"""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        a_  (   _lowerCAmelCase						:				List[Any]  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			SwinConfig()
       lowercase__    :	Optional[Any]     					=			swin_name.split('_'  )

       lowercase__    :	int     					=			name_split[1]
       lowercase__    :	Tuple     					=			int(name_split[4]  )
       lowercase__    :	Optional[int]     					=			int(name_split[3][-1]  )

       if model_size == "tiny":
              lowercase__    :	Tuple     					=			96
              lowercase__    :	List[Any]     					=			(2, 2, 6, 2)
              lowercase__    :	Tuple     					=			(3, 6, 12, 24)
       elif model_size == "small":
              lowercase__    :	Optional[Any]     					=			96
              lowercase__    :	List[Any]     					=			(2, 2, 18, 2)
              lowercase__    :	Union[str, Any]     					=			(3, 6, 12, 24)
       elif model_size == "base":
              lowercase__    :	int     					=			128
              lowercase__    :	Any     					=			(2, 2, 18, 2)
              lowercase__    :	Any     					=			(4, 8, 16, 32)
       else:
              lowercase__    :	List[str]     					=			192
              lowercase__    :	str     					=			(2, 2, 18, 2)
              lowercase__    :	Any     					=			(6, 12, 24, 48)

       if "in22k" in swin_name:
              lowercase__    :	Optional[Any]     					=			2_1841
       else:
              lowercase__    :	str     					=			1000
              lowercase__    :	int     					=			'huggingface/label-files'
              lowercase__    :	Optional[Any]     					=			'imagenet-1k-id2label.json'
              lowercase__    :	List[Any]     					=			json.load(open(hf_hub_download(_lowerCAmelCase     ,				_lowerCAmelCase     ,				repo_type='dataset'  )     ,				'r'  )  )
              lowercase__    :	str     					=			{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
              lowercase__    :	str     					=			idalabel
              lowercase__    :	str     					=			{v: k for k, v in idalabel.items()}

       lowercase__    :	Tuple     					=			img_size
       lowercase__    :	Tuple     					=			num_classes
       lowercase__    :	str     					=			embed_dim
       lowercase__    :	List[Any]     					=			depths
       lowercase__    :	Dict     					=			num_heads
       lowercase__    :	Any     					=			window_size

       return config





def        a_  (   _lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       if "patch_embed.proj" in name:
              lowercase__    :	Optional[Any]     					=			name.replace('patch_embed.proj'     ,				'embeddings.patch_embeddings.projection'  )
       if "patch_embed.norm" in name:
              lowercase__    :	Optional[int]     					=			name.replace('patch_embed.norm'     ,				'embeddings.norm'  )
       if "layers" in name:
              lowercase__    :	Any     					=			'encoder.' + name
       if "attn.proj" in name:
              lowercase__    :	str     					=			name.replace('attn.proj'     ,				'attention.output.dense'  )
       if "attn" in name:
              lowercase__    :	int     					=			name.replace('attn'     ,				'attention.self'  )
       if "norm1" in name:
              lowercase__    :	List[Any]     					=			name.replace('norm1'     ,				'layernorm_before'  )
       if "norm2" in name:
              lowercase__    :	Tuple     					=			name.replace('norm2'     ,				'layernorm_after'  )
       if "mlp.fc1" in name:
              lowercase__    :	Optional[Any]     					=			name.replace('mlp.fc1'     ,				'intermediate.dense'  )
       if "mlp.fc2" in name:
              lowercase__    :	List[str]     					=			name.replace('mlp.fc2'     ,				'output.dense'  )

       if name == "norm.weight":
              lowercase__    :	Tuple     					=			'layernorm.weight'
       if name == "norm.bias":
              lowercase__    :	Optional[int]     					=			'layernorm.bias'

       if "head" in name:
              lowercase__    :	Union[str, Any]     					=			name.replace('head'     ,				'classifier'  )
       else:
              lowercase__    :	Dict     					=			'swin.' + name

       return name





def        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       for key in orig_state_dict.copy().keys():
              lowercase__    :	Union[str, Any]     					=			orig_state_dict.pop(_lowerCAmelCase  )

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

                     if "weight" in key:
                            lowercase__    :	str     					=			val[:dim, :]
                            lowercase__    :	Optional[int]     					=			val[
                                dim : dim * 2, :
                            ]
                            lowercase__    :	Tuple     					=			val[-dim:, :]
                     else:
                            lowercase__    :	List[str]     					=			val[
                                :dim
                            ]
                            lowercase__    :	Tuple     					=			val[
                                dim : dim * 2
                            ]
                            lowercase__    :	Union[str, Any]     					=			val[
                                -dim:
                            ]
              else:
                     lowercase__    :	Union[str, Any]     					=			val

       return orig_state_dict





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			timm.create_model(_lowerCAmelCase     ,				pretrained=_lowerCAmelCase  )
       timm_model.eval()

       lowercase__    :	Optional[Any]     					=			get_swin_config(_lowerCAmelCase  )
       lowercase__    :	Any     					=			SwinForImageClassification(_lowerCAmelCase  )
       model.eval()

       lowercase__    :	Dict     					=			convert_state_dict(timm_model.state_dict()     ,				_lowerCAmelCase  )
       model.load_state_dict(_lowerCAmelCase  )

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

       lowercase__    :	int     					=			AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'     ,				'-'  )  )  )
       lowercase__    :	List[str]     					=			Image.open(requests.get(_lowerCAmelCase     ,				stream=_lowerCAmelCase  ).raw  )
       lowercase__    :	List[str]     					=			image_processor(images=_lowerCAmelCase     ,				return_tensors='pt'  )

       lowercase__    :	Tuple     					=			timm_model(inputs['pixel_values']  )
       lowercase__    :	Optional[Any]     					=			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__":
       _UpperCamelCase     :		List[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."
       )

       _UpperCamelCase     :		Any											=					parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


"""simple docstring"""





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_UpperCamelCase     :		Any											=					[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def        a_  (   _lowerCAmelCase						:				Optional[Any]=True  ):





       '''simple docstring'''

       if with_config:
              return [
                  {
                      "testcase_name": d["dataset"] + "/" + d["config_name"],
                      "dataset": d["dataset"],
                      "config_name": d["config_name"],
                  }
                  for d in DATASETS_ON_HF_GCP
              ]
       else:
              return [
                  {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
              ]


@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_a))
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					str										=						None
      lowerCamelCase__	:					Optional[Any]										=						None


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      List[Any]:
             with TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[str]     					=			dataset_module_factory(a	,    cache_dir=a							)

                    lowercase__    :	List[Any]     					=			import_main_class(dataset_module.module_path	,    dataset=a							)

                    lowercase__    :	DatasetBuilder     					=			builder_cls(
                        cache_dir=a	,    config_name=a	,    hash=dataset_module.hash	,    )

                    lowercase__    :	Union[str, Any]     					=			'/'.join(
                        [
                            HF_GCP_BASE_URL,
                            builder_instance._relative_data_dir(with_hash=a							).replace(os.sep	,    '/'							),
                            config.DATASET_INFO_FILENAME,
                        ]							)
                    lowercase__    :	Union[str, Any]     					=			cached_path(a	,    cache_dir=a							)
                    self.assertTrue(os.path.exists(a							)							)





@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
       lowercase__    :	int     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	Optional[int]     					=			import_main_class(dataset_module.module_path  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
       lowercase__    :	Optional[int]     					=			None
       builder_instance.download_and_prepare()
       lowercase__    :	Optional[int]     					=			builder_instance.as_dataset()
       assert ds






@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	List[str]     					=			import_main_class(dataset_module.module_path     ,				dataset=_lowerCAmelCase  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       lowercase__    :	Union[str, Any]     					=			builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  )
       assert "train" in ds
       assert isinstance(ds['train']     ,				_lowerCAmelCase  )
       assert next(iter(ds['train']  )  )

