






import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:
  class 		SCREAMING_SNAKE_CASE__	:

    '''simple docstring'''







    @staticmethod
    def  lowerCamelCase       (      *lowerCamelCase		:			List[str]       ,				**lowerCamelCase		:			Optional[int]							)						->	str:


           """simple docstring"""
           pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  MODEL_FOR_OBJECT_DETECTION_MAPPING


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Tuple							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    ObjectDetectionPipeline(model=lowerCamelCase       ,				image_processor=lowerCamelCase							)
         return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       ,				threshold=0.0							)

         self.assertGreater(len(lowerCamelCase							)       ,				0							)
         for detected_object in outputs:
                self.assertEqual(
                    lowerCamelCase       ,				{
                        """score""": ANY(lowerCamelCase							),
                        """label""": ANY(lowerCamelCase							),
                        """box""": {"""xmin""": ANY(lowerCamelCase							), """ymin""": ANY(lowerCamelCase							), """xmax""": ANY(lowerCamelCase							), """ymax""": ANY(lowerCamelCase							)},
                    }       ,				)

         import datasets

         _UpperCAmelCase							=    datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,				"""image"""       ,				split="""test"""							)

         _UpperCAmelCase							=    [
             Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							),
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             # RGBA
             dataset[0]["""file"""],
             # LA
             dataset[1]["""file"""],
             # L
             dataset[2]["""file"""],
         ]
         _UpperCAmelCase							=    object_detector(lowerCamelCase       ,				threshold=0.0							)

         self.assertEqual(len(lowerCamelCase							)       ,				len(lowerCamelCase							)							)
         for outputs in batch_outputs:
                self.assertGreater(len(lowerCamelCase							)       ,				0							)
                for detected_object in outputs:
                       self.assertEqual(
                           lowerCamelCase       ,				{
                               """score""": ANY(lowerCamelCase							),
                               """label""": ANY(lowerCamelCase							),
                               """box""": {"""xmin""": ANY(lowerCamelCase							), """ymin""": ANY(lowerCamelCase							), """xmax""": ANY(lowerCamelCase							), """ymax""": ANY(lowerCamelCase							)},
                           }       ,				)


  @require_tf
  @unittest.skip("""Object detection not implemented in TF"""							)
  def  lowerCamelCase       (      self		:			int							)						->	str:


         """simple docstring"""
         pass


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


         """simple docstring"""
         _UpperCAmelCase							=    """hf-internal-testing/tiny-detr-mobilenetsv3"""

         _UpperCAmelCase							=    AutoModelForObjectDetection.from_pretrained(lowerCamelCase							)
         _UpperCAmelCase							=    AutoFeatureExtractor.from_pretrained(lowerCamelCase							)
         _UpperCAmelCase							=    ObjectDetectionPipeline(model=lowerCamelCase       ,				feature_extractor=lowerCamelCase							)

         _UpperCAmelCase							=    object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""       ,				threshold=0.0							)

         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                 {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
             ]       ,				)

         _UpperCAmelCase							=    object_detector(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]       ,				threshold=0.0       ,				)

         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 [
                     {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                     {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                 ],
                 [
                     {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                     {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                 ],
             ]       ,				)


  @require_torch
  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    """facebook/detr-resnet-50"""

         _UpperCAmelCase							=    AutoModelForObjectDetection.from_pretrained(lowerCamelCase							)
         _UpperCAmelCase							=    AutoFeatureExtractor.from_pretrained(lowerCamelCase							)
         _UpperCAmelCase							=    ObjectDetectionPipeline(model=lowerCamelCase       ,				feature_extractor=lowerCamelCase							)

         _UpperCAmelCase							=    object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""							)
         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                 {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                 {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                 {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                 {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
             ]       ,				)

         _UpperCAmelCase							=    object_detector(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]							)
         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 [
                     {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                     {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                     {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                     {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                     {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                 ],
                 [
                     {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                     {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                     {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                     {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                     {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                 ],
             ]       ,				)


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


         """simple docstring"""
         _UpperCAmelCase							=    """facebook/detr-resnet-50"""

         _UpperCAmelCase							=    pipeline("""object-detection"""       ,				model=lowerCamelCase							)

         _UpperCAmelCase							=    object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""							)
         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                 {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                 {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                 {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                 {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
             ]       ,				)

         _UpperCAmelCase							=    object_detector(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]							)
         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 [
                     {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                     {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                     {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                     {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                     {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                 ],
                 [
                     {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                     {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                     {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                     {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                     {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
                 ],
             ]       ,				)


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


         """simple docstring"""
         _UpperCAmelCase							=    0.9985
         _UpperCAmelCase							=    """facebook/detr-resnet-50"""

         _UpperCAmelCase							=    pipeline("""object-detection"""       ,				model=lowerCamelCase							)

         _UpperCAmelCase							=    object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""       ,				threshold=lowerCamelCase							)
         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                 {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
             ]       ,				)





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


         """simple docstring"""
         _UpperCAmelCase							=    """Narsil/layoutlmv3-finetuned-funsd"""
         _UpperCAmelCase							=    0.9993

         _UpperCAmelCase							=    pipeline("""object-detection"""       ,				model=lowerCamelCase       ,				threshold=lowerCamelCase							)

         _UpperCAmelCase							=    object_detector(
             """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""							)
         self.assertEqual(
             nested_simplify(lowerCamelCase       ,				decimals=4							)       ,				[
                 {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
                 {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
             ]       ,				)







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 ✅''')