


"""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	:  List[str]            =     logging.get_logger(__name__)

A	:  int            =     {
    "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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 ="""owlvit_text_model"""

							def __init__(	self       ,  __a=4_94_08       ,  __a=5_12       ,  __a=20_48       ,  __a=12       ,  __a=8       ,  __a=16       ,  __a="quick_gelu"       ,  __a=1e-5       ,  __a=0.0       ,  __a=0.0_2       ,  __a=1.0       ,  __a=0       ,  __a=4_94_06       ,  __a=4_94_07       ,  **__a       ,  ):
										super().__init__(pad_token_id=__a       ,  bos_token_id=__a       ,  eos_token_id=__a       ,  **__a      )

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



							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										cls._set_token_in_kwargs(__a      )

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

										# get the text config dict if we are loading from OwlViTConfig
										if config_dict.get("model_type"      ) == "owlvit":
													__lowerCAmelCase							       =     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(__a       ,  **__a      )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 ="""owlvit_vision_model"""

							def __init__(	self       ,  __a=7_68       ,  __a=30_72       ,  __a=12       ,  __a=12       ,  __a=3       ,  __a=7_68       ,  __a=32       ,  __a="quick_gelu"       ,  __a=1e-5       ,  __a=0.0       ,  __a=0.0_2       ,  __a=1.0       ,  **__a       ,  ):
										super().__init__(**__a      )

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



							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										cls._set_token_in_kwargs(__a      )

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

										# get the vision config dict if we are loading from OwlViTConfig
										if config_dict.get("model_type"      ) == "owlvit":
													__lowerCAmelCase							       =     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(__a       ,  **__a      )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 ="""owlvit"""
							__UpperCAmelCase		:       Dict							 =True

							def __init__(	self       ,  __a=None       ,  __a=None       ,  __a=5_12       ,  __a=2.6_5_9_2       ,  __a=True       ,  **__a       ,  ):
										super().__init__(**__a      )

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

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

										__lowerCAmelCase							       =     OwlViTTextConfig(**__a      )
										__lowerCAmelCase							       =     OwlViTVisionConfig(**__a      )

										__lowerCAmelCase							       =     projection_dim
										__lowerCAmelCase							       =     logit_scale_init_value
										__lowerCAmelCase							       =     return_dict
										__lowerCAmelCase							       =     1.0

							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										cls._set_token_in_kwargs(__a      )

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

										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(__a       ,  **__a      )

							@classmethod
							def      snake_case							(	cls       ,  __a       ,  __a       ,  **__a      ):
										__lowerCAmelCase							       =     {}
										__lowerCAmelCase							       =     text_config
										__lowerCAmelCase							       =     vision_config

										return cls.from_dict(__a       ,  **__a      )



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     copy.deepcopy(self.__dict__      )
										__lowerCAmelCase							       =     self.text_config.to_dict()
										__lowerCAmelCase							       =     self.vision_config.to_dict()
										__lowerCAmelCase							       =     self.__class__.model_type
										return output
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

							@property
							def      snake_case							(	self      ):
										return 1e-4

							def      snake_case							(	self       ,  __a       ,  __a = -1       ,  __a = -1       ,  __a = None       ,  ):
										__lowerCAmelCase							       =     super().generate_dummy_inputs(
										    processor.tokenizer       ,  batch_size=__a       ,  seq_length=__a       ,  framework=__a      )
										__lowerCAmelCase							       =     super().generate_dummy_inputs(
										    processor.image_processor       ,  batch_size=__a       ,  framework=__a      )
										return {**text_input_dict, **image_input_dict}



							@property
							def      snake_case							(	self      ):
										return 14








"""simple docstring"""


from __future__ import annotations
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a=None      ):
										__lowerCAmelCase							       =     data
										__lowerCAmelCase							       =     None



							def __repr__(	self      ):
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     self
										while temp:
													string_rep.append(f"{temp.data}"      )
													__lowerCAmelCase							       =     temp.next
										return "->".join(__a      )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not elements_list:
						raise Exception("The Elements List is empty" )

			__lowerCAmelCase							       =     __lowerCAmelCase							       =     Node(elements_list[0] )
			for i in range(1       ,					len(_UpperCamelCase ) ):
						__lowerCAmelCase							       =     Node(elements_list[i] )
						__lowerCAmelCase							       =     current.next
			return head


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if head_node is not None and isinstance(_UpperCamelCase       ,					_UpperCamelCase ):
						print_reverse(head_node.next )
						print(head_node.data )


def _lowerCamelCase      (						):

			'''simple docstring'''




			from doctest import testmod

			testmod()

			__lowerCAmelCase							       =     make_linked_list([14, 52, 14, 12, 43] )
			print("Linked List:" )
			print(_UpperCamelCase )
			print("Elements in Reverse:" )
			print_reverse(_UpperCamelCase )


if __name__ == "__main__":
	main()




