


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a : Union[str, Any]		=				{
    '''configuration_altclip''': [
        '''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AltCLIPConfig''',
        '''AltCLIPTextConfig''',
        '''AltCLIPVisionConfig''',
    ],
    '''processing_altclip''': ['''AltCLIPProcessor'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Optional[int]		=				[
	    '''ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''AltCLIPPreTrainedModel''',
	    '''AltCLIPModel''',
	    '''AltCLIPTextModel''',
	    '''AltCLIPVisionModel''',
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	a : List[Any]		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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 : Union[str, Any]		=				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 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit_text_model"






					def __init__(				self ,       snake_case_=4_9_4_0_8 ,       snake_case_=5_1_2 ,       snake_case_=2_0_4_8 ,       snake_case_=1_2 ,       snake_case_=8 ,       snake_case_=1_6 ,       snake_case_="quick_gelu" ,       snake_case_=1e-5 ,       snake_case_=0.0 ,       snake_case_=0.0_2 ,       snake_case_=1.0 ,       snake_case_=0 ,       snake_case_=4_9_4_0_6 ,       snake_case_=4_9_4_0_7 ,       **snake_case_ ,       )   ->			int:







								'''simple docstring'''

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

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






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

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




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit_vision_model"






					def __init__(				self ,       snake_case_=7_6_8 ,       snake_case_=3_0_7_2 ,       snake_case_=1_2 ,       snake_case_=1_2 ,       snake_case_=3 ,       snake_case_=7_6_8 ,       snake_case_=3_2 ,       snake_case_="quick_gelu" ,       snake_case_=1e-5 ,       snake_case_=0.0 ,       snake_case_=0.0_2 ,       snake_case_=1.0 ,       **snake_case_ ,       )   ->			Any:







								'''simple docstring'''

								super().__init__(**snake_case_						)

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






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

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




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit"
					__UpperCAmelCase            =				True






					def __init__(				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_=5_1_2 ,       snake_case_=2.6_5_9_2 ,       snake_case_=True ,       **snake_case_ ,       )   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__(**snake_case_						)

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

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

								__lowercase 					=   OwlViTTextConfig(**snake_case_						)
								__lowercase 					=   OwlViTVisionConfig(**snake_case_						)

								__lowercase 					=   projection_dim
								__lowercase 					=   logit_scale_init_value
								__lowercase 					=   return_dict
								__lowercase 					=   1.0






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **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(snake_case_ ,       **snake_case_						)






					@classmethod
					def A      (				cls ,       snake_case_ ,       snake_case_ ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {}
								__lowercase 					=   text_config
								__lowercase 					=   vision_config

								return cls.from_dict(snake_case_ ,       **snake_case_						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   copy.deepcopy(self.__dict__						)
								__lowercase 					=   self.text_config.to_dict()
								__lowercase 					=   self.vision_config.to_dict()
								__lowercase 					=   self.__class__.model_type
								return output




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					@property
					def A      (				self						)   ->			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 A      (				self						)   ->			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 A      (				self						)   ->			float:







								'''simple docstring'''

								return 1e-4






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

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






					@property
					def A      (				self						)   ->			int:







								'''simple docstring'''

								return 1_4


