



'''simple docstring'''

import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


a							   =				logging.get_logger(__name__)  # pylint: disable=invalid-name






class 						a_		(     snake_case    ):


							def __init__(				self   :						Union[str, Any]      ,						a_   :						List[str]      ,						a_   :						str=7_6_8			)			->					List[Any]:
										super().__init__(a_			)
										snake_case:    List[Any]      							=proj_size

										snake_case:    Optional[Any]      							=CLIPVisionModel(a_			)
										snake_case:    Optional[int]      							=PaintByExampleMapper(a_			)
										snake_case:    Union[str, Any]      							=nn.LayerNorm(config.hidden_size			)
										snake_case:    List[Any]      							=nn.Linear(config.hidden_size      ,						self.proj_size			)

										# uncondition for scaling
										snake_case:    Dict      							=nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)




							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Tuple      ,						a_   :						Any=False			)			->					Optional[Any]:
										snake_case:    List[str]      							=self.model(pixel_values=a_			)
										snake_case:    int      							=clip_output.pooler_output
										snake_case:    List[str]      							=self.mapper(latent_states[:, None]			)
										snake_case:    Any      							=self.final_layer_norm(a_			)
										snake_case:    Optional[int]      							=self.proj_out(a_			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states






class 						a_		(     nn.Module    ):


							def __init__(				self   :						int      ,						a_   :						str			)			->					List[Any]:
										super().__init__()
										snake_case:    Any      							=(config.num_hidden_layers + 1) // 5
										snake_case:    Optional[int]      							=config.hidden_size
										snake_case:    Optional[int]      							=1
										snake_case:    Tuple      							=nn.ModuleList(
										    [
										        BasicTransformerBlock(a_      ,						a_      ,						a_      ,						activation_fn='gelu'      ,						attention_bias=a_			)
										        for _ in range(a_			)
										    ]			)




							def    UpperCamelCase     (				self   :						str      ,						a_   :						Union[str, Any]			)			->					List[Any]:
										for block in self.blocks:
													snake_case:    Any      							=block(a_			)

										return hidden_states









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a							   =				{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				['OwlViTFeatureExtractor']
	a							   =				['OwlViTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				[
	    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OwlViTModel',
	    'OwlViTPreTrainedModel',
	    'OwlViTTextModel',
	    'OwlViTVisionModel',
	    'OwlViTForObjectDetection',
	]

if TYPE_CHECKING:
	from .configuration_owlvit import (
	    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    OwlViTConfig,
	    OwlViTOnnxConfig,
	    OwlViTTextConfig,
	    OwlViTVisionConfig,
	)
	from .processing_owlvit import OwlViTProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_owlvit import OwlViTFeatureExtractor
		from .image_processing_owlvit import OwlViTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_owlvit import (
		    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OwlViTForObjectDetection,
		    OwlViTModel,
		    OwlViTPreTrainedModel,
		    OwlViTTextModel,
		    OwlViTVisionModel,
		)

else:
	import sys

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




