


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

import cva
A				:       Tuple      =  ''
A				:       int      =  ''
A				:       Optional[Any]      =  ''
A				:       Tuple      =  1  # (0 is vertical, 1 is horizontal)





def 						_lowerCAmelCase							(     )    ->						None:


						'''simple docstring'''






						__snake_case				=		get_dataset(_lowerCamelCase   ,						_lowerCamelCase			)
						print("Processing..."			)
						__snake_case				=		update_image_and_anno(_lowerCamelCase   ,						_lowerCamelCase   ,						_lowerCamelCase			)

						for index, image in enumerate(_lowerCamelCase			):
												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												__snake_case				=		random_chars(32			)
												__snake_case				=		paths[index].split(os.sep			)[-1].rsplit("."   ,						1			)[0]
												__snake_case				=		F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
												cva.imwrite(F'''/{file_root}.jpg'''   ,						_lowerCamelCase   ,						[cva.IMWRITE_JPEG_QUALITY, 85]			)
												print(F'''Success {index+1}/{len(_lowerCamelCase			)} with {file_name}'''			)
												__snake_case				=		[]
												for anno in new_annos[index]:
																		__snake_case				=		F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
																		annos_list.append(_lowerCamelCase			)
												with open(F'''/{file_root}.txt'''   ,						"w"			) as outfile:
																		outfile.write("\n".join(line for line in annos_list			)			)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						tuple[list, list]:


						'''simple docstring'''






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

												__snake_case				=		[]
												for obj_list in obj_lists:
																		__snake_case				=		obj_list.rstrip("\n"			).split(" "			)
																		boxes.append(
																		    [
																		        int(obj[0]			),
																		        float(obj[1]			),
																		        float(obj[2]			),
																		        float(obj[3]			),
																		        float(obj[4]			),
																		    ]			)
												if not boxes:
																		continue
												img_paths.append(_lowerCamelCase			)
												labels.append(_lowerCamelCase			)
						return img_paths, labels





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = 1			)    ->						tuple[list, list, list]:


						'''simple docstring'''






						__snake_case				=		[]
						__snake_case				=		[]
						__snake_case				=		[]
						for idx in range(len(_lowerCamelCase			)			):
												__snake_case				=		[]
												__snake_case				=		img_list[idx]
												path_list.append(_lowerCamelCase			)
												__snake_case				=		anno_list[idx]
												__snake_case				=		cva.imread(_lowerCamelCase			)
												if flip_type == 1:
																		__snake_case				=		cva.flip(_lowerCamelCase   ,						_lowerCamelCase			)
																		for bbox in img_annos:
																								__snake_case				=		1 - bbox[1]
																								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
												elif flip_type == 0:
																		__snake_case				=		cva.flip(_lowerCamelCase   ,						_lowerCamelCase			)
																		for bbox in img_annos:
																								__snake_case				=		1 - bbox[2]
																								new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
												new_annos_lists.append(_lowerCamelCase			)
												new_imgs_list.append(_lowerCamelCase			)
						return new_imgs_list, new_annos_lists, path_list





def 						_lowerCAmelCase							(     _lowerCAmelCase = 32			)    ->						str:


						'''simple docstring'''






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


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












'''simple docstring'''






import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

			if "_quant" in model_name:
						raise ValueError("""Quantized models are not supported.""" )

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__snake_case					:      Tuple							      =     1001
			__snake_case					:      Any							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[Any]							      =     """huggingface/label-files"""
			__snake_case					:      List[Any]							      =     json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Dict							      =     {int(_lowerCamelCase ) + 1: v for k, v in idalabel.items()}
			__snake_case					:      List[str]							      =     """background"""
			__snake_case					:      List[str]							      =     idalabel
			__snake_case					:      List[Any]							      =     {v: k for k, v in idalabel.items()}

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     MobileNetVaForImageClassification(_lowerCamelCase ).eval()

			# Load weights from TensorFlow checkpoint
			load_tf_weights_in_mobilenet_va(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			__snake_case					:      Optional[int]							      =     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}    ,	size={"""shortest_edge""": config.image_size + 32}    ,	)
			__snake_case					:      Tuple							      =     image_processor(images=prepare_img()    ,	return_tensors="""pt""" )
			__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
			__snake_case					:      List[Any]							      =     outputs.logits

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]    ,	_lowerCamelCase    ,	atol=1E-4 )

			Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
			model.save_pretrained(_lowerCamelCase )
			print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
			image_processor.save_pretrained(_lowerCamelCase )

			if push_to_hub:
						print("""Pushing to the hub...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="mobilenet_v1_1.0_224",
	    type=str,
	    help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	__UpperCamelCase					    =	parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


