import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def     __lowerCAmelCase						(       a_		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       384
     SCREAMING_SNAKE_CASE					:			int    	=       7
     if "tiny" in model_name:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       96
          SCREAMING_SNAKE_CASE					:			int    	=       (2, 2, 6, 2)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (3, 6, 12, 24)
     elif "small" in model_name:
          SCREAMING_SNAKE_CASE					:			str    	=       96
          SCREAMING_SNAKE_CASE					:			List[str]    	=       (2, 2, 18, 2)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       (3, 6, 12, 24)
     elif "base" in model_name:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       128
          SCREAMING_SNAKE_CASE					:			Tuple    	=       (2, 2, 18, 2)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       (4, 8, 16, 32)
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       12
          SCREAMING_SNAKE_CASE					:			int    	=       512
     elif "large" in model_name:
          SCREAMING_SNAKE_CASE					:			Any    	=       192
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (2, 2, 18, 2)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       (6, 12, 24, 48)
          SCREAMING_SNAKE_CASE					:			Any    	=       12
          SCREAMING_SNAKE_CASE					:			str    	=       768

     # set label information
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       150
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       'huggingface/label-files'
     SCREAMING_SNAKE_CASE					:			List[str]    	=       'ade20k-id2label.json'
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       json.load(open(hf_hub_download(a_       ,      a_       ,      repo_type='dataset'		)       ,      'r'		)		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {int(a_		): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE					:			List[str]    	=       {v: k for k, v in idalabel.items()}

     SCREAMING_SNAKE_CASE					:			List[str]    	=       SwinConfig(
         embed_dim=a_       ,      depths=a_       ,      num_heads=a_       ,      window_size=a_       ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,      )
     SCREAMING_SNAKE_CASE					:			Tuple    	=       UperNetConfig(
         backbone_config=a_       ,      auxiliary_in_channels=a_       ,      num_labels=a_       ,      idalabel=a_       ,      labelaid=a_       ,      )

     return config


def     __lowerCAmelCase						(       a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       []

     # fmt: off
     # stem
     rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')		)
     rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')		)
     rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')		)
     rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')		)
     # stages
     for i in range(len(config.backbone_config.depths		)		):
          for j in range(config.backbone_config.depths[i]		):
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")		)

          if i < 3:
               rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")		)
               rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")		)
          rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")		)
          rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")		)

     # decode head
     rename_keys.extend(
         [
             ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
             ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
             ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
             ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
         ]		)
     # fmt: on

     return rename_keys


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       dct.pop(a_		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       val


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





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
     for i in range(len(backbone_config.depths		)		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       num_features[i]
          for j in range(backbone_config.depths[i]		):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""		)
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""		)
               # next, add query, keys and values (in that order) to the state dict
               SCREAMING_SNAKE_CASE					:			str    	=       in_proj_weight[:dim, :]
               SCREAMING_SNAKE_CASE					:			Any    	=       in_proj_bias[: dim]
               SCREAMING_SNAKE_CASE					:			Any    	=       in_proj_weight[
                   dim : dim * 2, :
               ]
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       in_proj_bias[
                   dim : dim * 2
               ]
               SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_weight[
                   -dim :, :
               ]
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       in_proj_bias[-dim :]
               # fmt: on


def     __lowerCAmelCase						(       a_		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       x.shape
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       x.reshape(a_       ,      4       ,      in_channel // 4		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       x[:, [0, 2, 1, 3], :].transpose(1       ,      2		).reshape(a_       ,      a_		)
     return x


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





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x.shape
     SCREAMING_SNAKE_CASE					:			Dict    	=       x.reshape(a_       ,      in_channel // 4       ,      4		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x[:, :, [0, 2, 1, 3]].transpose(1       ,      2		).reshape(a_       ,      a_		)

     return x


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       x.shape[0]
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x.reshape(4       ,      in_channel // 4		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       x[[0, 2, 1, 3], :].transpose(0       ,      1		).reshape(a_		)
     return x


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       x.shape[0]
     SCREAMING_SNAKE_CASE					:			str    	=       x.reshape(in_channel // 4       ,      4		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       x[:, [0, 2, 1, 3]].transpose(0       ,      1		).reshape(a_		)
     return x


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       {
         'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
         'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
         'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
         'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
     }
     SCREAMING_SNAKE_CASE					:			str    	=       model_name_to_url[model_name]
     SCREAMING_SNAKE_CASE					:			str    	=       torch.hub.load_state_dict_from_url(a_       ,      map_location='cpu'       ,      file_name=a_		)[
         'state_dict'
     ]

     for name, param in state_dict.items():
          print(a_       ,      param.shape		)

     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       get_upernet_config(a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       UperNetForSemanticSegmentation(a_		)
     model.eval()

     # replace "bn" => "batch_norm"
     for key in state_dict.copy().keys():
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       state_dict.pop(a_		)
          if "bn" in key:
               SCREAMING_SNAKE_CASE					:			List[str]    	=       key.replace('bn'       ,      'batch_norm'		)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       val

     # rename keys
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       create_rename_keys(a_		)
     for src, dest in rename_keys:
          rename_key(a_       ,      a_       ,      a_		)
     read_in_q_k_v(a_       ,      config.backbone_config		)

     # fix downsample parameters
     for key, value in state_dict.items():
          if "downsample" in key:
               if "reduction" in key:
                    SCREAMING_SNAKE_CASE					:			List[str]    	=       reverse_correct_unfold_reduction_order(a_		)
               if "norm" in key:
                    SCREAMING_SNAKE_CASE					:			Tuple    	=       reverse_correct_unfold_norm_order(a_		)

     model.load_state_dict(a_		)

     # verify on image
     SCREAMING_SNAKE_CASE					:			int    	=       'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
     SCREAMING_SNAKE_CASE					:			str    	=       Image.open(requests.get(a_       ,      stream=a_		).raw		).convert('RGB'		)

     SCREAMING_SNAKE_CASE					:			Tuple    	=       SegformerImageProcessor()
     SCREAMING_SNAKE_CASE					:			int    	=       processor(a_       ,      return_tensors='pt'		).pixel_values

     with torch.no_grad():
          SCREAMING_SNAKE_CASE					:			str    	=       model(a_		)
          SCREAMING_SNAKE_CASE					:			int    	=       outputs.logits

     print(logits.shape		)
     print('First values of logits:'       ,      logits[0, 0, :3, :3]		)
     # assert values
     if model_name == "upernet-swin-tiny":
          SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor(
              [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]		)
     elif model_name == "upernet-swin-small":
          SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(
              [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]		)
     elif model_name == "upernet-swin-base":
          SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(
              [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]		)
     elif model_name == "upernet-swin-large":
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor(
              [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]		)
     print('Logits:'       ,      outputs.logits[0, 0, :3, :3]		)
     assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,      a_       ,      atol=1e-4		)
     print('Looks ok!'		)

     if pytorch_dump_folder_path is not None:
          print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
          model.save_pretrained(a_		)
          print(f"""Saving processor to {pytorch_dump_folder_path}"""		)
          processor.save_pretrained(a_		)

     if push_to_hub:
          print(f"""Pushing model and processor for {model_name} to hub"""		)
          model.push_to_hub(f"""openmmlab/{model_name}"""		)
          processor.push_to_hub(f"""openmmlab/{model_name}"""		)


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[int]  				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""upernet-swin-tiny""",
     type=str,
     choices=[f"""upernet-swin-{size}""" for size in ["""tiny""", """small""", """base""", """large"""]],
     help="""Name of the Swin + UperNet model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 _lowerCAmelCase  :Dict  				=    parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''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


_lowerCAmelCase  :List[Any]  				=    logging.get_logger(__name__)

# General docstring
_lowerCAmelCase  :Optional[Any]  				=    """RegNetConfig"""

# Base docstring
_lowerCAmelCase  :int  				=    """facebook/regnet-y-040"""
_lowerCAmelCase  :Any  				=    [1, 1_088, 7, 7]

# Image classification docstring
_lowerCAmelCase  :Optional[Any]  				=    """facebook/regnet-y-040"""
_lowerCAmelCase  :Optional[int]  				=    """tabby, tabby cat"""

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






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 3    ,							lowercase__ = 1    ,							lowercase__ = 1    ,							lowercase__ = "relu"    ,							)							-> Any:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       nn.Convad(
             lowercase__    ,							lowercase__    ,							kernel_size=lowercase__    ,							stride=lowercase__    ,							padding=kernel_size // 2    ,							groups=lowercase__    ,							bias=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			Dict    	=       nn.BatchNormad(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       ACTaFN[activation] if activation is not None else nn.Identity()






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.convolution(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.normalization(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.activation(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> str:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       RegNetConvLayer(
             config.num_channels    ,							config.embedding_size    ,							kernel_size=3    ,							stride=2    ,							activation=config.hidden_act )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       config.num_channels






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			Dict    	=       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					:			str    	=       self.embedder(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 2 )							-> Optional[int]:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       nn.Convad(lowercase__    ,							lowercase__    ,							kernel_size=1    ,							stride=lowercase__    ,							bias=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       nn.BatchNormad(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tensor:
         SCREAMING_SNAKE_CASE					:			int    	=       self.convolution(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       self.normalization(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         super().__init__()

         SCREAMING_SNAKE_CASE					:			Any    	=       nn.AdaptiveAvgPoolad((1, 1) )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       nn.Sequential(
             nn.Convad(lowercase__    ,							lowercase__    ,							kernel_size=1 )    ,							nn.ReLU()    ,							nn.Convad(lowercase__    ,							lowercase__    ,							kernel_size=1 )    ,							nn.Sigmoid()    ,							)






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Dict:
         # b c h w -> b c 1 1
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.pooler(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.attention(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       hidden_state * attention
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 1 )							-> Tuple:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       in_channels != out_channels or stride != 1
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       max(1    ,							out_channels // config.groups_width )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
             RegNetShortCut(lowercase__    ,							lowercase__    ,							stride=lowercase__ ) if should_apply_shortcut else nn.Identity()
         )
         SCREAMING_SNAKE_CASE					:			Any    	=       nn.Sequential(
             RegNetConvLayer(lowercase__    ,							lowercase__    ,							kernel_size=1    ,							activation=config.hidden_act )    ,							RegNetConvLayer(lowercase__    ,							lowercase__    ,							stride=lowercase__    ,							groups=lowercase__    ,							activation=config.hidden_act )    ,							RegNetConvLayer(lowercase__    ,							lowercase__    ,							kernel_size=1    ,							activation=lowercase__ )    ,							)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ACTaFN[config.hidden_act]






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Any:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       hidden_state
         SCREAMING_SNAKE_CASE					:			str    	=       self.layer(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       self.shortcut(lowercase__ )
         hidden_state += residual
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.activation(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 1 )							-> str:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Dict    	=       in_channels != out_channels or stride != 1
         SCREAMING_SNAKE_CASE					:			int    	=       max(1    ,							out_channels // config.groups_width )
         SCREAMING_SNAKE_CASE					:			int    	=       (
             RegNetShortCut(lowercase__    ,							lowercase__    ,							stride=lowercase__ ) if should_apply_shortcut else nn.Identity()
         )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       nn.Sequential(
             RegNetConvLayer(lowercase__    ,							lowercase__    ,							kernel_size=1    ,							activation=config.hidden_act )    ,							RegNetConvLayer(lowercase__    ,							lowercase__    ,							stride=lowercase__    ,							groups=lowercase__    ,							activation=config.hidden_act )    ,							RegNetSELayer(lowercase__    ,							reduced_channels=int(round(in_channels / 4 ) ) )    ,							RegNetConvLayer(lowercase__    ,							lowercase__    ,							kernel_size=1    ,							activation=lowercase__ )    ,							)
         SCREAMING_SNAKE_CASE					:			Tuple    	=       ACTaFN[config.hidden_act]






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       hidden_state
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.layer(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.shortcut(lowercase__ )
         hidden_state += residual
         SCREAMING_SNAKE_CASE					:			int    	=       self.activation(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 2    ,							lowercase__ = 2    ,							)							-> Any:
         super().__init__()

         SCREAMING_SNAKE_CASE					:			Tuple    	=       RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

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






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.layers(lowercase__ )
         return hidden_state






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Dict:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Dict    	=       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(
                 lowercase__    ,							config.embedding_size    ,							config.hidden_sizes[0]    ,							stride=2 if config.downsample_in_first_stage else 1    ,							depth=config.depths[0]    ,							) )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       zip(config.hidden_sizes    ,							config.hidden_sizes[1:] )
         for (in_channels, out_channels), depth in zip(lowercase__    ,							config.depths[1:] ):
              self.stages.append(RegNetStage(lowercase__    ,							lowercase__    ,							lowercase__    ,							depth=lowercase__ ) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = False    ,							lowercase__ = True )							-> BaseModelOutputWithNoAttention:
         SCREAMING_SNAKE_CASE					:			str    	=       () if output_hidden_states else None

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

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       stage_module(lowercase__ )

         if output_hidden_states:
              SCREAMING_SNAKE_CASE					:			int    	=       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=lowercase__    ,							hidden_states=lowercase__ )






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Any							=						RegNetConfig
    snake_case__							:   List[str]							=						"regnet"
    snake_case__							:   List[Any]							=						"pixel_values"
    snake_case__							:   Optional[Any]							=						True






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Any:
         if isinstance(lowercase__    ,							nn.Convad ):
              nn.init.kaiming_normal_(module.weight    ,							mode='fan_out'    ,							nonlinearity='relu' )
         elif isinstance(lowercase__    ,							(nn.BatchNormad, nn.GroupNorm) ):
              nn.init.constant_(module.weight    ,							1 )
              nn.init.constant_(module.bias    ,							0 )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=False )							-> Tuple:
         if isinstance(lowercase__    ,							lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Tuple    	=       value






_lowerCAmelCase  :Optional[Any]  				=    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.
"""

_lowerCAmelCase  :Optional[Any]  				=    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."   ,    _SCREAMING_SNAKE_CASE   ,    )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       config
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       RegNetEmbeddings(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       RegNetEncoder(lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       nn.AdaptiveAvgPoolad((1, 1) )
         # Initialize weights and apply final processing
         self.post_init()






    @add_start_docstrings_to_model_forward(lowercase__ )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC    ,							output_type=lowercase__    ,							config_class=_CONFIG_FOR_DOC    ,							modality='vision'    ,							expected_output=_EXPECTED_OUTPUT_SHAPE    ,							)
    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None )							-> BaseModelOutputWithPoolingAndNoAttention:
         SCREAMING_SNAKE_CASE					:			Dict    	=       (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       return_dict if return_dict is not None else self.config.use_return_dict

         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.embedder(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.encoder(
             lowercase__    ,							output_hidden_states=lowercase__    ,							return_dict=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_outputs[0]

         SCREAMING_SNAKE_CASE					:			Dict    	=       self.pooler(lowercase__ )

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

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




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Dict:
         super().__init__(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       config.num_labels
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       RegNetModel(lowercase__ )
         # classification head
         SCREAMING_SNAKE_CASE					:			int    	=       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(lowercase__ )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT    ,							output_type=lowercase__    ,							config_class=_CONFIG_FOR_DOC    ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,							)
    def       _UpperCamelCase    (       self    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> ImageClassifierOutputWithNoAttention:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       return_dict if return_dict is not None else self.config.use_return_dict

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.regnet(lowercase__    ,							output_hidden_states=lowercase__    ,							return_dict=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       outputs.pooler_output if return_dict else outputs[1]

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.classifier(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       None

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

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

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



