




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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "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							(   UpperCamelCase__		):
     _a							 =						"owlvit_text_model"

     def __init__(  self		,     _a=4_9408		,     _a=512		,     _a=2048		,     _a=12		,     _a=8		,     _a=16		,     _a="quick_gelu"		,     _a=1e-5		,     _a=0.0		,     _a=0.02		,     _a=1.0		,     _a=0		,     _a=4_9406		,     _a=4_9407		,     **_a		,     )						->							int:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Dict			=						vocab_size
           _A							:     List[Any]			=						hidden_size
           _A							:     Optional[int]			=						intermediate_size
           _A							:     Optional[Any]			=						num_hidden_layers
           _A							:     List[Any]			=						num_attention_heads
           _A							:     List[Any]			=						max_position_embeddings
           _A							:     str			=						hidden_act
           _A							:     Optional[Any]			=						layer_norm_eps
           _A							:     Optional[int]			=						attention_dropout
           _A							:     Optional[int]			=						initializer_range
           _A							:     Optional[int]			=						initializer_factor



     @classmethod
     def 					a__	(  cls		,     _a		,     **_a				)						->							"PretrainedConfig":
           cls._set_token_in_kwargs(_a				)

           _A			,   _A							:     Union[str, Any]			=						cls.get_config_dict(_a		,     **_a				)

           # get the text config dict if we are loading from OwlViTConfig
           if config_dict.get("""model_type"""				) == "owlvit":
                 _A							:     Dict			=						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 						lowercase							(   UpperCamelCase__		):
     _a							 =						"owlvit_vision_model"

     def __init__(  self		,     _a=768		,     _a=3072		,     _a=12		,     _a=12		,     _a=3		,     _a=768		,     _a=32		,     _a="quick_gelu"		,     _a=1e-5		,     _a=0.0		,     _a=0.02		,     _a=1.0		,     **_a		,     )						->							Dict:
           super().__init__(**_a				)

           _A							:     int			=						hidden_size
           _A							:     Optional[int]			=						intermediate_size
           _A							:     Tuple			=						num_hidden_layers
           _A							:     Optional[Any]			=						num_attention_heads
           _A							:     Union[str, Any]			=						num_channels
           _A							:     Any			=						image_size
           _A							:     str			=						patch_size
           _A							:     List[Any]			=						hidden_act
           _A							:     Optional[int]			=						layer_norm_eps
           _A							:     Optional[int]			=						attention_dropout
           _A							:     List[str]			=						initializer_range
           _A							:     List[str]			=						initializer_factor



     @classmethod
     def 					a__	(  cls		,     _a		,     **_a				)						->							"PretrainedConfig":
           cls._set_token_in_kwargs(_a				)

           _A			,   _A							:     Tuple			=						cls.get_config_dict(_a		,     **_a				)

           # get the vision config dict if we are loading from OwlViTConfig
           if config_dict.get("""model_type"""				) == "owlvit":
                 _A							:     List[Any]			=						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 						lowercase							(   UpperCamelCase__		):
     _a							 =						"owlvit"
     _a							 =						True

     def __init__(  self		,     _a=None		,     _a=None		,     _a=512		,     _a=2.6592		,     _a=True		,     **_a		,     )						->							Optional[Any]:
           super().__init__(**_a				)

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

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

           _A							:     Dict			=						OwlViTTextConfig(**_a				)
           _A							:     int			=						OwlViTVisionConfig(**_a				)

           _A							:     Any			=						projection_dim
           _A							:     List[Any]			=						logit_scale_init_value
           _A							:     Any			=						return_dict
           _A							:     List[str]			=						1.0

     @classmethod
     def 					a__	(  cls		,     _a		,     **_a				)						->							"PretrainedConfig":
           cls._set_token_in_kwargs(_a				)

           _A			,   _A							:     Optional[Any]			=						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 					a__	(  cls		,     _a		,     _a		,     **_a				)						->							List[str]:
           _A							:     Dict			=						{}
           _A							:     Dict			=						text_config
           _A							:     Optional[Any]			=						vision_config

           return cls.from_dict(_a		,     **_a				)



     def 					a__	(  self				)						->							List[Any]:
           _A							:     Dict			=						copy.deepcopy(self.__dict__				)
           _A							:     Dict			=						self.text_config.to_dict()
           _A							:     Optional[Any]			=						self.vision_config.to_dict()
           _A							:     List[str]			=						self.__class__.model_type
           return output







class 						lowercase							(   UpperCamelCase__		):

     @property
     def 					a__	(  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 					a__	(  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 					a__	(  self				)						->							float:
           return 1e-4

     def 					a__	(  self		,     _a		,     _a = -1		,     _a = -1		,     _a = None		,     )						->							Mapping[str, Any]:
           _A							:     Tuple			=						super().generate_dummy_inputs(
               processor.tokenizer		,     batch_size=_a		,     seq_length=_a		,     framework=_a				)
           _A							:     Any			=						super().generate_dummy_inputs(
               processor.image_processor		,     batch_size=_a		,     framework=_a				)
           return {**text_input_dict, **image_input_dict}



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


"""simple docstring"""






from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[float]		) ->    Optional[Any]:
      '''simple docstring'''


      return np.maximum(0					,		lowerCAmelCase__		)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



