
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		snake_case  :       Tuple     			=						SwinvaConfig()
		snake_case  :       int     			=						swinva_name.split('''_'''			)

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

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

		if "to" in swinva_name:
				snake_case  :       int     			=						(12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				snake_case  :       Union[str, Any]     			=						2_1841
				snake_case  :       Optional[Any]     			=						'''huggingface/label-files'''
				snake_case  :       Tuple     			=						'''imagenet-22k-id2label.json'''
				snake_case  :       Tuple     			=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       repo_type='''dataset'''			) ,       '''r'''			)			)
				snake_case  :       str     			=						{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
				snake_case  :       Optional[int]     			=						idalabel
				snake_case  :       Optional[int]     			=						{v: k for k, v in idalabel.items()}

		else:
				snake_case  :       str     			=						1000
				snake_case  :       List[Any]     			=						'''huggingface/label-files'''
				snake_case  :       List[Any]     			=						'''imagenet-1k-id2label.json'''
				snake_case  :       Dict     			=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       repo_type='''dataset'''			) ,       '''r'''			)			)
				snake_case  :       Any     			=						{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
				snake_case  :       List[Any]     			=						idalabel
				snake_case  :       Union[str, Any]     			=						{v: k for k, v in idalabel.items()}

		snake_case  :       int     			=						img_size
		snake_case  :       int     			=						num_classes
		snake_case  :       int     			=						embed_dim
		snake_case  :       int     			=						depths
		snake_case  :       List[str]     			=						num_heads
		snake_case  :       Tuple     			=						window_size

		return config




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Union[str, Any]:


		'''simple docstring'''


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

		if "head" in name:
				snake_case  :       str     			=						name.replace('''head''' ,       '''classifier'''			)
		else:
				snake_case  :       Dict     			=						'''swinv2.''' + name

		return name




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				snake_case  :       str     			=						orig_state_dict.pop(SCREAMING_SNAKE_CASE__			)

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

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

		return orig_state_dict




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


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

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

		snake_case  :       Optional[int]     			=						convert_state_dict(timm_model.state_dict() ,       SCREAMING_SNAKE_CASE__			)
		model.load_state_dict(SCREAMING_SNAKE_CASE__			)

		snake_case  :       Union[str, Any]     			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

		snake_case  :       Union[str, Any]     			=						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__":
						lowercase__						       =      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."
						)

						lowercase__						       =      parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


