'''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 ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowercase          =  logging.get_logger(__name__)

# General docstring
__lowercase          =  '''RegNetConfig'''

# Base docstring
__lowercase          =  '''facebook/regnet-y-040'''
__lowercase          =  [1, 1_0_8_8, 7, 7]

# Image classification docstring
__lowercase          =  '''facebook/regnet-y-040'''
__lowercase          =  '''tabby, tabby cat'''

__lowercase          =  [
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 3       ,    __lowerCAmelCase = 1       ,    __lowerCAmelCase = 1       ,    __lowerCAmelCase = "relu"       ,    ):




					"""simple docstring"""


					super().__init__()
					lowerCAmelCase   					=  nn.Convad(
					    __lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=__lowerCAmelCase       ,    stride=__lowerCAmelCase       ,    padding=kernel_size // 2       ,    groups=__lowerCAmelCase       ,    bias=__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  nn.BatchNormad(__lowerCAmelCase)
					lowerCAmelCase   					=  ACTaFN[activation] if activation is not None else nn.Identity()







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.convolution(__lowerCAmelCase)
					lowerCAmelCase   					=  self.normalization(__lowerCAmelCase)
					lowerCAmelCase   					=  self.activation(__lowerCAmelCase)
					return hidden_state







class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__()
					lowerCAmelCase   					=  RegNetConvLayer(
					    config.num_channels       ,    config.embedding_size       ,    kernel_size=3       ,    stride=2       ,    activation=config.hidden_act)
					lowerCAmelCase   					=  config.num_channels







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					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)
					return hidden_state







class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 2):




					"""simple docstring"""


					super().__init__()
					lowerCAmelCase   					=  nn.Convad(__lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=1       ,    stride=__lowerCAmelCase       ,    bias=__lowerCAmelCase)
					lowerCAmelCase   					=  nn.BatchNormad(__lowerCAmelCase)







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.convolution(__lowerCAmelCase)
					lowerCAmelCase   					=  self.normalization(__lowerCAmelCase)
					return hidden_state







class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__()

					lowerCAmelCase   					=  nn.AdaptiveAvgPoolad((1, 1))
					lowerCAmelCase   					=  nn.Sequential(
					    nn.Convad(__lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=1)       ,    nn.ReLU()       ,    nn.Convad(__lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=1)       ,    nn.Sigmoid()       ,    )







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.pooler(__lowerCAmelCase)
					lowerCAmelCase   					=  self.attention(__lowerCAmelCase)
					lowerCAmelCase   					=  hidden_state * attention
					return hidden_state







class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 1):




					"""simple docstring"""


					super().__init__()
					lowerCAmelCase   					=  in_channels != out_channels or stride != 1
					lowerCAmelCase   					=  max(1       ,    out_channels // config.groups_width)
					lowerCAmelCase   					=  (
					    RegNetShortCut(__lowerCAmelCase       ,    __lowerCAmelCase       ,    stride=__lowerCAmelCase) if should_apply_shortcut else nn.Identity()
					)
					lowerCAmelCase   					=  nn.Sequential(
					    RegNetConvLayer(__lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=1       ,    activation=config.hidden_act)       ,    RegNetConvLayer(__lowerCAmelCase       ,    __lowerCAmelCase       ,    stride=__lowerCAmelCase       ,    groups=__lowerCAmelCase       ,    activation=config.hidden_act)       ,    RegNetConvLayer(__lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=1       ,    activation=__lowerCAmelCase)       ,    )
					lowerCAmelCase   					=  ACTaFN[config.hidden_act]







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  hidden_state
					lowerCAmelCase   					=  self.layer(__lowerCAmelCase)
					lowerCAmelCase   					=  self.shortcut(__lowerCAmelCase)
					hidden_state += residual
					lowerCAmelCase   					=  self.activation(__lowerCAmelCase)
					return hidden_state







class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 1):




					"""simple docstring"""


					super().__init__()
					lowerCAmelCase   					=  in_channels != out_channels or stride != 1
					lowerCAmelCase   					=  max(1       ,    out_channels // config.groups_width)
					lowerCAmelCase   					=  (
					    RegNetShortCut(__lowerCAmelCase       ,    __lowerCAmelCase       ,    stride=__lowerCAmelCase) if should_apply_shortcut else nn.Identity()
					)
					lowerCAmelCase   					=  nn.Sequential(
					    RegNetConvLayer(__lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=1       ,    activation=config.hidden_act)       ,    RegNetConvLayer(__lowerCAmelCase       ,    __lowerCAmelCase       ,    stride=__lowerCAmelCase       ,    groups=__lowerCAmelCase       ,    activation=config.hidden_act)       ,    RegNetSELayer(__lowerCAmelCase       ,    reduced_channels=int(round(in_channels / 4)))       ,    RegNetConvLayer(__lowerCAmelCase       ,    __lowerCAmelCase       ,    kernel_size=1       ,    activation=__lowerCAmelCase)       ,    )
					lowerCAmelCase   					=  ACTaFN[config.hidden_act]







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  hidden_state
					lowerCAmelCase   					=  self.layer(__lowerCAmelCase)
					lowerCAmelCase   					=  self.shortcut(__lowerCAmelCase)
					hidden_state += residual
					lowerCAmelCase   					=  self.activation(__lowerCAmelCase)
					return hidden_state







class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 2       ,    __lowerCAmelCase = 2       ,    ):




					"""simple docstring"""


					super().__init__()

					lowerCAmelCase   					=  RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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







			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.layers(__lowerCAmelCase)
					return hidden_state







class 				a__(					nn.Module	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					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(
					    RegNetStage(
					        __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(RegNetStage(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    depth=__lowerCAmelCase))







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = True):




					"""simple docstring"""


					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 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Optional[int]      =       RegNetConfig
			UpperCAmelCase_			:     Optional[int]      =       '''regnet'''
			UpperCAmelCase_			:     int      =       '''pixel_values'''
			UpperCAmelCase_			:     Union[str, Any]      =       True



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=False):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  value







__lowercase          =  R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__lowercase          =  R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''






@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.''' ,						lowerCAmelCase__ ,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase)
					lowerCAmelCase   					=  config
					lowerCAmelCase   					=  RegNetEmbeddings(__lowerCAmelCase)
					lowerCAmelCase   					=  RegNetEncoder(__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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					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(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    ''' ,						lowerCAmelCase__ ,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase)
					lowerCAmelCase   					=  config.num_labels
					lowerCAmelCase   					=  RegNetModel(__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 							a_       (   self       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  return_dict if return_dict is not None else self.config.use_return_dict

					lowerCAmelCase   					=  self.regnet(__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)





'''simple docstring'''




from typing import TYPE_CHECKING

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


__lowercase          =  {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ResNetForImageClassification''',
						    '''ResNetModel''',
						    '''ResNetPreTrainedModel''',
						    '''ResNetBackbone''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFResNetForImageClassification''',
						    '''TFResNetModel''',
						    '''TFResNetPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FlaxResNetForImageClassification''',
						    '''FlaxResNetModel''',
						    '''FlaxResNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_resnet import (
												    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ResNetBackbone,
												    ResNetForImageClassification,
												    ResNetModel,
												    ResNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_resnet import (
												    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFResNetForImageClassification,
												    TFResNetModel,
												    TFResNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure)




