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 	__A		(      _A       ):



 """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							=	128
  __a							=	(2, 2, 18, 2)
  __a							=	(4, 8, 16, 32)
 else:
  __a							=	192
  __a							=	(2, 2, 18, 2)
  __a							=	(6, 12, 24, 48)

 if "in22k" in swin_name:
  __a							=	2_1841
 else:
  __a							=	1000
  __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 	__A		(      _A       ):



 """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 	__A		(      _A				,					_A       ):



 """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 	__A		(      _A				,					_A       ):



 """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__":
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		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			:						Union[str, Any]				  =		parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (abs(source - target       ) / target) < 0.01






@pytest.mark.integration
def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	_TestCommandArgs(dataset=_A				,					all_configs=_A				,					save_infos=_A       )
 __a							=	TestCommand(*_A       )
 test_command.run()
 __a							=	os.path.join(_A				,					"README.md"       )
 assert os.path.exists(_A       )
 __a							=	DatasetInfosDict.from_directory(_A       )
 __a							=	DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"       )       ),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]       )       ),
                     "langs": Sequence(Value("string"       )       ),
                     "spans": Sequence(Value("string"       )       ),
                 }       )				,					splits=[
                 {
                     "name": "train",
                     "num_bytes": 235_1563,
                     "num_examples": 1_0000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 23_8418,
                     "num_examples": 1000,
                 },
             ]				,					download_size=394_0680				,					dataset_size=258_9981				,					)
     }       )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  __a							,  __a							=	getattr(dataset_infos["default"]				,					_A       ), getattr(expected_dataset_infos["default"]				,					_A       )
  if key == "num_bytes":
   assert is_apercent_close(_A				,					_A       )
  elif key == "splits":
   assert list(_A       ) == list(_A       )
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes				,					expected[split].num_bytes       )
  else:
   result == expected


