




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			SwinConfig(image_size=1_9_2       )

						if "base" in model_name:
												_UpperCAmelCase      =			6
												_UpperCAmelCase      =			1_2_8
												_UpperCAmelCase      =			(2, 2, 1_8, 2)
												_UpperCAmelCase      =			(4, 8, 1_6, 3_2)
						elif "large" in model_name:
												_UpperCAmelCase      =			1_2
												_UpperCAmelCase      =			1_9_2
												_UpperCAmelCase      =			(2, 2, 1_8, 2)
												_UpperCAmelCase      =			(6, 1_2, 2_4, 4_8)
						else:
												raise ValueError("""Model not supported, only supports base and large variants"""       )

						_UpperCAmelCase      =			window_size
						_UpperCAmelCase      =			embed_dim
						_UpperCAmelCase      =			depths
						_UpperCAmelCase      =			num_heads

						return config




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Any:
						if "encoder.mask_token" in name:
												_UpperCAmelCase      =			name.replace("""encoder.mask_token"""			,     """embeddings.mask_token"""       )
						if "encoder.patch_embed.proj" in name:
												_UpperCAmelCase      =			name.replace("""encoder.patch_embed.proj"""			,     """embeddings.patch_embeddings.projection"""       )
						if "encoder.patch_embed.norm" in name:
												_UpperCAmelCase      =			name.replace("""encoder.patch_embed.norm"""			,     """embeddings.norm"""       )
						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 == "encoder.norm.weight":
												_UpperCAmelCase      =			"""layernorm.weight"""
						if name == "encoder.norm.bias":
												_UpperCAmelCase      =			"""layernorm.bias"""

						if "decoder" in name:
												pass
						else:
												_UpperCAmelCase      =			"""swin.""" + name

						return name




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Optional[Any]:
						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase      =			orig_state_dict.pop(snake_case       )

												if "attn_mask" in key:
																		pass
												elif "qkv" in key:
																		_UpperCAmelCase      =			key.split("""."""       )
																		_UpperCAmelCase      =			int(key_split[2]       )
																		_UpperCAmelCase      =			int(key_split[4]       )
																		_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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )["""model"""]

						_UpperCAmelCase      =			get_swin_config(snake_case       )
						_UpperCAmelCase      =			SwinForMaskedImageModeling(snake_case       )
						model.eval()

						_UpperCAmelCase      =			convert_state_dict(snake_case			,     snake_case       )
						model.load_state_dict(snake_case       )

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

						_UpperCAmelCase      =			ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2}       )
						_UpperCAmelCase      =			Image.open(requests.get(snake_case			,     stream=snake_case       ).raw       )
						_UpperCAmelCase      =			image_processor(images=snake_case			,     return_tensors="""pt"""       )

						with torch.no_grad():
												_UpperCAmelCase      =			model(**snake_case       ).logits

						print(outputs.keys()       )
						print("""Looks ok!"""       )

						if pytorch_dump_folder_path is not None:
												print(f"Saving model {model_name} to {pytorch_dump_folder_path}"       )
												model.save_pretrained(snake_case       )

												print(f"Saving image processor to {pytorch_dump_folder_path}"       )
												image_processor.save_pretrained(snake_case       )

						if push_to_hub:
												print(f"Pushing model and image processor for {model_name} to hub"       )
												model.push_to_hub(f"microsoft/{model_name}"       )
												image_processor.push_to_hub(f"microsoft/{model_name}"       )


if __name__ == "__main__":
				a													=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default="swin-base-simmim-window6-192",
				    type=str,
				    choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
				    help="Name of the Swin SimMIM model you'd like to convert.",
				)
				parser.add_argument(
				    "--checkpoint_path",
				    default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
				    type=str,
				    help="Path to the original PyTorch checkpoint (.pth file).",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
				)
				parser.add_argument(
				    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
				)

				a													=			parser.parse_args()
				convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				int:
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""			,     [False, True]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case			,     keep_in_memory=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				int:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Optional[int]:
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						_UpperCAmelCase      =			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
						_UpperCAmelCase      =			features.copy()
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""split"""			,     [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case			,     split=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )
						assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""			,     [str, list]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Any:
						if issubclass(snake_case			,     snake_case       ):
												_UpperCAmelCase      =			jsonl_path
						elif issubclass(snake_case			,     snake_case       ):
												_UpperCAmelCase      =			[jsonl_path]
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=("train",)       )						->				str:
						assert isinstance(snake_case			,     snake_case       )
						for split in splits:
												_UpperCAmelCase      =			dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""			,     [False, True]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase      =			JsonDatasetReader({"""train""": jsonl_path}			,     cache_dir=snake_case			,     keep_in_memory=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Tuple:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader({"""train""": jsonl_path}			,     features=snake_case			,     cache_dir=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case       )




@pytest.mark.parametrize("""split"""			,     [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						if split:
												_UpperCAmelCase      =			{split: jsonl_path}
						else:
												_UpperCAmelCase      =			"""train"""
												_UpperCAmelCase      =			{"""train""": jsonl_path, """test""": jsonl_path}
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case			,     splits=list(path.keys()       )       )
						assert all(dataset[split].split == split for split in path.keys()       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
						return json.load(snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
						return [json.loads(snake_case       ) for line in buffer]




class       _A				:






		@pytest.mark.parametrize("""lines, load_json_function"""						,		[(True, load_json_lines), (False, load_json)]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json_function(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								assert isinstance(exported_content[0]						,		_SCREAMING_SNAKE_CASE			)
								assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize(
		    """orient, container, keys, len_at"""						,		[
		        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
		        ("""split""", dict, {"""columns""", """data"""}, """data"""),
		        ("""index""", dict, set("""0123456789"""			), None),
		        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
		        ("""values""", list, None, None),
		        ("""table""", dict, {"""schema""", """data"""}, """data"""),
		    ]						,		)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		orient=_SCREAMING_SNAKE_CASE			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(_SCREAMING_SNAKE_CASE						,		"""keys"""			) and not hasattr(exported_content[0]						,		"""keys"""			)
								if len_at:
														assert len(exported_content[len_at]			) == 10
								else:
														assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize("""lines, load_json_function"""						,		[(True, load_json_lines), (False, load_json)]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		num_proc=2			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json_function(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								assert isinstance(exported_content[0]						,		_SCREAMING_SNAKE_CASE			)
								assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize(
		    """orient, container, keys, len_at"""						,		[
		        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
		        ("""split""", dict, {"""columns""", """data"""}, """data"""),
		        ("""index""", dict, set("""0123456789"""			), None),
		        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
		        ("""values""", list, None, None),
		        ("""table""", dict, {"""schema""", """data"""}, """data"""),
		    ]						,		)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		orient=_SCREAMING_SNAKE_CASE						,		num_proc=2			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(_SCREAMING_SNAKE_CASE						,		"""keys"""			) and not hasattr(exported_content[0]						,		"""keys"""			)
								if len_at:
														assert len(exported_content[len_at]			) == 10
								else:
														assert len(_SCREAMING_SNAKE_CASE			) == 10






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								with pytest.raises(_SCREAMING_SNAKE_CASE			):
														with io.BytesIO() as buffer:
																				JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		num_proc=0			)






		@pytest.mark.parametrize("""compression, extension"""						,		[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			tmp_path_factory.mktemp("""data"""			) / F"test.json.{extension}"
								_UpperCAmelCase      =			str(shared_datadir / F"test_file.json.{extension}"			)
								JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		compression=_SCREAMING_SNAKE_CASE			).write()

								with fsspec.open(_SCREAMING_SNAKE_CASE						,		"""rb"""						,		compression="""infer"""			) as f:
														_UpperCAmelCase      =			f.read()
								with fsspec.open(_SCREAMING_SNAKE_CASE						,		"""rb"""						,		compression="""infer"""			) as f:
														_UpperCAmelCase      =			f.read()
								assert exported_content == original_content