






"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCamelCase_							      = {
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__      ,A__		):
			if got_ver is None or want_ver is None:
						raise ValueError(
						    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
						    F""" reinstalling {pkg}."""		)
			if not ops[op](version.parse(A__		)      ,version.parse(A__		)		):
						raise ImportError(
						    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""		)

def 							snake_case (  A__      ,A__ = None		):
			UpperCAmelCase_	:					int     	=   F"""\n{hint}""" if hint is not None else ""

			# non-versioned check
			if re.match(r"^[\w_\-\d]+$"      ,A__		):
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   requirement, None, None
			else:
						UpperCAmelCase_	:					Optional[int]     	=   re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"      ,A__		)
						if not match:
									raise ValueError(
									    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
									    F""" got {requirement}"""		)
						UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   match[0]
						UpperCAmelCase_	:					Optional[Any]     	=   want_full.split(","		)  # there could be multiple requirements
						UpperCAmelCase_	:					int     	=   {}
						for w in want_range:
									UpperCAmelCase_	:					str     	=   re.findall(r"^([\s!=<>]{1,2})(.+)"      ,A__		)
									if not match:
												raise ValueError(
												    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
												    F""" but got {requirement}"""		)
									UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   match[0]
									UpperCAmelCase_	:					Union[str, Any]     	=   want_ver
									if op not in ops:
												raise ValueError(F"""{requirement}: need one of {list(ops.keys()		)}, but got {op}"""		)

    # special case
			if pkg == "python":
						UpperCAmelCase_	:					List[Any]     	=   ".".join([str(A__		) for x in sys.version_info[:3]]		)
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)
						return

			# check if any version is installed
			try:
						UpperCAmelCase_	:					str     	=   importlib.metadata.version(A__		)
			except importlib.metadata.PackageNotFoundError:
						raise importlib.metadata.PackageNotFoundError(
						    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""		)

			# check that the right version is installed if version number or a range was provided
			if want_ver is not None:
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)




def 							snake_case (  A__		):
			UpperCAmelCase_	:					Any     	=   "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
			return require_version(A__      ,A__		)




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


SCREAMING_SNAKE_CASE			:						Dict				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Tuple				  =		{
    """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_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_text_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=4_94_08  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=20_48  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=8  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   Tuple="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Any=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.02  ,				__SCREAMING_SNAKE_CASE			:   int=1.0  ,				__SCREAMING_SNAKE_CASE			:   int=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_06  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_07  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __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			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

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

   # 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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_vision_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=30_72  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   str=3  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=32  ,				__SCREAMING_SNAKE_CASE			:   Dict="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   int=0.0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Dict=1.0  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __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			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Tuple							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

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

   # 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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				__SCREAMING_SNAKE_CASE			:   str=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2.65_92  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   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(**__SCREAMING_SNAKE_CASE							)
   __a							=	OwlViTVisionConfig(**__SCREAMING_SNAKE_CASE							)

   __a							=	projection_dim
   __a							=	logit_scale_init_value
   __a							=	return_dict
   __a							=	1.0

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

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

   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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	{}
   __a							=	text_config
   __a							=	vision_config

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __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_			(							a_			):

  @property
  def 		_UpperCAmelCase (		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 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return OrderedDict(
       [
           ("logits_per_image", {0: "batch"}),
           ("logits_per_text", {0: "batch"}),
           ("text_embeds", {0: "batch"}),
           ("image_embeds", {0: "batch"}),
       ]							)

  @property
  def 		_UpperCAmelCase (		self			:   str							):
   return 1E-4

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

  @property
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   return 14


