

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			:     List[str]										=      logging.get_logger(__name__)

# General docstring
__snake_case			:     List[Any]										=      "RegNetConfig"

# Base docstring
__snake_case			:     List[Any]										=      "facebook/regnet-y-040"
__snake_case			:     List[str]										=      [1, 1088, 7, 7]

# Image classification docstring
__snake_case			:     Tuple										=      "facebook/regnet-y-040"
__snake_case			:     List[str]										=      "tabby, tabby cat"

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



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__()
									A__					:       List[str]     =nn.Convad(
									    _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=_SCREAMING_SNAKE_CASE		,      stride=_SCREAMING_SNAKE_CASE		,      padding=kernel_size // 2		,      groups=_SCREAMING_SNAKE_CASE		,      bias=_SCREAMING_SNAKE_CASE		,      )
									A__					:       List[Any]     =nn.BatchNormad(_SCREAMING_SNAKE_CASE			)
									A__					:       List[Any]     =ACTaFN[activation] if activation is not None else nn.Identity()



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





									'''simple docstring'''
									A__					:       int     =self.convolution(_SCREAMING_SNAKE_CASE			)
									A__					:       Union[str, Any]     =self.normalization(_SCREAMING_SNAKE_CASE			)
									A__					:       List[str]     =self.activation(_SCREAMING_SNAKE_CASE			)
									return hidden_state



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__()
									A__					:       str     =RegNetConvLayer(
									    config.num_channels		,      config.embedding_size		,      kernel_size=3		,      stride=2		,      activation=config.hidden_act			)
									A__					:       Optional[int]     =config.num_channels



			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						Union[str, Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       int     =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__					:       str     =self.embedder(_SCREAMING_SNAKE_CASE			)
									return hidden_state



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






			def __init__(				self	:						str		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int = 2			)      ->		Tuple:





									'''simple docstring'''
									super().__init__()
									A__					:       Any     =nn.Convad(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=1		,      stride=_SCREAMING_SNAKE_CASE		,      bias=_SCREAMING_SNAKE_CASE			)
									A__					:       List[str]     =nn.BatchNormad(_SCREAMING_SNAKE_CASE			)



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





									'''simple docstring'''
									A__					:       List[str]     =self.convolution(_SCREAMING_SNAKE_CASE			)
									A__					:       Optional[Any]     =self.normalization(_SCREAMING_SNAKE_CASE			)
									return hidden_state



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__()

									A__					:       Dict     =nn.AdaptiveAvgPoolad((1, 1)			)
									A__					:       str     =nn.Sequential(
									    nn.Convad(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=1			)		,      nn.ReLU()		,      nn.Convad(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=1			)		,      nn.Sigmoid()		,      )



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





									'''simple docstring'''
									A__					:       Dict     =self.pooler(_SCREAMING_SNAKE_CASE			)
									A__					:       List[str]     =self.attention(_SCREAMING_SNAKE_CASE			)
									A__					:       List[str]     =hidden_state * attention
									return hidden_state



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__()
									A__					:       Tuple     =in_channels != out_channels or stride != 1
									A__					:       int     =max(1		,      out_channels // config.groups_width			)
									A__					:       Optional[int]     =(
									    RegNetShortCut(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      stride=_SCREAMING_SNAKE_CASE			) if should_apply_shortcut else nn.Identity()
									)
									A__					:       str     =nn.Sequential(
									    RegNetConvLayer(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=1		,      activation=config.hidden_act			)		,      RegNetConvLayer(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      stride=_SCREAMING_SNAKE_CASE		,      groups=_SCREAMING_SNAKE_CASE		,      activation=config.hidden_act			)		,      RegNetConvLayer(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=1		,      activation=_SCREAMING_SNAKE_CASE			)		,      )
									A__					:       Tuple     =ACTaFN[config.hidden_act]



			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						List[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Tuple     =hidden_state
									A__					:       List[Any]     =self.layer(_SCREAMING_SNAKE_CASE			)
									A__					:       Union[str, Any]     =self.shortcut(_SCREAMING_SNAKE_CASE			)
									hidden_state += residual
									A__					:       Tuple     =self.activation(_SCREAMING_SNAKE_CASE			)
									return hidden_state



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__()
									A__					:       Optional[Any]     =in_channels != out_channels or stride != 1
									A__					:       str     =max(1		,      out_channels // config.groups_width			)
									A__					:       Any     =(
									    RegNetShortCut(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      stride=_SCREAMING_SNAKE_CASE			) if should_apply_shortcut else nn.Identity()
									)
									A__					:       List[str]     =nn.Sequential(
									    RegNetConvLayer(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=1		,      activation=config.hidden_act			)		,      RegNetConvLayer(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      stride=_SCREAMING_SNAKE_CASE		,      groups=_SCREAMING_SNAKE_CASE		,      activation=config.hidden_act			)		,      RegNetSELayer(_SCREAMING_SNAKE_CASE		,      reduced_channels=int(round(in_channels / 4			)			)			)		,      RegNetConvLayer(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      kernel_size=1		,      activation=_SCREAMING_SNAKE_CASE			)		,      )
									A__					:       Union[str, Any]     =ACTaFN[config.hidden_act]



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





									'''simple docstring'''
									A__					:       Union[str, Any]     =hidden_state
									A__					:       Tuple     =self.layer(_SCREAMING_SNAKE_CASE			)
									A__					:       Dict     =self.shortcut(_SCREAMING_SNAKE_CASE			)
									hidden_state += residual
									A__					:       Tuple     =self.activation(_SCREAMING_SNAKE_CASE			)
									return hidden_state



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__()

									A__					:       Optional[int]     =RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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



			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       int     =self.layers(_SCREAMING_SNAKE_CASE			)
									return hidden_state



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






			def __init__(				self	:						Tuple		,      lowerCAmelCase_	:						Union[str, Any]			)      ->		int:





									'''simple docstring'''
									super().__init__()
									A__					:       Optional[int]     =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(
									        _SCREAMING_SNAKE_CASE		,      config.embedding_size		,      config.hidden_sizes[0]		,      stride=2 if config.downsample_in_first_stage else 1		,      depth=config.depths[0]		,      )			)
									A__					:       Tuple     =zip(config.hidden_sizes		,      config.hidden_sizes[1:]			)
									for (in_channels, out_channels), depth in zip(_SCREAMING_SNAKE_CASE		,      config.depths[1:]			):
															self.stages.append(RegNetStage(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      depth=_SCREAMING_SNAKE_CASE			)			)



			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						Any = False		,      lowerCAmelCase_	:						Union[str, Any] = True			)      ->		BaseModelOutputWithNoAttention:





									'''simple docstring'''
									A__					:       List[Any]     =() if output_hidden_states else None

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

															A__					:       str     =stage_module(_SCREAMING_SNAKE_CASE			)

									if output_hidden_states:
															A__					:       Union[str, Any]     =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=_SCREAMING_SNAKE_CASE		,      hidden_states=_SCREAMING_SNAKE_CASE			)



class lowerCamelCase			(  UpperCAmelCase__						):



			'''simple docstring'''






			__snake_case        =	RegNetConfig
			__snake_case        =	'regnet'
			__snake_case        =	'pixel_values'
			__snake_case        =	True


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Tuple			)      ->		Optional[Any]:





									'''simple docstring'''




									if isinstance(_SCREAMING_SNAKE_CASE		,      nn.Convad			):
															nn.init.kaiming_normal_(module.weight		,      mode="""fan_out"""		,      nonlinearity="""relu"""			)
									elif isinstance(_SCREAMING_SNAKE_CASE		,      (nn.BatchNormad, nn.GroupNorm)			):
															nn.init.constant_(module.weight		,      1			)
															nn.init.constant_(module.bias		,      0			)



			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						Union[str, Any]=False			)      ->		List[str]:





									'''simple docstring'''




									if isinstance(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE			):
															A__					:       int     =value


__snake_case			:     List[Any]										=      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			:     Dict										=      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.'	,						UpperCAmelCase__	,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class lowerCamelCase			(  UpperCAmelCase__						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__(_SCREAMING_SNAKE_CASE			)
									A__					:       Optional[int]     =config
									A__					:       Tuple     =RegNetEmbeddings(_SCREAMING_SNAKE_CASE			)
									A__					:       Dict     =RegNetEncoder(_SCREAMING_SNAKE_CASE			)
									A__					:       Optional[int]     =nn.AdaptiveAvgPoolad((1, 1)			)
									# Initialize weights and apply final processing
									self.post_init()



			@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE			)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC		,      output_type=_SCREAMING_SNAKE_CASE		,      config_class=_CONFIG_FOR_DOC		,      modality="""vision"""		,      expected_output=_EXPECTED_OUTPUT_SHAPE		,      )
			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						Optional[Any]		,      lowerCAmelCase_	:						Tuple = None		,      lowerCAmelCase_	:						List[Any] = None			)      ->		BaseModelOutputWithPoolingAndNoAttention:





									'''simple docstring'''
									A__					:       str     =(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									A__					:       List[Any]     =return_dict if return_dict is not None else self.config.use_return_dict

									A__					:       Optional[int]     =self.embedder(_SCREAMING_SNAKE_CASE			)

									A__					:       Dict     =self.encoder(
									    _SCREAMING_SNAKE_CASE		,      output_hidden_states=_SCREAMING_SNAKE_CASE		,      return_dict=_SCREAMING_SNAKE_CASE			)

									A__					:       Any     =encoder_outputs[0]

									A__					:       Dict     =self.pooler(_SCREAMING_SNAKE_CASE			)

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

									return BaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=_SCREAMING_SNAKE_CASE		,      pooler_output=_SCREAMING_SNAKE_CASE		,      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    '	,						UpperCAmelCase__	,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class lowerCamelCase			(  UpperCAmelCase__						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__(_SCREAMING_SNAKE_CASE			)
									A__					:       str     =config.num_labels
									A__					:       List[Any]     =RegNetModel(_SCREAMING_SNAKE_CASE			)
									# classification head
									A__					:       Optional[Any]     =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(_SCREAMING_SNAKE_CASE			)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT		,      output_type=_SCREAMING_SNAKE_CASE		,      config_class=_CONFIG_FOR_DOC		,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,      )
			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						Dict = None		,      lowerCAmelCase_	:						List[Any] = None		,      lowerCAmelCase_	:						Union[str, Any] = None		,      lowerCAmelCase_	:						Optional[Any] = None		,      )      ->		ImageClassifierOutputWithNoAttention:





									'''simple docstring'''
									A__					:       Any     =return_dict if return_dict is not None else self.config.use_return_dict

									A__					:       str     =self.regnet(_SCREAMING_SNAKE_CASE		,      output_hidden_states=_SCREAMING_SNAKE_CASE		,      return_dict=_SCREAMING_SNAKE_CASE			)

									A__					:       Dict     =outputs.pooler_output if return_dict else outputs[1]

									A__					:       str     =self.classifier(_SCREAMING_SNAKE_CASE			)

									A__					:       Any     =None

									if labels is not None:
															if self.config.problem_type is None:
																					if self.num_labels == 1:
																											A__					:       Any     ="""regression"""
																					elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																											A__					:       List[Any]     ="""single_label_classification"""
																					else:
																											A__					:       Any     ="""multi_label_classification"""
															if self.config.problem_type == "regression":
																					A__					:       Optional[int]     =MSELoss()
																					if self.num_labels == 1:
																											A__					:       Optional[Any]     =loss_fct(logits.squeeze()		,      labels.squeeze()			)
																					else:
																											A__					:       Union[str, Any]     =loss_fct(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE			)
															elif self.config.problem_type == "single_label_classification":
																					A__					:       Any     =CrossEntropyLoss()
																					A__					:       Optional[int]     =loss_fct(logits.view(-1		,      self.num_labels			)		,      labels.view(-1			)			)
															elif self.config.problem_type == "multi_label_classification":
																					A__					:       Union[str, Any]     =BCEWithLogitsLoss()
																					A__					:       Optional[Any]     =loss_fct(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE			)

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

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




'''simple docstring'''



from typing import TYPE_CHECKING

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


__snake_case			:     Any										=      {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     Optional[int]										=      [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     List[Any]										=      ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

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

