"""simple docstring"""



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(	a				):
       __a       =    SwinConfig()
       __a       =    swin_name.split("_"				)

       __a       =    name_split[1]
       __a       =    int(name_split[4]				)
       __a       =    int(name_split[3][-1]				)

       if model_size == "tiny":
              __a       =    9_6
              __a       =    (2, 2, 6, 2)
              __a       =    (3, 6, 1_2, 2_4)
       elif model_size == "small":
              __a       =    9_6
              __a       =    (2, 2, 1_8, 2)
              __a       =    (3, 6, 1_2, 2_4)
       elif model_size == "base":
              __a       =    1_2_8
              __a       =    (2, 2, 1_8, 2)
              __a       =    (4, 8, 1_6, 3_2)
       else:
              __a       =    1_9_2
              __a       =    (2, 2, 1_8, 2)
              __a       =    (6, 1_2, 2_4, 4_8)

       if "in22k" in swin_name:
              __a       =    2_1_8_4_1
       else:
              __a       =    1_0_0_0
              __a       =    '''huggingface/label-files'''
              __a       =    '''imagenet-1k-id2label.json'''
              __a       =    json.load(open(hf_hub_download(lowercase_   ,    lowercase_   ,    repo_type="dataset"				)   ,    "r"				)				)
              __a       =    {int(lowercase_				): v for k, v in idalabel.items()}
              __a       =    idalabel
              __a       =    {v: k for k, v in idalabel.items()}

       __a       =    img_size
       __a       =    num_classes
       __a       =    embed_dim
       __a       =    depths
       __a       =    num_heads
       __a       =    window_size

       return config



def 	_lowerCamelCase(	a				):
       if "patch_embed.proj" in name:
              __a       =    name.replace("patch_embed.proj"   ,    "embeddings.patch_embeddings.projection"				)
       if "patch_embed.norm" in name:
              __a       =    name.replace("patch_embed.norm"   ,    "embeddings.norm"				)
       if "layers" in name:
              __a       =    '''encoder.''' + name
       if "attn.proj" in name:
              __a       =    name.replace("attn.proj"   ,    "attention.output.dense"				)
       if "attn" in name:
              __a       =    name.replace("attn"   ,    "attention.self"				)
       if "norm1" in name:
              __a       =    name.replace("norm1"   ,    "layernorm_before"				)
       if "norm2" in name:
              __a       =    name.replace("norm2"   ,    "layernorm_after"				)
       if "mlp.fc1" in name:
              __a       =    name.replace("mlp.fc1"   ,    "intermediate.dense"				)
       if "mlp.fc2" in name:
              __a       =    name.replace("mlp.fc2"   ,    "output.dense"				)

       if name == "norm.weight":
              __a       =    '''layernorm.weight'''
       if name == "norm.bias":
              __a       =    '''layernorm.bias'''

       if "head" in name:
              __a       =    name.replace("head"   ,    "classifier"				)
       else:
              __a       =    '''swin.''' + name

       return name



def 	_lowerCamelCase(	a   ,    a				):
       for key in orig_state_dict.copy().keys():
              __a       =    orig_state_dict.pop(lowercase_				)

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

                     if "weight" in key:
                            __a       =    val[:dim, :]
                            __a       =    val[
                                dim : dim * 2, :
                            ]
                            __a       =    val[-dim:, :]
                     else:
                            __a       =    val[
                                :dim
                            ]
                            __a       =    val[
                                dim : dim * 2
                            ]
                            __a       =    val[
                                -dim:
                            ]
              else:
                     __a       =    val

       return orig_state_dict



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    timm.create_model(lowercase_   ,    pretrained=lowercase_				)
       timm_model.eval()

       __a       =    get_swin_config(lowercase_				)
       __a       =    SwinForImageClassification(lowercase_				)
       model.eval()

       __a       =    convert_state_dict(timm_model.state_dict()   ,    lowercase_				)
       model.load_state_dict(lowercase_				)

       __a       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

       __a       =    AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"   ,    "-"				)				)				)
       __a       =    Image.open(requests.get(lowercase_   ,    stream=lowercase_				).raw				)
       __a       =    image_processor(images=lowercase_   ,    return_tensors="pt"				)

       __a       =    timm_model(inputs["pixel_values"]				)
       __a       =    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__":
   SCREAMING_SNAKE_CASE__:str									=			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."""
   )

   SCREAMING_SNAKE_CASE__:Tuple									=			parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DeiTImageProcessor instead.'''   ,				a__   ,				)
             super().__init__(*a__   ,				**a__)

