



"""simple docstring"""


import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__A   :							Any													=   (720, 1280)  # Height, Width
__A   :							Optional[int]													=   (0.4, 0.6)  # if height or width lower than this scale, drop it.
__A   :							int													=   1 / 100
__A   :							int													=   ''''''
__A   :							str													=   ''''''
__A   :							int													=   ''''''
__A   :							List[str]													=   250







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    get_dataset(_UpperCAmelCase					,     _UpperCAmelCase				)
				for index in range(_UpperCAmelCase				):
								_UpperCAmelCase					=    random.sample(range(len(_UpperCAmelCase				)				)					,     4				)
								_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    update_image_and_anno(
								    _UpperCAmelCase					,     _UpperCAmelCase					,     _UpperCAmelCase					,     _UpperCAmelCase					,     _UpperCAmelCase					,     filter_scale=_UpperCAmelCase					,     )

								# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
								_UpperCAmelCase					=    random_chars(32				)
								_UpperCAmelCase					=    path.split(os.sep				)[-1].rsplit('''.'''					,     1				)[0]
								_UpperCAmelCase					=    f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
								cva.imwrite(f'{file_root}.jpg'					,     _UpperCAmelCase					,     [cva.IMWRITE_JPEG_QUALITY, 85]				)
								print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'				)
								_UpperCAmelCase					=    []
								for anno in new_annos:
												_UpperCAmelCase					=    anno[3] - anno[1]
												_UpperCAmelCase					=    anno[4] - anno[2]
												_UpperCAmelCase					=    anno[1] + width / 2
												_UpperCAmelCase					=    anno[2] + height / 2
												_UpperCAmelCase					=    f'{anno[0]} {x_center} {y_center} {width} {height}'
												annos_list.append(_UpperCAmelCase				)
								with open(f'{file_root}.txt'					,     '''w'''				) as outfile:
												outfile.write('''\n'''.join(line for line in annos_list				)				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				_UpperCAmelCase					=    []
				_UpperCAmelCase					=    []
				for label_file in glob.glob(os.path.join(_UpperCAmelCase					,     '''*.txt'''				)				):
								_UpperCAmelCase					=    label_file.split(os.sep				)[-1].rsplit('''.'''					,     1				)[0]
								with open(_UpperCAmelCase				) as in_file:
												_UpperCAmelCase					=    in_file.readlines()
								_UpperCAmelCase					=    os.path.join(_UpperCAmelCase					,     f'{label_name}.jpg'				)

								_UpperCAmelCase					=    []
								for obj_list in obj_lists:
												_UpperCAmelCase					=    obj_list.rstrip('''\n'''				).split(''' '''				)
												_UpperCAmelCase					=    float(obj[1]				) - float(obj[3]				) / 2
												_UpperCAmelCase					=    float(obj[2]				) - float(obj[4]				) / 2
												_UpperCAmelCase					=    float(obj[1]				) + float(obj[3]				) / 2
												_UpperCAmelCase					=    float(obj[2]				) + float(obj[4]				) / 2

												boxes.append([int(obj[0]				), xmin, ymin, xmax, ymax]				)
								if not boxes:
												continue
								img_paths.append(_UpperCAmelCase				)
								labels.append(_UpperCAmelCase				)
				return img_paths, labels







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						List[Any] = 0.0					,     ):






				'''simple docstring'''



				_UpperCAmelCase					=    np.zeros([output_size[0], output_size[1], 3]					,     dtype=np.uinta				)
				_UpperCAmelCase					=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				_UpperCAmelCase					=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				_UpperCAmelCase					=    int(scale_x * output_size[1]				)
				_UpperCAmelCase					=    int(scale_y * output_size[0]				)

				_UpperCAmelCase					=    []
				_UpperCAmelCase					=    []
				for i, index in enumerate(_UpperCAmelCase				):
								_UpperCAmelCase					=    all_img_list[index]
								path_list.append(_UpperCAmelCase				)
								_UpperCAmelCase					=    all_annos[index]
								_UpperCAmelCase					=    cva.imread(_UpperCAmelCase				)
								if i == 0:  # top-left
												_UpperCAmelCase					=    cva.resize(_UpperCAmelCase					,     (divid_point_x, divid_point_y)				)
												_UpperCAmelCase					=    img
												for bbox in img_annos:
																_UpperCAmelCase					=    bbox[1] * scale_x
																_UpperCAmelCase					=    bbox[2] * scale_y
																_UpperCAmelCase					=    bbox[3] * scale_x
																_UpperCAmelCase					=    bbox[4] * scale_y
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
								elif i == 1:  # top-right
												_UpperCAmelCase					=    cva.resize(_UpperCAmelCase					,     (output_size[1] - divid_point_x, divid_point_y)				)
												_UpperCAmelCase					=    img
												for bbox in img_annos:
																_UpperCAmelCase					=    scale_x + bbox[1] * (1 - scale_x)
																_UpperCAmelCase					=    bbox[2] * scale_y
																_UpperCAmelCase					=    scale_x + bbox[3] * (1 - scale_x)
																_UpperCAmelCase					=    bbox[4] * scale_y
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
								elif i == 2:  # bottom-left
												_UpperCAmelCase					=    cva.resize(_UpperCAmelCase					,     (divid_point_x, output_size[0] - divid_point_y)				)
												_UpperCAmelCase					=    img
												for bbox in img_annos:
																_UpperCAmelCase					=    bbox[1] * scale_x
																_UpperCAmelCase					=    scale_y + bbox[2] * (1 - scale_y)
																_UpperCAmelCase					=    bbox[3] * scale_x
																_UpperCAmelCase					=    scale_y + bbox[4] * (1 - scale_y)
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
								else:  # bottom-right
												_UpperCAmelCase					=    cva.resize(
												    _UpperCAmelCase					,     (output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
												_UpperCAmelCase					=    img
												for bbox in img_annos:
																_UpperCAmelCase					=    scale_x + bbox[1] * (1 - scale_x)
																_UpperCAmelCase					=    scale_y + bbox[2] * (1 - scale_y)
																_UpperCAmelCase					=    scale_x + bbox[3] * (1 - scale_x)
																_UpperCAmelCase					=    scale_y + bbox[4] * (1 - scale_y)
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)

    # Remove bounding box small than scale of filter
				if filter_scale > 0:
								_UpperCAmelCase					=    [
								    anno
								    for anno in new_anno
								    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
								]

				return output_img, new_anno, path_list[0]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				assert number_char > 1, "The number of character should greater than 1"
				_UpperCAmelCase					=    ascii_lowercase + digits
				return "".join(random.choice(_UpperCAmelCase				) for _ in range(_UpperCAmelCase				)				)


if __name__ == "__main__":
					main()
					print("DONE ✅")








"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     repo_type='''dataset'''				)					,     '''r'''				)				)
								_UpperCAmelCase					=    {int(_SCREAMING_SNAKE_CASE				): 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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase					=    orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

								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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    timm.create_model(_SCREAMING_SNAKE_CASE					,     pretrained=_SCREAMING_SNAKE_CASE				)
				timm_model.eval()

				_UpperCAmelCase					=    get_swin_config(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    SwinForImageClassification(_SCREAMING_SNAKE_CASE				)
				model.eval()

				_UpperCAmelCase					=    convert_state_dict(timm_model.state_dict()					,     _SCREAMING_SNAKE_CASE				)
				model.load_state_dict(_SCREAMING_SNAKE_CASE				)

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

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

				_UpperCAmelCase					=    timm_model(inputs['''pixel_values''']				)
				_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE				).logits

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

				print(f'Saving model {swin_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				)


if __name__ == "__main__":
					__A   :							int										=   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."
					)

					__A   :							Tuple										=   parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



