


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

import cva
import numpy as np

# Parrameters
__snake_case   							=						(7_2_0, 1_2_8_0)  # Height, Width
__snake_case   							=						(0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case   							=						1 / 1_0_0
__snake_case   							=						''''''
__snake_case   							=						''''''
__snake_case   							=						''''''
__snake_case   							=						2_5_0






def 		_A		(		)					-> None:


     """simple docstring"""


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

          # 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'''					,  _lowercase					,  [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(_lowercase )
          with open(f'''{file_root}.txt'''					,  'w' ) as outfile:
               outfile.write('\n'.join(line for line in annos_list ) )






def 		_A		(		_lowercase					,  _lowercase )					-> tuple[list, list]:


     """simple docstring"""


     __UpperCamelCase =			[]
     __UpperCamelCase =			[]
     for label_file in glob.glob(os.path.join(_lowercase					,  '*.txt' ) ):
          __UpperCamelCase =			label_file.split(os.sep )[-1].rsplit('.'					,  1 )[0]
          with open(_lowercase ) as in_file:
               __UpperCamelCase =			in_file.readlines()
          __UpperCamelCase =			os.path.join(_lowercase					,  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(_lowercase )
          labels.append(_lowercase )
     return img_paths, labels






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase = 0.0					,  )					-> tuple[list, list, str]:


     """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(_lowercase ):
          __UpperCamelCase =			all_img_list[index]
          path_list.append(_lowercase )
          __UpperCamelCase =			all_annos[index]
          __UpperCamelCase =			cva.imread(_lowercase )
          if i == 0:  # top-left
               __UpperCamelCase =			cva.resize(_lowercase					,  (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(_lowercase					,  (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(_lowercase					,  (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(
                   _lowercase					,  (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 		_A		(		_lowercase )					-> str:


     """simple docstring"""


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


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





'''simple docstring'''







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
      import tensorflow as tf

      from transformers import TFBlipTextModel
      from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class        A__   :



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=12		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=32		,					UpperCamelCase__=2		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=0		,					UpperCamelCase__=None		,					)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   seq_length
           A_						       =   is_training
           A_						       =   use_input_mask
           A_						       =   use_labels
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   projection_dim
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   intermediate_size
           A_						       =   dropout
           A_						       =   attention_dropout
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   scope
           A_						       =   bos_token_id



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

           A_						       =   None
           if self.use_input_mask:
                  A_						       =   random_attention_mask([self.batch_size, self.seq_length]							)

           if input_mask is not None:
                  A_						       =   input_mask.numpy()
                  A_       ,      A_						       =   input_mask.shape
                  A_						       =   np.random.randint(1		,					seq_length - 1		,					size=(batch_size,)							)
                  for batch_idx, start_index in enumerate(UpperCamelCase__							):
                         A_						       =   1
                         A_						       =   0

           A_						       =   self.get_config()

           return config, input_ids, tf.convert_to_tensor(UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           return BlipTextConfig(
               vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					projection_dim=self.projection_dim		,					num_hidden_layers=self.num_hidden_layers		,					num_attention_heads=self.num_attention_heads		,					intermediate_size=self.intermediate_size		,					dropout=self.dropout		,					attention_dropout=self.attention_dropout		,					max_position_embeddings=self.max_position_embeddings		,					initializer_range=self.initializer_range		,					bos_token_id=self.bos_token_id		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           A_						       =   TFBlipTextModel(config=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					training=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					training=UpperCamelCase__							)
           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)							)
           self.parent.assertEqual(result.pooler_output.shape		,					(self.batch_size, self.hidden_size)							)







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           A_       ,      A_       ,      A_						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_tf
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(TFBlipTextModel,) if is_tf_available() else ()
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   BlipTextModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					hidden_size=37							)



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""Blip does not use inputs_embeds"""							)
    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           pass



    @slow
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   TFBlipTextModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__=True							)    ->   str:





           '''simple docstring'''
           super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__							)
