"""simple docstring"""






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

import cva
__lowerCAmelCase					: Dict        =""""""
__lowerCAmelCase					: Optional[int]        =""""""
__lowerCAmelCase					: Union[str, Any]        =""""""
__lowerCAmelCase					: Optional[int]        =1  # (0 is vertical, 1 is horizontal)


def     UpperCAmelCase__ (       )				->					None:
      '''simple docstring'''


      lowercase							,							lowercase														=			get_dataset(lowerCAmelCase__      ,	lowerCAmelCase__	)
      print("""Processing..."""	)
      lowercase							,							lowercase							,							lowercase														=			update_image_and_anno(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)

      for index, image in enumerate(lowerCAmelCase__	):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            lowercase														=			random_chars(3_2	)
            lowercase														=			paths[index].split(os.sep	)[-1].rsplit("""."""      ,	1	)[0]
            lowercase														=			f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
            cva.imwrite(f'/{file_root}.jpg'      ,	lowerCAmelCase__      ,	[cva.IMWRITE_JPEG_QUALITY, 8_5]	)
            print(f'Success {index+1}/{len(lowerCAmelCase__	)} with {file_name}'	)
            lowercase														=			[]
            for anno in new_annos[index]:
                  lowercase														=			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     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :str	)				->					tuple[list, list]:
      '''simple docstring'''


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

            lowercase														=			[]
            for obj_list in obj_lists:
                  lowercase														=			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     UpperCAmelCase__ (       lowerCAmelCase__  :list      ,	lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int = 1	)				->					tuple[list, list, list]:
      '''simple docstring'''


      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			[]
      for idx in range(len(lowerCAmelCase__	)	):
            lowercase														=			[]
            lowercase														=			img_list[idx]
            path_list.append(lowerCAmelCase__	)
            lowercase														=			anno_list[idx]
            lowercase														=			cva.imread(lowerCAmelCase__	)
            if flip_type == 1:
                  lowercase														=			cva.flip(lowerCAmelCase__      ,	lowerCAmelCase__	)
                  for bbox in img_annos:
                        lowercase														=			1 - bbox[1]
                        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]	)
            elif flip_type == 0:
                  lowercase														=			cva.flip(lowerCAmelCase__      ,	lowerCAmelCase__	)
                  for bbox in img_annos:
                        lowercase														=			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     UpperCAmelCase__ (       lowerCAmelCase__  :int = 3_2	)				->					str:
      '''simple docstring'''


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


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






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.roberta.modeling_flax_roberta import (
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaModel,
       )





class 				_A		( unittest.TestCase			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=32	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=4	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_attention_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_choices

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size		)

            lowercase														=			None
            if self.use_attention_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase														=			None
            if self.use_token_type_ids:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.type_vocab_size		)

            lowercase														=			RobertaConfig(
                vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, num_attention_heads=self.num_attention_heads	, intermediate_size=self.intermediate_size	, hidden_act=self.hidden_act	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, max_position_embeddings=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, is_decoder=__lowerCAmelCase	, initializer_range=self.initializer_range	, )

            return config, input_ids, token_type_ids, attention_mask

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs
            lowercase														=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs

            lowercase														=			True
            lowercase														=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2		)

            return (
                config,
                input_ids,
                token_type_ids,
                encoder_hidden_states,
                encoder_attention_mask,
            )






@require_flax
class 				_A		( lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Optional[int]													=			True

      snake_case__    :     List[str]													=			(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			FlaxRobertaModelTester(self		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_class_name in self.all_model_classes:
                  lowercase														=			model_class_name.from_pretrained("""roberta-base"""	, from_pt=__lowerCAmelCase		)
                  lowercase														=			model(np.ones((1, 1)		)		)
                  self.assertIsNotNone(__lowerCAmelCase		)



