

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 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







  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__  =96
    a__  =(2, 2, 6, 2)
    a__  =(3, 6, 12, 24)
  elif model_size == "small":
    a__  =96
    a__  =(2, 2, 18, 2)
    a__  =(3, 6, 12, 24)
  elif model_size == "base":
    a__  =1_28
    a__  =(2, 2, 18, 2)
    a__  =(4, 8, 16, 32)
  else:
    a__  =1_92
    a__  =(2, 2, 18, 2)
    a__  =(6, 12, 24, 48)

  if "in22k" in swin_name:
    a__  =2_18_41
  else:
    a__  =10_00
    a__  ='''huggingface/label-files'''
    a__  ='''imagenet-1k-id2label.json'''
    a__  =json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
    a__  ={int(_A							): 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 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  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 			UpperCAmelCase__				(			_A      : str	,      _A      : Dict							):





  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    a__  =orig_state_dict.pop(_A							)

    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 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : List[Any]							):





  '''simple docstring'''







  a__  =timm.create_model(_A	,      pretrained=_A							)
  timm_model.eval()

  a__  =get_swin_config(_A							)
  a__  =SwinForImageClassification(_A							)
  model.eval()

  a__  =convert_state_dict(timm_model.state_dict()	,      _A							)
  model.load_state_dict(_A							)

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

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

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

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

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

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


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)



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
