





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__   :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(a__   ,			a__   ,			repo_type="""dataset"""       )   ,			"""r"""       )       )
														UpperCamelCase__				     =			{int(a__       ): 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     _UpperCamelCase     (a__   :List[Any]       ):






							"""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     _UpperCamelCase     (a__   :Dict   ,			a__   :Optional[Any]       ):






							"""simple docstring"""





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

														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     _UpperCamelCase     (a__   :int   ,			a__   :Optional[int]       ):






							"""simple docstring"""





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

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

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

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

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

							UpperCamelCase__				     =			timm_model(inputs["""pixel_values"""]       )
							UpperCamelCase__				     =			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__":
					UpperCamelCase__											= 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__											= parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)











import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxBertModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxRobertaModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """bert-base is not a local folder and is not a valid model identifier"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""bert-base"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  , revision="""aaaaaa"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"""  , ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""  )







							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(__lowerCAmelCase  , """Use `from_pt=True` to load this model"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""  )




