



'''simple docstring'''


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

import cva
import numpy as np

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







def 				__lowerCamelCase				(   )       ->	None:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  get_dataset(A__    ,						A__    )
   for index in range(A__    ):
      UpperCamelCase					=  random.sample(range(len(A__    )    )    ,						4    )
      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  update_image_and_anno(
          A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						filter_scale=A__    ,						)

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







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	tuple[list, list]:






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







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__ = 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(A__    ):
      UpperCamelCase					=  all_img_list[index]
      path_list.append(A__    )
      UpperCamelCase					=  all_annos[index]
      UpperCamelCase					=  cva.imread(A__    )
      if i == 0:  # top-left
         UpperCamelCase					=  cva.resize(A__    ,						(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(A__    ,						(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(A__    ,						(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(
             A__    ,						(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 				__lowerCamelCase				(   A__    )       ->	str:






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


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


'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
						import torch


if is_vision_available():
						from PIL import Image

						from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       ViTImageProcessor if is_vision_available() else None



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  (3, 32, 128)
					lowerCAmelCase   					=  tempfile.mkdtemp()

					# fmt: off
					lowerCAmelCase   					=  ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
					# fmt: on
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					with open(self.vocab_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write(json.dumps(__lowerCAmelCase) + """\n""")

					lowerCAmelCase   					=  {
					    """do_normalize""": False,
					    """do_resize""": True,
					    """image_processor_type""": """ViTImageProcessor""",
					    """resample""": 3,
					    """size""": {"""height""": 32, """width""": 128},
					}
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    __lowerCAmelCase)
					with open(self.image_processor_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							json.dump(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return ViTImageProcessor.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)

					lowerCAmelCase   					=  Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1))

					return image_input



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)
					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(self.tmpdirname       ,    use_fast=__lowerCAmelCase)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)

					lowerCAmelCase   					=  self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)""")
					lowerCAmelCase   					=  self.get_image_processor(do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(
					    self.tmpdirname       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor_add_kwargs.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""np""")

					for key in input_image_proc.keys():
							self.assertAlmostEqual(input_image_proc[key].sum()       ,    input_processor[key].sum()       ,    delta=1E-2)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer(__lowerCAmelCase)
					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

					self.assertListEqual(list(inputs.keys())       ,    ["""pixel_values""", """labels"""])

					# test if it raises when no input is passed
					with pytest.raises(__lowerCAmelCase):
							processor()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					lowerCAmelCase   					=  processor.char_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  tokenizer.batch_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  [seq.replace(""" """       ,    """""") for seq in decoded_tok]

					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

					self.assertListEqual(list(inputs.keys())       ,    processor.model_input_names)







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    38)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    50257)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    30522)

					lowerCAmelCase   					=  processor.batch_decode([char_input, bpe_input, wp_input])

					self.assertListEqual(list(results.keys())       ,    ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])




