




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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__a          =			logging.get_logger(__name__)

# General docstring
__a          =			'ResNetConfig'

# Base docstring
__a          =			'microsoft/resnet-50'
__a          =			[1, 2_048, 7, 7]

# Image classification docstring
__a          =			'microsoft/resnet-50'
__a          =			'tiger cat'

__a          =			[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class   __a(					nn.Module       ):






    """simple docstring"""





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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Tensor:
         UpperCAmelCase_      :							Any       =      self.convolution(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      self.normalization(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      self.activation(_SCREAMING_SNAKE_CASE  )
         return hidden_state


class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         super().__init__()
         UpperCAmelCase_      :							Any       =      ResNetConvLayer(
             config.num_channels       ,config.embedding_size       ,kernel_size=7       ,stride=2       ,activation=config.hidden_act  )
         UpperCAmelCase_      :							int       =      nn.MaxPoolad(kernel_size=3       ,stride=2       ,padding=1  )
         UpperCAmelCase_      :							List[str]       =      config.num_channels





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Tensor:
         UpperCAmelCase_      :							Union[str, Any]       =      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.'''  )
         UpperCAmelCase_      :							Dict       =      self.embedder(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      self.pooler(_SCREAMING_SNAKE_CASE  )
         return embedding


class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = 2  )			->		List[str]:
         super().__init__()
         UpperCAmelCase_      :							Union[str, Any]       =      nn.Convad(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,kernel_size=1       ,stride=_SCREAMING_SNAKE_CASE       ,bias=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      nn.BatchNormad(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Tensor:
         UpperCAmelCase_      :							List[Any]       =      self.convolution(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      self.normalization(_SCREAMING_SNAKE_CASE  )
         return hidden_state


class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = "relu"  )			->		List[str]:
         super().__init__()
         UpperCAmelCase_      :							int       =      in_channels != out_channels or stride != 1
         UpperCAmelCase_      :							List[Any]       =      (
             ResNetShortCut(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,stride=_SCREAMING_SNAKE_CASE  ) if should_apply_shortcut else nn.Identity()
         )
         UpperCAmelCase_      :							List[Any]       =      nn.Sequential(
             ResNetConvLayer(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,stride=_SCREAMING_SNAKE_CASE  )       ,ResNetConvLayer(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,activation=_SCREAMING_SNAKE_CASE  )       ,)
         UpperCAmelCase_      :							str       =      ACTaFN[activation]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         UpperCAmelCase_      :							List[str]       =      hidden_state
         UpperCAmelCase_      :							List[str]       =      self.layer(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      self.shortcut(_SCREAMING_SNAKE_CASE  )
         hidden_state += residual
         UpperCAmelCase_      :							Tuple       =      self.activation(_SCREAMING_SNAKE_CASE  )
         return hidden_state


class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = "relu"       ,_SCREAMING_SNAKE_CASE = 4  )			->		Optional[int]:
         super().__init__()
         UpperCAmelCase_      :							Union[str, Any]       =      in_channels != out_channels or stride != 1
         UpperCAmelCase_      :							Optional[int]       =      out_channels // reduction
         UpperCAmelCase_      :							Union[str, Any]       =      (
             ResNetShortCut(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,stride=_SCREAMING_SNAKE_CASE  ) if should_apply_shortcut else nn.Identity()
         )
         UpperCAmelCase_      :							str       =      nn.Sequential(
             ResNetConvLayer(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,kernel_size=1  )       ,ResNetConvLayer(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,stride=_SCREAMING_SNAKE_CASE  )       ,ResNetConvLayer(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,kernel_size=1       ,activation=_SCREAMING_SNAKE_CASE  )       ,)
         UpperCAmelCase_      :							Optional[Any]       =      ACTaFN[activation]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         UpperCAmelCase_      :							Optional[int]       =      hidden_state
         UpperCAmelCase_      :							List[str]       =      self.layer(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      self.shortcut(_SCREAMING_SNAKE_CASE  )
         hidden_state += residual
         UpperCAmelCase_      :							Any       =      self.activation(_SCREAMING_SNAKE_CASE  )
         return hidden_state


class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = 2       ,_SCREAMING_SNAKE_CASE = 2       ,)			->		List[str]:
         super().__init__()

         UpperCAmelCase_      :							Optional[Any]       =      ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

         UpperCAmelCase_      :							List[Any]       =      nn.Sequential(
             # downsampling is done in the first layer with stride of 2
             layer(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,stride=_SCREAMING_SNAKE_CASE       ,activation=config.hidden_act  )       ,*[layer(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,activation=config.hidden_act  ) for _ in range(depth - 1  )]       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Tensor:
         UpperCAmelCase_      :							List[str]       =      input
         for layer in self.layers:
              UpperCAmelCase_      :							Union[str, Any]       =      layer(_SCREAMING_SNAKE_CASE  )
         return hidden_state


class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         super().__init__()
         UpperCAmelCase_      :							Tuple       =      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(
             ResNetStage(
                 _SCREAMING_SNAKE_CASE       ,config.embedding_size       ,config.hidden_sizes[0]       ,stride=2 if config.downsample_in_first_stage else 1       ,depth=config.depths[0]       ,)  )
         UpperCAmelCase_      :							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(ResNetStage(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,depth=_SCREAMING_SNAKE_CASE  )  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = True  )			->		BaseModelOutputWithNoAttention:
         UpperCAmelCase_      :							Union[str, Any]       =      () if output_hidden_states else None

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

              UpperCAmelCase_      :							int       =      stage_module(_SCREAMING_SNAKE_CASE  )

         if output_hidden_states:
              UpperCAmelCase_      :							str       =      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   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				ResNetConfig
    lowerCAmelCase        =				'''resnet'''
    lowerCAmelCase        =				'''pixel_values'''
    lowerCAmelCase        =				True





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		Tuple:
         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							Any       =      value


__a          =			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 ([`ResNetConfig`]): 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'

__a          =			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 [`~utils.ModelOutput`] instead of a plain tuple.\n'

@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''    ,						_a    ,						)
class   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         super().__init__(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      config
         UpperCAmelCase_      :							List[Any]       =      ResNetEmbeddings(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      ResNetEncoder(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None  )			->		BaseModelOutputWithPoolingAndNoAttention:
         UpperCAmelCase_      :							Any       =      (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         UpperCAmelCase_      :							Optional[int]       =      return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase_      :							List[str]       =      self.embedder(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.encoder(
             _SCREAMING_SNAKE_CASE       ,output_hidden_states=_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							List[Any]       =      encoder_outputs[0]

         UpperCAmelCase_      :							Tuple       =      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(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''    ,						_a    ,						)
class   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         super().__init__(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      config.num_labels
         UpperCAmelCase_      :							Any       =      ResNetModel(_SCREAMING_SNAKE_CASE  )
         # classification head
         UpperCAmelCase_      :							List[str]       =      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 							a__				(				self       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,)			->		ImageClassifierOutputWithNoAttention:
         UpperCAmelCase_      :							Tuple       =      return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase_      :							Dict       =      self.resnet(_SCREAMING_SNAKE_CASE       ,output_hidden_states=_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE  )

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

         UpperCAmelCase_      :							int       =      self.classifier(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      None

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

         if not return_dict:
              UpperCAmelCase_      :							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  )


@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''    ,						_a    ,						)
class   __a(					_a    ,						_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         super().__init__(_SCREAMING_SNAKE_CASE  )
         super()._init_backbone(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      [config.embedding_size] + config.hidden_sizes
         UpperCAmelCase_      :							str       =      ResNetEmbeddings(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      ResNetEncoder(_SCREAMING_SNAKE_CASE  )

         # initialize weights and apply final processing
         self.post_init()





    @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE  )
    @replace_return_docstrings(output_type=_SCREAMING_SNAKE_CASE       ,config_class=_CONFIG_FOR_DOC  )
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None  )			->		BackboneOutput:
         UpperCAmelCase_      :							List[str]       =      return_dict if return_dict is not None else self.config.use_return_dict
         UpperCAmelCase_      :							str       =      (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )

         UpperCAmelCase_      :							Optional[Any]       =      self.embedder(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.encoder(_SCREAMING_SNAKE_CASE       ,output_hidden_states=_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      outputs.hidden_states

         UpperCAmelCase_      :							Dict       =      ()
         for idx, stage in enumerate(self.stage_names  ):
              if stage in self.out_features:
                   feature_maps += (hidden_states[idx],)

         if not return_dict:
              UpperCAmelCase_      :							List[Any]       =      (feature_maps,)
              if output_hidden_states:
                   output += (outputs.hidden_states,)
              return output

         return BackboneOutput(
             feature_maps=_SCREAMING_SNAKE_CASE       ,hidden_states=outputs.hidden_states if output_hidden_states else None       ,attentions=_SCREAMING_SNAKE_CASE       ,)





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

     if model_size == "tiny":
          UpperCAmelCase_      :							Tuple       =      96
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 6, 2)
          UpperCAmelCase_      :							str       =      (3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase_      :							Dict       =      96
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							List[str]       =      (3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase_      :							Tuple       =      128
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Optional[int]       =      (4, 8, 16, 32)
     else:
          UpperCAmelCase_      :							List[str]       =      192
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Union[str, Any]       =      (6, 12, 24, 48)

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if "patch_embed.proj" in name:
          UpperCAmelCase_      :							str       =      name.replace('''patch_embed.proj'''			,						'''embeddings.patch_embeddings.projection'''				)
     if "patch_embed.norm" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''patch_embed.norm'''			,						'''embeddings.norm'''				)
     if "layers" in name:
          UpperCAmelCase_      :							Tuple       =      '''encoder.''' + name
     if "attn.proj" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''attn.proj'''			,						'''attention.output.dense'''				)
     if "attn" in name:
          UpperCAmelCase_      :							int       =      name.replace('''attn'''			,						'''attention.self'''				)
     if "norm1" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''norm1'''			,						'''layernorm_before'''				)
     if "norm2" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''norm2'''			,						'''layernorm_after'''				)
     if "mlp.fc1" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''mlp.fc1'''			,						'''intermediate.dense'''				)
     if "mlp.fc2" in name:
          UpperCAmelCase_      :							Optional[int]       =      name.replace('''mlp.fc2'''			,						'''output.dense'''				)

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               UpperCAmelCase_      :							str       =      key.split('''.'''				)
               UpperCAmelCase_      :							str       =      int(key_split[1]				)
               UpperCAmelCase_      :							Optional[Any]       =      int(key_split[3]				)
               UpperCAmelCase_      :							Optional[int]       =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    UpperCAmelCase_      :							Any       =      val[:dim, :]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_      :							List[str]       =      val[-dim:, :]
               else:
                    UpperCAmelCase_      :							Any       =      val[
                        :dim
                    ]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_      :							Union[str, Any]       =      val[
                        -dim:
                    ]
          else:
               UpperCAmelCase_      :							Optional[Any]       =      val

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_      :							int       =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''			,						'''-'''				)				)				)
     UpperCAmelCase_      :							Optional[Any]       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)
     UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				)

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      model(**_lowercase				).logits

     assert torch.allclose(_lowercase			,						_lowercase			,						atol=1E-3				)

     print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
     image_processor.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin timm 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.'
     )

     __a          =			parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)