




'''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      UpperCamelCase_(	snake_case							:       Optional[int]     ):

							'''simple docstring'''






							snake_case_						=      SwinConfig()
							snake_case_						=      swin_name.split("_"     )

							snake_case_						=      name_split[1]
							snake_case_						=      int(name_split[4]     )
							snake_case_						=      int(name_split[3][-1]     )

							if model_size == "tiny":
														snake_case_						=      9_6
														snake_case_						=      (2, 2, 6, 2)
														snake_case_						=      (3, 6, 1_2, 2_4)
							elif model_size == "small":
														snake_case_						=      9_6
														snake_case_						=      (2, 2, 1_8, 2)
														snake_case_						=      (3, 6, 1_2, 2_4)
							elif model_size == "base":
														snake_case_						=      1_2_8
														snake_case_						=      (2, 2, 1_8, 2)
														snake_case_						=      (4, 8, 1_6, 3_2)
							else:
														snake_case_						=      1_9_2
														snake_case_						=      (2, 2, 1_8, 2)
														snake_case_						=      (6, 1_2, 2_4, 4_8)

							if "in22k" in swin_name:
														snake_case_						=      2_1_8_4_1
							else:
														snake_case_						=      1_0_0_0
														snake_case_						=      "huggingface/label-files"
														snake_case_						=      "imagenet-1k-id2label.json"
														snake_case_						=      json.load(open(hf_hub_download(snake_case							,				snake_case							,				repo_type="dataset"     )							,				"r"     )     )
														snake_case_						=      {int(snake_case     ): v for k, v in idalabel.items()}
														snake_case_						=      idalabel
														snake_case_						=      {v: k for k, v in idalabel.items()}

							snake_case_						=      img_size
							snake_case_						=      num_classes
							snake_case_						=      embed_dim
							snake_case_						=      depths
							snake_case_						=      num_heads
							snake_case_						=      window_size

							return config


def      UpperCamelCase_(	snake_case							:       Optional[int]     ):

							'''simple docstring'''






							if "patch_embed.proj" in name:
														snake_case_						=      name.replace("patch_embed.proj"							,				"embeddings.patch_embeddings.projection"     )
							if "patch_embed.norm" in name:
														snake_case_						=      name.replace("patch_embed.norm"							,				"embeddings.norm"     )
							if "layers" in name:
														snake_case_						=      "encoder." + name
							if "attn.proj" in name:
														snake_case_						=      name.replace("attn.proj"							,				"attention.output.dense"     )
							if "attn" in name:
														snake_case_						=      name.replace("attn"							,				"attention.self"     )
							if "norm1" in name:
														snake_case_						=      name.replace("norm1"							,				"layernorm_before"     )
							if "norm2" in name:
														snake_case_						=      name.replace("norm2"							,				"layernorm_after"     )
							if "mlp.fc1" in name:
														snake_case_						=      name.replace("mlp.fc1"							,				"intermediate.dense"     )
							if "mlp.fc2" in name:
														snake_case_						=      name.replace("mlp.fc2"							,				"output.dense"     )

							if name == "norm.weight":
														snake_case_						=      "layernorm.weight"
							if name == "norm.bias":
														snake_case_						=      "layernorm.bias"

							if "head" in name:
														snake_case_						=      name.replace("head"							,				"classifier"     )
							else:
														snake_case_						=      "swin." + name

							return name


def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Any     ):

							'''simple docstring'''






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

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

																					if "weight" in key:
																												snake_case_						=      val[:dim, :]
																												snake_case_						=      val[
																												    dim : dim * 2, :
																												]
																												snake_case_						=      val[-dim:, :]
																					else:
																												snake_case_						=      val[
																												    :dim
																												]
																												snake_case_						=      val[
																												    dim : dim * 2
																												]
																												snake_case_						=      val[
																												    -dim:
																												]
														else:
																					snake_case_						=      val

							return orig_state_dict


def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       str     ):

							'''simple docstring'''






							snake_case_						=      timm.create_model(snake_case							,				pretrained=snake_case     )
							timm_model.eval()

							snake_case_						=      get_swin_config(snake_case     )
							snake_case_						=      SwinForImageClassification(snake_case     )
							model.eval()

							snake_case_						=      convert_state_dict(timm_model.state_dict()							,				snake_case     )
							model.load_state_dict(snake_case     )

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

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

							snake_case_						=      timm_model(inputs["pixel_values"]     )
							snake_case_						=      model(**snake_case     ).logits

							assert torch.allclose(snake_case							,				snake_case							,				atol=1e-3     )

							print(f'Saving model {swin_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 __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= 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."
		)

		_SCREAMING_SNAKE_CASE							:   Dict      		= parser.parse_args()
		convert_swin_checkpoint(args.swin_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    :							Any      =					{
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[Any]      =					["ChineseCLIPFeatureExtractor"]
					lowercase    :							List[Any]      =					["ChineseCLIPImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Any      =					[
					    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ChineseCLIPModel",
					    "ChineseCLIPPreTrainedModel",
					    "ChineseCLIPTextModel",
					    "ChineseCLIPVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_chinese_clip import (
					    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ChineseCLIPConfig,
					    ChineseCLIPOnnxConfig,
					    ChineseCLIPTextConfig,
					    ChineseCLIPVisionConfig,
					)
					from .processing_chinese_clip import ChineseCLIPProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_chinese_clip import (
										    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ChineseCLIPModel,
										    ChineseCLIPPreTrainedModel,
										    ChineseCLIPTextModel,
										    ChineseCLIPVisionModel,
										)

else:
					import sys

					lowercase    :							List[Any]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

