



"""simple docstring"""


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict







@pytest.mark.parametrize(
    '''files'''					,     [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]					,     )
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    tmp_path_factory.mktemp('''dset_infos_dir'''				)
				if "full:README.md" in files:
								with open(dataset_infos_dir / '''README.md'''					,     '''w'''				) as f:
												f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''				)
				if "empty:README.md" in files:
								with open(dataset_infos_dir / '''README.md'''					,     '''w'''				) as f:
												f.write(''''''				)
    # we want to support dataset_infos.json for backward compatibility
				if "dataset_infos.json" in files:
								with open(dataset_infos_dir / '''dataset_infos.json'''					,     '''w'''				) as f:
												f.write('''{"default": {"dataset_size": 42}}'''				)
				_UpperCAmelCase					=    DatasetInfosDict.from_directory(_SCREAMING_SNAKE_CASE				)
				assert dataset_infos
				assert dataset_infos["default"].dataset_size == 42







@pytest.mark.parametrize(
    '''dataset_info'''					,     [
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''					,     features=Features({'''a''': Value('''int32'''				)}				)					,     builder_name='''builder'''					,     config_name='''config'''					,     version='''1.0.0'''					,     splits=[{'''name''': '''train'''}]					,     download_size=42					,     ),
    ]					,     )
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						DatasetInfo				):






				'''simple docstring'''



				_UpperCAmelCase					=    str(_SCREAMING_SNAKE_CASE				)
				dataset_info.write_to_directory(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    DatasetInfo.from_directory(_SCREAMING_SNAKE_CASE				)
				assert dataset_info == reloaded
				assert os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE					,     '''dataset_info.json'''				)				)







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    DatasetInfo(
				    description='''foo'''					,     citation='''bar'''					,     homepage='''https://foo.bar'''					,     license='''CC0'''					,     features=Features({'''a''': Value('''int32'''				)}				)					,     post_processed={}					,     supervised_keys=()					,     task_templates=[]					,     builder_name='''builder'''					,     config_name='''config'''					,     version='''1.0.0'''					,     splits=[{'''name''': '''train''', '''num_examples''': 42}]					,     download_checksums={}					,     download_size=1337					,     post_processing_size=442					,     dataset_size=1234					,     size_in_bytes=1337 + 442 + 1234					,     )
				_UpperCAmelCase					=    dataset_info._to_yaml_dict()
				assert sorted(_SCREAMING_SNAKE_CASE				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								assert key in dataset_info_yaml_dict
								assert isinstance(dataset_info_yaml_dict[key]					,     (list, dict, int, str)				)
				_UpperCAmelCase					=    yaml.safe_dump(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    yaml.safe_load(_SCREAMING_SNAKE_CASE				)
				assert dataset_info_yaml_dict == reloaded







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    DatasetInfo()
				_UpperCAmelCase					=    dataset_info._to_yaml_dict()
				assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    '''dataset_infos_dict'''					,     [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}				),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}				),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''					,     features=Features({'''a''': Value('''int32'''				)}				)					,     builder_name='''builder'''					,     config_name='''config'''					,     version='''1.0.0'''					,     splits=[{'''name''': '''train'''}]					,     download_size=42					,     )
            }				),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42				),
                '''v2''': DatasetInfo(dataset_size=1337				),
            }				),
    ]					,     )
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						DatasetInfosDict				):






				'''simple docstring'''



				_UpperCAmelCase					=    str(_SCREAMING_SNAKE_CASE				)
				dataset_infos_dict.write_to_directory(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    DatasetInfosDict.from_directory(_SCREAMING_SNAKE_CASE				)

				# the config_name of the dataset_infos_dict take over the attribute
				for config_name, dataset_info in dataset_infos_dict.items():
								_UpperCAmelCase					=    config_name
								# the yaml representation doesn't include fields like description or citation
								# so we just test that we can recover what we can from the yaml
								_UpperCAmelCase					=    DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
				assert dataset_infos_dict == reloaded

				if dataset_infos_dict:
								assert os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE					,     '''README.md'''				)				)








"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    SwinConfig()
				_UpperCAmelCase					=    swin_name.split('''_'''				)

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

				if model_size == "tiny":
								_UpperCAmelCase					=    96
								_UpperCAmelCase					=    (2, 2, 6, 2)
								_UpperCAmelCase					=    (3, 6, 12, 24)
				elif model_size == "small":
								_UpperCAmelCase					=    96
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (3, 6, 12, 24)
				elif model_size == "base":
								_UpperCAmelCase					=    128
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (4, 8, 16, 32)
				else:
								_UpperCAmelCase					=    192
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (6, 12, 24, 48)

				if "in22k" in swin_name:
								_UpperCAmelCase					=    2_1841
				else:
								_UpperCAmelCase					=    1000
								_UpperCAmelCase					=    '''huggingface/label-files'''
								_UpperCAmelCase					=    '''imagenet-1k-id2label.json'''
								_UpperCAmelCase					=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     repo_type='''dataset'''				)					,     '''r'''				)				)
								_UpperCAmelCase					=    {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
								_UpperCAmelCase					=    idalabel
								_UpperCAmelCase					=    {v: k for k, v in idalabel.items()}

				_UpperCAmelCase					=    img_size
				_UpperCAmelCase					=    num_classes
				_UpperCAmelCase					=    embed_dim
				_UpperCAmelCase					=    depths
				_UpperCAmelCase					=    num_heads
				_UpperCAmelCase					=    window_size

				return config







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



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

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

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

				return name







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase					=    orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

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

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

				return orig_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    timm.create_model(_SCREAMING_SNAKE_CASE					,     pretrained=_SCREAMING_SNAKE_CASE				)
				timm_model.eval()

				_UpperCAmelCase					=    get_swin_config(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    SwinForImageClassification(_SCREAMING_SNAKE_CASE				)
				model.eval()

				_UpperCAmelCase					=    convert_state_dict(timm_model.state_dict()					,     _SCREAMING_SNAKE_CASE				)
				model.load_state_dict(_SCREAMING_SNAKE_CASE				)

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

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

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

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

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

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


if __name__ == "__main__":
					__A   :							int										=   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   :							Tuple										=   parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



