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		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = SwinConfig()
       lowerCAmelCase__					     = swin_name.split('_'     )

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

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

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

       lowerCAmelCase__					     = img_size
       lowerCAmelCase__					     = num_classes
       lowerCAmelCase__					     = embed_dim
       lowerCAmelCase__					     = depths
       lowerCAmelCase__					     = num_heads
       lowerCAmelCase__					     = window_size

       return config






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

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

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

       return name






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for key in orig_state_dict.copy().keys():
              lowerCAmelCase__					     = orig_state_dict.pop(lowerCAmelCase__     )

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

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

       return orig_state_dict






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = timm.create_model(lowerCAmelCase__     ,  pretrained=lowerCAmelCase__     )
       timm_model.eval()

       lowerCAmelCase__					     = get_swin_config(lowerCAmelCase__     )
       lowerCAmelCase__					     = SwinForImageClassification(lowerCAmelCase__     )
       model.eval()

       lowerCAmelCase__					     = convert_state_dict(timm_model.state_dict()     ,  lowerCAmelCase__     )
       model.load_state_dict(lowerCAmelCase__     )

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

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

       lowerCAmelCase__					     = timm_model(inputs['pixel_values']     )
       lowerCAmelCase__					     = model(**lowerCAmelCase__     ).logits

       assert torch.allclose(lowerCAmelCase__     ,  lowerCAmelCase__     ,  atol=1e-3     )

       print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""     )
       model.save_pretrained(lowerCAmelCase__     )

       print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
       image_processor.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			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.'
    )

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

lowerCAmelCase__     						=			'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       # Make sure the supplied data is a bytes-like object
       if not isinstance(lowerCAmelCase__     ,  lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
              raise TypeError(lowerCAmelCase__     )

       lowerCAmelCase__					     = ''.join(bin(lowerCAmelCase__     )[2:].zfill(8     ) for byte in data     )

       lowerCAmelCase__					     = len(lowerCAmelCase__     ) % 6 != 0

       if padding_needed:
              # The padding that will be added later
              lowerCAmelCase__					     = b'=' * ((6 - len(lowerCAmelCase__     ) % 6) // 2)

              # Append binary_stream with arbitrary binary digits (0's by default) to make its
              # length a multiple of 6.
              binary_stream += "0" * (6 - len(lowerCAmelCase__     ) % 6)
       else:
              lowerCAmelCase__					     = b''

       # Encode every 6 binary digits to their corresponding Base64 character
       return (
           "".join(
               B64_CHARSET[int(binary_stream[index : index + 6]     ,  2     )]
               for index in range(0     ,  len(lowerCAmelCase__     )     ,  6     )     ).encode()
           + padding
       )






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       # Make sure encoded_data is either a string or a bytes-like object
       if not isinstance(lowerCAmelCase__     ,  lowerCAmelCase__     ) and not isinstance(lowerCAmelCase__     ,  lowerCAmelCase__     ):
              lowerCAmelCase__					     = (
                  'argument should be a bytes-like object or ASCII string, '
                  F"""not '{encoded_data.__class__.__name__}'"""
              )
              raise TypeError(lowerCAmelCase__     )

       # In case encoded_data is a bytes-like object, make sure it contains only
       # ASCII characters so we convert it to a string object
       if isinstance(lowerCAmelCase__     ,  lowerCAmelCase__     ):
              try:
                     lowerCAmelCase__					     = encoded_data.decode('utf-8'     )
              except UnicodeDecodeError:
                     raise ValueError('base64 encoded data should only contain ASCII characters'     )

       lowerCAmelCase__					     = encoded_data.count('='     )

       # Check if the encoded string contains non base64 characters
       if padding:
              assert all(
                  char in B64_CHARSET for char in encoded_data[:-padding]     ), "Invalid base64 character(s) found."
       else:
              assert all(
                  char in B64_CHARSET for char in encoded_data     ), "Invalid base64 character(s) found."

       # Check the padding
       assert len(lowerCAmelCase__     ) % 4 == 0 and padding < 3, "Incorrect padding"

       if padding:
              # Remove padding if there is one
              lowerCAmelCase__					     = encoded_data[:-padding]

              lowerCAmelCase__					     = ''.join(
                  bin(B64_CHARSET.index(lowerCAmelCase__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
       else:
              lowerCAmelCase__					     = ''.join(
                  bin(B64_CHARSET.index(lowerCAmelCase__     )     )[2:].zfill(6     ) for char in encoded_data     )

       lowerCAmelCase__					     = [
           int(binary_stream[index : index + 8]     ,  2     )
           for index in range(0     ,  len(lowerCAmelCase__     )     ,  8     )
       ]

       return bytes(lowerCAmelCase__     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
