



"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		int 					=					SwinvaConfig()
					snake_case_					:		Union[str, Any] 					=					swinva_name.split("""_"""			)

					snake_case_					:		List[Any] 					=					name_split[1]
					if "to" in name_split[3]:
										snake_case_					:		Union[str, Any] 					=					int(name_split[3][-3:]			)
					else:
										snake_case_					:		Optional[int] 					=					int(name_split[3]			)
					if "to" in name_split[2]:
										snake_case_					:		Dict 					=					int(name_split[2][-2:]			)
					else:
										snake_case_					:		int 					=					int(name_split[2][6:]			)

					if model_size == "tiny":
										snake_case_					:		str 					=					9_6
										snake_case_					:		Dict 					=					(2, 2, 6, 2)
										snake_case_					:		int 					=					(3, 6, 1_2, 2_4)
					elif model_size == "small":
										snake_case_					:		Tuple 					=					9_6
										snake_case_					:		Optional[Any] 					=					(2, 2, 1_8, 2)
										snake_case_					:		Optional[int] 					=					(3, 6, 1_2, 2_4)
					elif model_size == "base":
										snake_case_					:		Dict 					=					1_2_8
										snake_case_					:		str 					=					(2, 2, 1_8, 2)
										snake_case_					:		List[Any] 					=					(4, 8, 1_6, 3_2)
					else:
										snake_case_					:		Tuple 					=					1_9_2
										snake_case_					:		Tuple 					=					(2, 2, 1_8, 2)
										snake_case_					:		Optional[Any] 					=					(6, 1_2, 2_4, 4_8)

					if "to" in swinva_name:
										snake_case_					:		str 					=					(1_2, 1_2, 1_2, 6)

					if ("22k" in swinva_name) and ("to" not in swinva_name):
										snake_case_					:		str 					=					2_1_8_4_1
										snake_case_					:		str 					=					"""huggingface/label-files"""
										snake_case_					:		List[str] 					=					"""imagenet-22k-id2label.json"""
										snake_case_					:		Union[str, Any] 					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    repo_type="""dataset"""			)				,    """r"""			)			)
										snake_case_					:		Optional[int] 					=					{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
										snake_case_					:		int 					=					idalabel
										snake_case_					:		Optional[Any] 					=					{v: k for k, v in idalabel.items()}

					else:
										snake_case_					:		int 					=					1_0_0_0
										snake_case_					:		List[Any] 					=					"""huggingface/label-files"""
										snake_case_					:		List[Any] 					=					"""imagenet-1k-id2label.json"""
										snake_case_					:		Union[str, Any] 					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    repo_type="""dataset"""			)				,    """r"""			)			)
										snake_case_					:		Union[str, Any] 					=					{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
										snake_case_					:		Dict 					=					idalabel
										snake_case_					:		Optional[int] 					=					{v: k for k, v in idalabel.items()}

					snake_case_					:		Dict 					=					img_size
					snake_case_					:		int 					=					num_classes
					snake_case_					:		str 					=					embed_dim
					snake_case_					:		Dict 					=					depths
					snake_case_					:		int 					=					num_heads
					snake_case_					:		Optional[Any] 					=					window_size

					return config







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					if "patch_embed.proj" in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""patch_embed.proj"""				,    """embeddings.patch_embeddings.projection"""			)
					if "patch_embed.norm" in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""patch_embed.norm"""				,    """embeddings.norm"""			)
					if "layers" in name:
										snake_case_					:		List[str] 					=					"""encoder.""" + name
					if "attn.proj" in name:
										snake_case_					:		Dict 					=					name.replace("""attn.proj"""				,    """attention.output.dense"""			)
					if "attn" in name:
										snake_case_					:		Dict 					=					name.replace("""attn"""				,    """attention.self"""			)
					if "norm1" in name:
										snake_case_					:		List[Any] 					=					name.replace("""norm1"""				,    """layernorm_before"""			)
					if "norm2" in name:
										snake_case_					:		Dict 					=					name.replace("""norm2"""				,    """layernorm_after"""			)
					if "mlp.fc1" in name:
										snake_case_					:		int 					=					name.replace("""mlp.fc1"""				,    """intermediate.dense"""			)
					if "mlp.fc2" in name:
										snake_case_					:		List[Any] 					=					name.replace("""mlp.fc2"""				,    """output.dense"""			)
					if "q_bias" in name:
										snake_case_					:		Optional[Any] 					=					name.replace("""q_bias"""				,    """query.bias"""			)
					if "k_bias" in name:
										snake_case_					:		List[Any] 					=					name.replace("""k_bias"""				,    """key.bias"""			)
					if "v_bias" in name:
										snake_case_					:		int 					=					name.replace("""v_bias"""				,    """value.bias"""			)
					if "cpb_mlp" in name:
										snake_case_					:		str 					=					name.replace("""cpb_mlp"""				,    """continuous_position_bias_mlp"""			)
					if name == "norm.weight":
										snake_case_					:		Tuple 					=					"""layernorm.weight"""
					if name == "norm.bias":
										snake_case_					:		int 					=					"""layernorm.bias"""

					if "head" in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""head"""				,    """classifier"""			)
					else:
										snake_case_					:		Optional[Any] 					=					"""swinv2.""" + name

					return name







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					for key in orig_state_dict.copy().keys():
										snake_case_					:		Tuple 					=					orig_state_dict.pop(SCREAMING_SNAKE_CASE__			)

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

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

					return orig_state_dict







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					timm.create_model(SCREAMING_SNAKE_CASE__				,    pretrained=SCREAMING_SNAKE_CASE__			)
					timm_model.eval()

					snake_case_					:		Any 					=					get_swinva_config(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		List[str] 					=					SwinvaForImageClassification(SCREAMING_SNAKE_CASE__			)
					model.eval()

					snake_case_					:		Any 					=					convert_state_dict(timm_model.state_dict()				,    SCREAMING_SNAKE_CASE__			)
					model.load_state_dict(SCREAMING_SNAKE_CASE__			)

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

					snake_case_					:		Optional[Any] 					=					AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""				,    """-"""			)			)			)
					snake_case_					:		Dict 					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,    stream=SCREAMING_SNAKE_CASE__			).raw			)
					snake_case_					:		Optional[Any] 					=					image_processor(images=SCREAMING_SNAKE_CASE__				,    return_tensors="""pt"""			)

					snake_case_					:		Optional[int] 					=					timm_model(inputs["""pixel_values"""]			)
					snake_case_					:		List[str] 					=					model(**SCREAMING_SNAKE_CASE__			).logits

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

					print(f'Saving model {swinva_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__			)

					model.push_to_hub(
					    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)				,    organization="""nandwalritik"""				,    commit_message="""Add model"""				,    )


if __name__ == "__main__":
			a_				   =					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--swinv2_name''',
			    default='''swinv2_tiny_patch4_window8_256''',
			    type=str,
			    help='''Name of the Swinv2 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_				   =					parser.parse_args()
			convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""upernet"""







						def __init__(self   ,		lowercase__=None   ,		lowercase__=5_12   ,		lowercase__=0.02   ,		lowercase__=[1, 2, 3, 6]   ,		lowercase__=True   ,		lowercase__=0.4   ,		lowercase__=3_84   ,		lowercase__=2_56   ,		lowercase__=1   ,		lowercase__=False   ,		lowercase__=2_55   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )

											if backbone_config is None:
																logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
																snake_case_					:		List[Any] 					=					CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
											elif isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		Tuple 					=					backbone_config.get("""model_type"""       )
																snake_case_					:		List[str] 					=					CONFIG_MAPPING[backbone_model_type]
																snake_case_					:		List[Any] 					=					config_class.from_dict(lowercase__       )

											snake_case_					:		List[Any] 					=					backbone_config
											snake_case_					:		Optional[Any] 					=					hidden_size
											snake_case_					:		Any 					=					initializer_range
											snake_case_					:		str 					=					pool_scales
											snake_case_					:		Dict 					=					use_auxiliary_head
											snake_case_					:		str 					=					auxiliary_loss_weight
											snake_case_					:		List[str] 					=					auxiliary_in_channels
											snake_case_					:		Optional[Any] 					=					auxiliary_channels
											snake_case_					:		Any 					=					auxiliary_num_convs
											snake_case_					:		List[Any] 					=					auxiliary_concat_input
											snake_case_					:		List[str] 					=					loss_ignore_index







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					copy.deepcopy(self.__dict__       )
											snake_case_					:		Union[str, Any] 					=					self.backbone_config.to_dict()
											snake_case_					:		Any 					=					self.__class__.model_type
											return output




