




'''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


_snake_case				:				Optional[int]       							=		logging.get_logger(__name__)

# General docstring
_snake_case				:				Tuple       							=		'RegNetConfig'

# Base docstring
_snake_case				:				Union[str, Any]       							=		'facebook/regnet-y-040'
_snake_case				:				int       							=		[1, 1088, 7, 7]

# Image classification docstring
_snake_case				:				int       							=		'facebook/regnet-y-040'
_snake_case				:				Tuple       							=		'tabby, tabby cat'

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

class       A   (		nn.Module   ):







							def __init__(  self	:  str							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int = 3							,      lowerCAmelCase_	:  int = 1							,      lowerCAmelCase_	:  int = 1							,      lowerCAmelCase_	:  Optional[str] = "relu"							,      )      ->							Union[str, Any]:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  Tuple )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    self.convolution(lowerCAmelCase_ )
														_a     					=    self.normalization(lowerCAmelCase_ )
														_a     					=    self.activation(lowerCAmelCase_ )
														return hidden_state



class       A   (		nn.Module   ):







							def __init__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  RegNetConfig )      ->							str:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Dict )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    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.''' )
														_a     					=    self.embedder(lowerCAmelCase_ )
														return hidden_state



class       A   (		nn.Module   ):







							def __init__(  self	:  List[Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int = 2 )      ->							Tuple:


														"""simple docstring"""


														super().__init__()
														_a     					=    nn.Convad(lowerCAmelCase_							,      lowerCAmelCase_							,      kernel_size=1							,      stride=lowerCAmelCase_							,      bias=lowerCAmelCase_ )
														_a     					=    nn.BatchNormad(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Tensor )      ->							Tensor:


														"""simple docstring"""


														_a     					=    self.convolution(lowerCAmelCase_ )
														_a     					=    self.normalization(lowerCAmelCase_ )
														return hidden_state



class       A   (		nn.Module   ):







							def __init__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							Any:


														"""simple docstring"""


														super().__init__()

														_a     					=    nn.AdaptiveAvgPoolad((1, 1) )
														_a     					=    nn.Sequential(
														    nn.Convad(lowerCAmelCase_							,      lowerCAmelCase_							,      kernel_size=1 )							,      nn.ReLU()							,      nn.Convad(lowerCAmelCase_							,      lowerCAmelCase_							,      kernel_size=1 )							,      nn.Sigmoid()							,      )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  List[str] )      ->							str:


														"""simple docstring"""


														_a     					=    self.pooler(lowerCAmelCase_ )
														_a     					=    self.attention(lowerCAmelCase_ )
														_a     					=    hidden_state * attention
														return hidden_state



class       A   (		nn.Module   ):







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  RegNetConfig							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int = 1 )      ->							Any:


														"""simple docstring"""


														super().__init__()
														_a     					=    in_channels != out_channels or stride != 1
														_a     					=    max(1							,      out_channels // config.groups_width )
														_a     					=    (
														    RegNetShortCut(lowerCAmelCase_							,      lowerCAmelCase_							,      stride=lowerCAmelCase_ ) if should_apply_shortcut else nn.Identity()
														)
														_a     					=    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_ )							,      )
														_a     					=    ACTaFN[config.hidden_act]







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  Any )      ->							Dict:


														"""simple docstring"""


														_a     					=    hidden_state
														_a     					=    self.layer(lowerCAmelCase_ )
														_a     					=    self.shortcut(lowerCAmelCase_ )
														hidden_state += residual
														_a     					=    self.activation(lowerCAmelCase_ )
														return hidden_state



class       A   (		nn.Module   ):







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  RegNetConfig							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int = 1 )      ->							List[str]:


														"""simple docstring"""


														super().__init__()
														_a     					=    in_channels != out_channels or stride != 1
														_a     					=    max(1							,      out_channels // config.groups_width )
														_a     					=    (
														    RegNetShortCut(lowerCAmelCase_							,      lowerCAmelCase_							,      stride=lowerCAmelCase_ ) if should_apply_shortcut else nn.Identity()
														)
														_a     					=    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_ )							,      )
														_a     					=    ACTaFN[config.hidden_act]







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  Dict )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    hidden_state
														_a     					=    self.layer(lowerCAmelCase_ )
														_a     					=    self.shortcut(lowerCAmelCase_ )
														hidden_state += residual
														_a     					=    self.activation(lowerCAmelCase_ )
														return hidden_state



class       A   (		nn.Module   ):







							def __init__(  self	:  Any							,      lowerCAmelCase_	:  RegNetConfig							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int = 2							,      lowerCAmelCase_	:  int = 2							,      )      ->							List[Any]:


														"""simple docstring"""


														super().__init__()

														_a     					=    RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

														_a     					=    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        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    self.layers(lowerCAmelCase_ )
														return hidden_state



class       A   (		nn.Module   ):







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  RegNetConfig )      ->							Tuple:


														"""simple docstring"""


														super().__init__()
														_a     					=    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]							,      ) )
														_a     					=    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        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Tensor							,      lowerCAmelCase_	:  bool = False							,      lowerCAmelCase_	:  bool = True )      ->							BaseModelOutputWithNoAttention:


														"""simple docstring"""


														_a     					=    () if output_hidden_states else None

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

																					_a     					=    stage_module(lowerCAmelCase_ )

														if output_hidden_states:
																					_a     					=    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   (		_a   ):
							lowercase_          =    RegNetConfig
							lowercase_          =    'regnet'
							lowercase_          =    'pixel_values'
							lowercase_          =    True







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  int )      ->							Optional[Any]:


														"""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        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Tuple=False )      ->							Optional[int]:


														"""simple docstring"""


														if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ):
																					_a     					=    value



_snake_case				:				Tuple       							=		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 ([`RegNetConfig`]): 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'

_snake_case				:				Optional[Any]       							=		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 [`~file_utils.ModelOutput`] instead of a plain tuple.\n'

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







							def __init__(  self	:  List[Any]							,      lowerCAmelCase_	:  Tuple )      ->							List[Any]:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_ )
														_a     					=    config
														_a     					=    RegNetEmbeddings(lowerCAmelCase_ )
														_a     					=    RegNetEncoder(lowerCAmelCase_ )
														_a     					=    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        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  Tensor							,      lowerCAmelCase_	:  Optional[bool] = None							,      lowerCAmelCase_	:  Optional[bool] = None )      ->							BaseModelOutputWithPoolingAndNoAttention:


														"""simple docstring"""


														_a     					=    (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														_a     					=    return_dict if return_dict is not None else self.config.use_return_dict

														_a     					=    self.embedder(lowerCAmelCase_ )

														_a     					=    self.encoder(
														    lowerCAmelCase_							,      output_hidden_states=lowerCAmelCase_							,      return_dict=lowerCAmelCase_ )

														_a     					=    encoder_outputs[0]

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







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  List[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_ )
														_a     					=    config.num_labels
														_a     					=    RegNetModel(lowerCAmelCase_ )
														# classification head
														_a     					=    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        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Optional[torch.FloatTensor] = None							,      lowerCAmelCase_	:  Optional[torch.LongTensor] = None							,      lowerCAmelCase_	:  Optional[bool] = None							,      lowerCAmelCase_	:  Optional[bool] = None							,      )      ->							ImageClassifierOutputWithNoAttention:


														"""simple docstring"""


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

														_a     					=    self.regnet(lowerCAmelCase_							,      output_hidden_states=lowerCAmelCase_							,      return_dict=lowerCAmelCase_ )

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

														_a     					=    self.classifier(lowerCAmelCase_ )

														_a     					=    None

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

														if not return_dict:
																					_a     					=    (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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:				List[str]       							=		{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[Any]       							=		['XLNetTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				int       							=		['XLNetTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		[
				    'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'XLNetForMultipleChoice',
				    'XLNetForQuestionAnswering',
				    'XLNetForQuestionAnsweringSimple',
				    'XLNetForSequenceClassification',
				    'XLNetForTokenClassification',
				    'XLNetLMHeadModel',
				    'XLNetModel',
				    'XLNetPreTrainedModel',
				    'load_tf_weights_in_xlnet',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFXLNetForMultipleChoice',
				    'TFXLNetForQuestionAnsweringSimple',
				    'TFXLNetForSequenceClassification',
				    'TFXLNetForTokenClassification',
				    'TFXLNetLMHeadModel',
				    'TFXLNetMainLayer',
				    'TFXLNetModel',
				    'TFXLNetPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet import XLNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet_fast import XLNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlnet import (
								    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLNetForMultipleChoice,
								    XLNetForQuestionAnswering,
								    XLNetForQuestionAnsweringSimple,
								    XLNetForSequenceClassification,
								    XLNetForTokenClassification,
								    XLNetLMHeadModel,
								    XLNetModel,
								    XLNetPreTrainedModel,
								    load_tf_weights_in_xlnet,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlnet import (
								    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLNetForMultipleChoice,
								    TFXLNetForQuestionAnsweringSimple,
								    TFXLNetForSequenceClassification,
								    TFXLNetForTokenClassification,
								    TFXLNetLMHeadModel,
								    TFXLNetMainLayer,
								    TFXLNetModel,
								    TFXLNetPreTrainedModel,
								)

else:
				import sys

				_snake_case				:				Union[str, Any]       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

