




"""simple docstring"""







from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


a      =							logging.get_logger(__name__)

# General docstring
a      =							'ResNetConfig'

# Base docstring
a      =							'microsoft/resnet-50'
a      =							[1, 2_0_4_8, 7, 7]

# Image classification docstring
a      =							'microsoft/resnet-50'
a      =							'tiger cat'

a      =							[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Tuple						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 3						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			str = "relu"					):
										super().__init__()
										lowerCAmelCase  =				nn.Convad(
										    lowerCAmelCase						,						lowerCAmelCase						,						kernel_size=lowerCAmelCase						,						stride=lowerCAmelCase						,						padding=kernel_size // 2						,						bias=lowerCAmelCase					)
										lowerCAmelCase  =				nn.BatchNormad(lowerCAmelCase					)
										lowerCAmelCase  =				ACTaFN[activation] if activation is not None else nn.Identity()




						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			Tensor					):
										lowerCAmelCase  =				self.convolution(lowerCAmelCase					)
										lowerCAmelCase  =				self.normalization(lowerCAmelCase					)
										lowerCAmelCase  =				self.activation(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			ResNetConfig					):
										super().__init__()
										lowerCAmelCase  =				ResNetConvLayer(
										    config.num_channels						,						config.embedding_size						,						kernel_size=7						,						stride=2						,						activation=config.hidden_act					)
										lowerCAmelCase  =				nn.MaxPoolad(kernel_size=3						,						stride=2						,						padding=1					)
										lowerCAmelCase  =				config.num_channels




						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Tensor					):
										lowerCAmelCase  =				pixel_values.shape[1]
										if num_channels != self.num_channels:
														raise ValueError(
														    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""					)
										lowerCAmelCase  =				self.embedder(lowerCAmelCase					)
										lowerCAmelCase  =				self.pooler(lowerCAmelCase					)
										return embedding






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 2					):
										super().__init__()
										lowerCAmelCase  =				nn.Convad(lowerCAmelCase						,						lowerCAmelCase						,						kernel_size=1						,						stride=lowerCAmelCase						,						bias=lowerCAmelCase					)
										lowerCAmelCase  =				nn.BatchNormad(lowerCAmelCase					)




						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Tensor					):
										lowerCAmelCase  =				self.convolution(lowerCAmelCase					)
										lowerCAmelCase  =				self.normalization(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			str = "relu"					):
										super().__init__()
										lowerCAmelCase  =				in_channels != out_channels or stride != 1
										lowerCAmelCase  =				(
										    ResNetShortCut(lowerCAmelCase						,						lowerCAmelCase						,						stride=lowerCAmelCase					) if should_apply_shortcut else nn.Identity()
										)
										lowerCAmelCase  =				nn.Sequential(
										    ResNetConvLayer(lowerCAmelCase						,						lowerCAmelCase						,						stride=lowerCAmelCase					)						,						ResNetConvLayer(lowerCAmelCase						,						lowerCAmelCase						,						activation=lowerCAmelCase					)						,						)
										lowerCAmelCase  =				ACTaFN[activation]




						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			Any					):
										lowerCAmelCase  =				hidden_state
										lowerCAmelCase  =				self.layer(lowerCAmelCase					)
										lowerCAmelCase  =				self.shortcut(lowerCAmelCase					)
										hidden_state += residual
										lowerCAmelCase  =				self.activation(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			str = "relu"						,						lowerCAmelCase				:			int = 4					):
										super().__init__()
										lowerCAmelCase  =				in_channels != out_channels or stride != 1
										lowerCAmelCase  =				out_channels // reduction
										lowerCAmelCase  =				(
										    ResNetShortCut(lowerCAmelCase						,						lowerCAmelCase						,						stride=lowerCAmelCase					) if should_apply_shortcut else nn.Identity()
										)
										lowerCAmelCase  =				nn.Sequential(
										    ResNetConvLayer(lowerCAmelCase						,						lowerCAmelCase						,						kernel_size=1					)						,						ResNetConvLayer(lowerCAmelCase						,						lowerCAmelCase						,						stride=lowerCAmelCase					)						,						ResNetConvLayer(lowerCAmelCase						,						lowerCAmelCase						,						kernel_size=1						,						activation=lowerCAmelCase					)						,						)
										lowerCAmelCase  =				ACTaFN[activation]




						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Optional[int]					):
										lowerCAmelCase  =				hidden_state
										lowerCAmelCase  =				self.layer(lowerCAmelCase					)
										lowerCAmelCase  =				self.shortcut(lowerCAmelCase					)
										hidden_state += residual
										lowerCAmelCase  =				self.activation(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			int						,						lowerCAmelCase				:			ResNetConfig						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						):
										super().__init__()

										lowerCAmelCase  =				ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

										lowerCAmelCase  =				nn.Sequential(
										    # downsampling is done in the first layer with stride of 2
										    layer(lowerCAmelCase						,						lowerCAmelCase						,						stride=lowerCAmelCase						,						activation=config.hidden_act					)						,						*[layer(lowerCAmelCase						,						lowerCAmelCase						,						activation=config.hidden_act					) for _ in range(depth - 1					)]						,						)




						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Tensor					):
										lowerCAmelCase  =				input
										for layer in self.layers:
														lowerCAmelCase  =				layer(lowerCAmelCase					)
										return hidden_state






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			ResNetConfig					):
										super().__init__()
										lowerCAmelCase  =				nn.ModuleList([]					)
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										self.stages.append(
										    ResNetStage(
										        lowerCAmelCase						,						config.embedding_size						,						config.hidden_sizes[0]						,						stride=2 if config.downsample_in_first_stage else 1						,						depth=config.depths[0]						,						)					)
										lowerCAmelCase  =				zip(config.hidden_sizes						,						config.hidden_sizes[1:]					)
										for (in_channels, out_channels), depth in zip(lowerCAmelCase						,						config.depths[1:]					):
														self.stages.append(ResNetStage(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						depth=lowerCAmelCase					)					)




						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			Tensor						,						lowerCAmelCase				:			bool = False						,						lowerCAmelCase				:			bool = True					):
										lowerCAmelCase  =				() if output_hidden_states else None

										for stage_module in self.stages:
														if output_hidden_states:
																		lowerCAmelCase  =				hidden_states + (hidden_state,)

														lowerCAmelCase  =				stage_module(lowerCAmelCase					)

										if output_hidden_states:
														lowerCAmelCase  =				hidden_states + (hidden_state,)

										if not return_dict:
														return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

										return BaseModelOutputWithNoAttention(
										    last_hidden_state=lowerCAmelCase						,						hidden_states=lowerCAmelCase						,						)






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						ResNetConfig
						_a    						=						'resnet'
						_a    						=						'pixel_values'
						_a    						=						True


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Optional[Any]					):
										if isinstance(lowerCAmelCase						,						nn.Convad					):
														nn.init.kaiming_normal_(module.weight						,						mode="""fan_out"""						,						nonlinearity="""relu"""					)
										elif isinstance(lowerCAmelCase						,						(nn.BatchNormad, nn.GroupNorm)					):
														nn.init.constant_(module.weight						,						1					)
														nn.init.constant_(module.bias						,						0					)




						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			List[str]=False					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					):
														lowerCAmelCase  =				value





a      =							R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

a      =							R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'						,				_a						,				)
class       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Any						,						lowerCAmelCase				:			Any					):
										super().__init__(lowerCAmelCase					)
										lowerCAmelCase  =				config
										lowerCAmelCase  =				ResNetEmbeddings(lowerCAmelCase					)
										lowerCAmelCase  =				ResNetEncoder(lowerCAmelCase					)
										lowerCAmelCase  =				nn.AdaptiveAvgPoolad((1, 1)					)
										# Initialize weights and apply final processing
										self.post_init()




						@add_start_docstrings_to_model_forward(lowerCAmelCase					)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC						,						output_type=lowerCAmelCase						,						config_class=_CONFIG_FOR_DOC						,						modality="""vision"""						,						expected_output=_EXPECTED_OUTPUT_SHAPE						,						)
						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Tensor						,						lowerCAmelCase				:			Optional[bool] = None						,						lowerCAmelCase				:			Optional[bool] = None					):
										lowerCAmelCase  =				(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										lowerCAmelCase  =				return_dict if return_dict is not None else self.config.use_return_dict

										lowerCAmelCase  =				self.embedder(lowerCAmelCase					)

										lowerCAmelCase  =				self.encoder(
										    lowerCAmelCase						,						output_hidden_states=lowerCAmelCase						,						return_dict=lowerCAmelCase					)

										lowerCAmelCase  =				encoder_outputs[0]

										lowerCAmelCase  =				self.pooler(lowerCAmelCase					)

										if not return_dict:
														return (last_hidden_state, pooled_output) + encoder_outputs[1:]

										return BaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=lowerCAmelCase						,						pooler_output=lowerCAmelCase						,						hidden_states=encoder_outputs.hidden_states						,						)






@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '						,				_a						,				)
class       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Optional[int]						,						lowerCAmelCase				:			Optional[int]					):
										super().__init__(lowerCAmelCase					)
										lowerCAmelCase  =				config.num_labels
										lowerCAmelCase  =				ResNetModel(lowerCAmelCase					)
										# classification head
										lowerCAmelCase  =				nn.Sequential(
										    nn.Flatten()						,						nn.Linear(config.hidden_sizes[-1]						,						config.num_labels					) if config.num_labels > 0 else nn.Identity()						,						)
										# initialize weights and apply final processing
										self.post_init()




						@add_start_docstrings_to_model_forward(lowerCAmelCase					)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT						,						output_type=lowerCAmelCase						,						config_class=_CONFIG_FOR_DOC						,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,						)
						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Optional[torch.FloatTensor] = None						,						lowerCAmelCase				:			Optional[torch.LongTensor] = None						,						lowerCAmelCase				:			Optional[bool] = None						,						lowerCAmelCase				:			Optional[bool] = None						,						):
										lowerCAmelCase  =				return_dict if return_dict is not None else self.config.use_return_dict

										lowerCAmelCase  =				self.resnet(lowerCAmelCase						,						output_hidden_states=lowerCAmelCase						,						return_dict=lowerCAmelCase					)

										lowerCAmelCase  =				outputs.pooler_output if return_dict else outputs[1]

										lowerCAmelCase  =				self.classifier(lowerCAmelCase					)

										lowerCAmelCase  =				None

										if labels is not None:
														if self.config.problem_type is None:
																		if self.num_labels == 1:
																						lowerCAmelCase  =				"""regression"""
																		elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																						lowerCAmelCase  =				"""single_label_classification"""
																		else:
																						lowerCAmelCase  =				"""multi_label_classification"""
														if self.config.problem_type == "regression":
																		lowerCAmelCase  =				MSELoss()
																		if self.num_labels == 1:
																						lowerCAmelCase  =				loss_fct(logits.squeeze()						,						labels.squeeze()					)
																		else:
																						lowerCAmelCase  =				loss_fct(lowerCAmelCase						,						lowerCAmelCase					)
														elif self.config.problem_type == "single_label_classification":
																		lowerCAmelCase  =				CrossEntropyLoss()
																		lowerCAmelCase  =				loss_fct(logits.view(-1						,						self.num_labels					)						,						labels.view(-1					)					)
														elif self.config.problem_type == "multi_label_classification":
																		lowerCAmelCase  =				BCEWithLogitsLoss()
																		lowerCAmelCase  =				loss_fct(lowerCAmelCase						,						lowerCAmelCase					)

										if not return_dict:
														lowerCAmelCase  =				(logits,) + outputs[2:]
														return (loss,) + output if loss is not None else output

										return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase						,						logits=lowerCAmelCase						,						hidden_states=outputs.hidden_states					)






@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '						,				_a						,				)
class       SCREAMING_SNAKE_CASE__					(					_a						,				_a					):


						def __init__(							self				:			Tuple						,						lowerCAmelCase				:			Dict					):
										super().__init__(lowerCAmelCase					)
										super()._init_backbone(lowerCAmelCase					)

										lowerCAmelCase  =				[config.embedding_size] + config.hidden_sizes
										lowerCAmelCase  =				ResNetEmbeddings(lowerCAmelCase					)
										lowerCAmelCase  =				ResNetEncoder(lowerCAmelCase					)

										# initialize weights and apply final processing
										self.post_init()




						@add_start_docstrings_to_model_forward(lowerCAmelCase					)
						@replace_return_docstrings(output_type=lowerCAmelCase						,						config_class=_CONFIG_FOR_DOC					)
						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Tensor						,						lowerCAmelCase				:			Optional[bool] = None						,						lowerCAmelCase				:			Optional[bool] = None					):
										lowerCAmelCase  =				return_dict if return_dict is not None else self.config.use_return_dict
										lowerCAmelCase  =				(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)

										lowerCAmelCase  =				self.embedder(lowerCAmelCase					)

										lowerCAmelCase  =				self.encoder(lowerCAmelCase						,						output_hidden_states=lowerCAmelCase						,						return_dict=lowerCAmelCase					)

										lowerCAmelCase  =				outputs.hidden_states

										lowerCAmelCase  =				()
										for idx, stage in enumerate(self.stage_names					):
														if stage in self.out_features:
																		feature_maps += (hidden_states[idx],)

										if not return_dict:
														lowerCAmelCase  =				(feature_maps,)
														if output_hidden_states:
																		output += (outputs.hidden_states,)
														return output

										return BackboneOutput(
										    feature_maps=lowerCAmelCase						,						hidden_states=outputs.hidden_states if output_hidden_states else None						,						attentions=lowerCAmelCase						,						)











"""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__)





