





import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE_						       =	"""examples/"""
SCREAMING_SNAKE_CASE_						       =	{
    """examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), r"""\1version=\"VERSION\","""),
    """doc""": (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
SCREAMING_SNAKE_CASE_						       =	{
    """init""": """src/diffusers/__init__.py""",
    """setup""": """setup.py""",
}
SCREAMING_SNAKE_CASE_						       =	"""README.md"""



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      with open(__lowerCamelCase			,						"""r"""			,						encoding="""utf-8"""			,						newline="""\n"""     ) as f:
            SCREAMING_SNAKE_CASE		=						f.read()
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						REPLACE_PATTERNS[pattern]
      SCREAMING_SNAKE_CASE		=						replace.replace("""VERSION"""			,						__lowerCamelCase     )
      SCREAMING_SNAKE_CASE		=						re_pattern.sub(__lowerCamelCase			,						__lowerCamelCase     )
      with open(__lowerCamelCase			,						"""w"""			,						encoding="""utf-8"""			,						newline="""\n"""     ) as f:
            f.write(__lowerCamelCase     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


      for folder, directories, fnames in os.walk(__lowerCamelCase     ):
            # Removing some of the folders with non-actively maintained examples from the walk
            if "research_projects" in directories:
                  directories.remove("""research_projects"""     )
            if "legacy" in directories:
                  directories.remove("""legacy"""     )
            for fname in fnames:
                  if fname.endswith(""".py"""     ):
                        update_version_in_file(os.path.join(__lowerCamelCase			,						__lowerCamelCase     )			,						__lowerCamelCase			,						pattern="""examples"""     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=False     )			->       Dict:


      '''simple docstring'''


      for pattern, fname in REPLACE_FILES.items():
            update_version_in_file(__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase     )
      if not patch:
            update_version_in_examples(__lowerCamelCase     )



def        __lowercase       (				)			->       Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""🤗 Transformers currently provides the following architectures"""
      SCREAMING_SNAKE_CASE		=						"""1. Want to contribute a new model?"""
      with open(__lowerCamelCase			,						"""r"""			,						encoding="""utf-8"""			,						newline="""\n"""     ) as f:
            SCREAMING_SNAKE_CASE		=						f.readlines()

      # Find the start of the list.
      SCREAMING_SNAKE_CASE		=						0
      while not lines[start_index].startswith(_start_prompt     ):
            start_index += 1
      start_index += 1

      SCREAMING_SNAKE_CASE		=						start_index
      # Update the lines in the model list.
      while not lines[index].startswith(_end_prompt     ):
            if lines[index].startswith("""1."""     ):
                  SCREAMING_SNAKE_CASE		=						lines[index].replace(
                      """https://huggingface.co/docs/diffusers/main/model_doc"""			,						"""https://huggingface.co/docs/diffusers/model_doc"""			,						)
            index += 1

      with open(__lowerCamelCase			,						"""w"""			,						encoding="""utf-8"""			,						newline="""\n"""     ) as f:
            f.writelines(__lowerCamelCase     )



def        __lowercase       (				)			->       Optional[int]:


      '''simple docstring'''


      with open(REPLACE_FILES["""init"""]			,						"""r"""     ) as f:
            SCREAMING_SNAKE_CASE		=						f.read()
      SCREAMING_SNAKE_CASE		=						REPLACE_PATTERNS["""init"""][0].search(__lowerCamelCase     ).groups()[0]
      return packaging.version.parse(__lowerCamelCase     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE=False     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						get_version()
      if patch and default_version.is_devrelease:
            raise ValueError("""Can\'t create a patch version from the dev branch, checkout a released version!"""     )
      if default_version.is_devrelease:
            SCREAMING_SNAKE_CASE		=						default_version.base_version
      elif patch:
            SCREAMING_SNAKE_CASE		=						F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
      else:
            SCREAMING_SNAKE_CASE		=						F"""{default_version.major}.{default_version.minor + 1}.0"""

      # Now let's ask nicely if that's the right one.
      SCREAMING_SNAKE_CASE		=						input(F"""Which version are you releasing? [{default_version}]"""     )
      if len(__lowerCamelCase     ) == 0:
            SCREAMING_SNAKE_CASE		=						default_version

      print(F"""Updating version to {version}."""     )
      global_version_update(__lowerCamelCase			,						patch=__lowerCamelCase     )



def        __lowercase       (				)			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						get_version()
      SCREAMING_SNAKE_CASE		=						F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
      SCREAMING_SNAKE_CASE		=						current_version.base_version

      # Check with the user we got that right.
      SCREAMING_SNAKE_CASE		=						input(F"""Which version are we developing now? [{dev_version}]"""     )
      if len(__lowerCamelCase     ) == 0:
            SCREAMING_SNAKE_CASE		=						dev_version

      print(F"""Updating version to {version}."""     )
      global_version_update(__lowerCamelCase     )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_						       =	argparse.ArgumentParser()
    parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
    parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
    SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
    if not args.post_release:
        pre_release_work(patch=args.patch)
    elif args.patch:
        print("""Nothing to do after a patch :-)""")
    else:
        post_release_work()









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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_						       =	"""RegNetConfig"""

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

# Image classification docstring
SCREAMING_SNAKE_CASE_						       =	"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE_						       =	"""tabby, tabby cat"""

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

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     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__()
           SCREAMING_SNAKE_CASE		=						nn.Convad(
               lowerCamelCase__						,lowerCamelCase__						,kernel_size=lowerCamelCase__						,stride=lowerCamelCase__						,padding=kernel_size // 2						,groups=lowerCamelCase__						,bias=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						nn.BatchNormad(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						ACTaFN[activation] if activation is not None else nn.Identity()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   Tuple	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.convolution(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.normalization(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

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




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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."""	)
           SCREAMING_SNAKE_CASE		=						self.embedder(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1						,stride=lowerCamelCase__						,bias=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						nn.BatchNormad(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Tensor	)					->		Tensor:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.convolution(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.normalization(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE		=						nn.AdaptiveAvgPoolad((1, 1)	)
           SCREAMING_SNAKE_CASE		=						nn.Sequential(
               nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1	)						,nn.ReLU()						,nn.Convad(lowerCamelCase__						,lowerCamelCase__						,kernel_size=1	)						,nn.Sigmoid()						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Any	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.pooler(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.attention(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						hidden_state * attention
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

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




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Optional[Any]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						hidden_state
           SCREAMING_SNAKE_CASE		=						self.layer(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.shortcut(lowerCamelCase__	)
           hidden_state += residual
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   Dict						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 1	)					->		Optional[int]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Tuple	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						hidden_state
           SCREAMING_SNAKE_CASE		=						self.layer(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.shortcut(lowerCamelCase__	)
           hidden_state += residual
           SCREAMING_SNAKE_CASE		=						self.activation(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   RegNetConfig						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int = 2						,lowerCamelCase__						:   int = 2						,)					->		Tuple:




           '''simple docstring'''


           super().__init__()

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

           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Union[str, Any]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.layers(lowerCamelCase__	)
           return hidden_state

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						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]						,)	)
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Tensor						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   bool = True	)					->		BaseModelOutputWithNoAttention:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						() if output_hidden_states else None

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

                 SCREAMING_SNAKE_CASE		=						stage_module(lowerCamelCase__	)

           if output_hidden_states:
                 SCREAMING_SNAKE_CASE		=						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 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     List[Any]		     =      RegNetConfig
     __snake_case   :     Union[str, Any]		     =      "regnet"
     __snake_case   :     Optional[Any]		     =      "pixel_values"
     __snake_case   :     List[Any]		     =      True






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   int	)					->		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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   str=False	)					->		str:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,lowerCamelCase__	):
                 SCREAMING_SNAKE_CASE		=						value
SCREAMING_SNAKE_CASE_						       =	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.
"""

SCREAMING_SNAKE_CASE_						       =	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 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   str						,lowerCamelCase__						:   str	)					->		Any:




           '''simple docstring'''


           super().__init__(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						config
           SCREAMING_SNAKE_CASE		=						RegNetEmbeddings(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RegNetEncoder(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Tensor						,lowerCamelCase__						:   Optional[bool] = None						,lowerCamelCase__						:   Optional[bool] = None	)					->		BaseModelOutputWithPoolingAndNoAttention:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						self.embedder(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.encoder(
               lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,return_dict=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						encoder_outputs[0]

           SCREAMING_SNAKE_CASE		=						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    "							,				lowerCAmelCase_							,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						config.num_labels
           SCREAMING_SNAKE_CASE		=						RegNetModel(lowerCamelCase__	)
           # classification head
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Optional[torch.FloatTensor] = None						,lowerCamelCase__						:   Optional[torch.LongTensor] = None						,lowerCamelCase__						:   Optional[bool] = None						,lowerCamelCase__						:   Optional[bool] = None						,)					->		ImageClassifierOutputWithNoAttention:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						self.regnet(lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,return_dict=lowerCamelCase__	)

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

           SCREAMING_SNAKE_CASE		=						self.classifier(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						None

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

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

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


