerCamelCase__	:    int=2.6592			,						lowerCamelCase__	:    Optional[Any]=True			,						**lowerCamelCase__	:    List[str]			,						) ->			Tuple:
         """simple docstring"""





         super().__init__(**lowerCamelCase__       )

         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(**lowerCamelCase__       )
         A_							       =		OwlViTVisionConfig(**lowerCamelCase__       )

         A_							       =		projection_dim
         A_							       =		logit_scale_init_value
         A_							       =		return_dict
         A_							       =		1.0

   @classmethod
   def     UpperCamelCase					(				cls	:    Any			,						lowerCamelCase__	:    Union[str, os.PathLike]			,						**lowerCamelCase__	:    Optional[Any]       ) ->			"PretrainedConfig":
         """simple docstring"""





         cls._set_token_in_kwargs(lowerCamelCase__       )

         A_       ,A_							       =		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     UpperCamelCase					(				cls	:    Dict			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Dict			,						**lowerCamelCase__	:    Optional[int]       ) ->			Tuple:
         """simple docstring"""





         A_							       =		{}
         A_							       =		text_config
         A_							       =		vision_config

         return cls.from_dict(lowerCamelCase__			,						**lowerCamelCase__       )





   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Any:
         """simple docstring"""





         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    _lowercase				(					__lowerCamelCase			):

   @property
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         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	:    Optional[Any]       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         return OrderedDict(
             [
                 ('''logits_per_image''', {0: '''batch'''}),
                 ('''logits_per_text''', {0: '''batch'''}),
                 ('''text_embeds''', {0: '''batch'''}),
                 ('''image_embeds''', {0: '''batch'''}),
             ]       )

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			float:
         """simple docstring"""





         return 1e-4

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    "ProcessorMixin"			,						lowerCamelCase__	:    int = -1			,						lowerCamelCase__	:    int = -1			,						lowerCamelCase__	:    Optional["TensorType"] = None			,						) ->			Mapping[str, Any]:
         """simple docstring"""





         A_							       =		super().generate_dummy_inputs(
             processor.tokenizer			,						batch_size=lowerCamelCase__			,						seq_length=lowerCamelCase__			,						framework=lowerCamelCase__       )
         A_							       =		super().generate_dummy_inputs(
             processor.image_processor			,						batch_size=lowerCamelCase__			,						framework=lowerCamelCase__       )
         return {**text_input_dict, **image_input_dict}





   @property
   def     UpperCamelCase					(				self	:    Dict       ) ->			int:
         """simple docstring"""





         return 1_4








__lowercase				      =						"""Alexander Joslin"""

import operator as op

from .stack import Stack







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		{'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

      A_							       =		Stack()
      A_							       =		Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(SCREAMING_SNAKE_CASE   )   )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(SCREAMING_SNAKE_CASE   )
            elif i == ")":
                  # RULE 4
                  A_							       =		operator_stack.peek()
                  operator_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()

                  A_							       =		operators[opr](SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                  operand_stack.push(SCREAMING_SNAKE_CASE   )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
      __lowercase				      =						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





