






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ImageGPTImageProcessor
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple=7       ,				lowerCamelCase		:			Any=3       ,				lowerCamelCase		:			Tuple=18       ,				lowerCamelCase		:			Dict=30       ,				lowerCamelCase		:			Union[str, Any]=400       ,				lowerCamelCase		:			Union[str, Any]=True       ,				lowerCamelCase		:			Optional[Any]=None       ,				lowerCamelCase		:			Any=True       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    size if size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    do_normalize





  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         return {
             # here we create 2 clusters for the sake of simplicity
             "clusters": np.asarray(
                 [
                     [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                     [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
                 ]							),
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
         }



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ImageGPTImageProcessor if is_vision_available() else None


  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ImageGPTImageProcessingTester(self							)


  @property
  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[Any]:


         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()


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


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""clusters"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""size"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_normalize"""							)							)


  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,				{"""height""": 18, """width""": 18}							)

         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42							)
         self.assertEqual(image_processor.size       ,				{"""height""": 42, """width""": 42}							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         _UpperCAmelCase							=    json.loads(image_processor.to_json_string()							)
         for key, value in self.image_processor_dict.items():
                if key == "clusters":
                       self.assertTrue(np.array_equal(lowerCamelCase       ,				obj[key]							)							)
                else:
                       self.assertEqual(obj[key]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
                _UpperCAmelCase							=    os.path.join(lowerCamelCase       ,				"""image_processor.json"""							)
                image_processor_first.to_json_file(lowerCamelCase							)
                _UpperCAmelCase							=    self.image_processing_class.from_json_file(lowerCamelCase							).to_dict()

         _UpperCAmelCase							=    image_processor_first.to_dict()
         for key, value in image_processor_first.items():
                if key == "clusters":
                       self.assertTrue(np.array_equal(lowerCamelCase       ,				image_processor_second[key]							)							)
                else:
                       self.assertEqual(image_processor_first[key]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
                image_processor_first.save_pretrained(lowerCamelCase							)
                _UpperCAmelCase							=    self.image_processing_class.from_pretrained(lowerCamelCase							).to_dict()

         _UpperCAmelCase							=    image_processor_first.to_dict()
         for key, value in image_processor_first.items():
                if key == "clusters":
                       self.assertTrue(np.array_equal(lowerCamelCase       ,				image_processor_second[key]							)							)
                else:
                       self.assertEqual(image_processor_first[key]       ,				lowerCamelCase							)





  @unittest.skip("""ImageGPT requires clusters at initialization"""							)
  def  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         pass




def       _SCREAMING_SNAKE_CASE (							)				->						Dict:
       _UpperCAmelCase							=    load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,      split="""test""" )

       _UpperCAmelCase							=    Image.open(dataset[4]["""file"""] )
       _UpperCAmelCase							=    Image.open(dataset[5]["""file"""] )

       _UpperCAmelCase							=    [imagea, imagea]

       return images





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

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""							)

         _UpperCAmelCase							=    prepare_images()

         # test non-batched
         _UpperCAmelCase							=    image_processing(images[0]       ,				return_tensors="""pt"""							)

         self.assertIsInstance(encoding.input_ids       ,				torch.LongTensor							)
         self.assertEqual(encoding.input_ids.shape       ,				(1, 1024)							)

         _UpperCAmelCase							=    [306, 191, 191]
         self.assertEqual(encoding.input_ids[0, :3].tolist()       ,				lowerCamelCase							)

         # test batched
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							)

         self.assertIsInstance(encoding.input_ids       ,				torch.LongTensor							)
         self.assertEqual(encoding.input_ids.shape       ,				(2, 1024)							)

         _UpperCAmelCase							=    [303, 13, 13]
         self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,				lowerCamelCase							)







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

import cva
import numpy as np

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




def       _SCREAMING_SNAKE_CASE (							)				->						None:
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataset(__snake_case   ,      __snake_case )
       for index in range(__snake_case ):
              _UpperCAmelCase							=    random.sample(range(len(__snake_case ) )   ,      4 )
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    update_image_and_anno(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      filter_scale=__snake_case   ,      )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              _UpperCAmelCase							=    random_chars(3_2 )
              _UpperCAmelCase							=    path.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              _UpperCAmelCase							=    f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""   ,      __snake_case   ,      [cva.IMWRITE_JPEG_QUALITY, 8_5] )
              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(__snake_case )
              with open(f"""{file_root}.txt"""   ,      """w""" ) as outfile:
                     outfile.write("""\n""".join(line for line in annos_list ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						tuple[list, list]:
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for label_file in glob.glob(os.path.join(__snake_case   ,      """*.txt""" ) ):
              _UpperCAmelCase							=    label_file.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              with open(__snake_case ) as in_file:
                     _UpperCAmelCase							=    in_file.readlines()
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      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(__snake_case )
              labels.append(__snake_case )
       return img_paths, labels




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = 0.0   ,      )				->						tuple[list, list, str]:
       _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(__snake_case ):
              _UpperCAmelCase							=    all_img_list[index]
              path_list.append(__snake_case )
              _UpperCAmelCase							=    all_annos[index]
              _UpperCAmelCase							=    cva.imread(__snake_case )
              if i == 0:  # top-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (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(__snake_case   ,      (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(__snake_case   ,      (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(
                         __snake_case   ,      (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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						str:
       assert number_char > 1, "The number of character should greater than 1"
       _UpperCAmelCase							=    ascii_lowercase + digits
       return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )


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