

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

import cva
import numpy as np

# Parrameters
lowercase_             =     (7_2_0, 1_2_8_0)  # Height, Width
lowercase_             =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
lowercase_             =     1 / 1_0_0
lowercase_             =     ''
lowercase_             =     ''
lowercase_             =     ''
lowercase_             =     2_5_0
def   a   (    )					->   None:





      """simple docstring"""



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

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





      """simple docstring"""



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

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

                  boxes.append([int(obj[0]  ), xmin, ymin, xmax, ymax]  )
            if not boxes:
                  continue
            img_paths.append(A__  )
            labels.append(A__  )
      return img_paths, labels
def   a   (    A__			:     list   ,     A__			:     list   ,     A__			:     list[int]   ,     A__			:     tuple[int, int]   ,     A__			:     tuple[float, float]   ,     A__			:     float = 0.0   ,     )					->   tuple[list, list, str]:





      """simple docstring"""



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

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





      """simple docstring"""



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


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





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase  (      unittest.TestCase							):




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

        _lowercase			 =tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]							,     dtype=tf.intaa							,     )  # J'aime le camembert !"

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        _lowercase			 =tf.TensorShape((1, 10, 768)      )
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice.
        _lowercase			 =tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]							,     dtype=tf.floataa							,     )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,     expected_slice.numpy()							,     atol=1e-4      )      )


