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       snake_case					:




        @staticmethod
        def 	__lowercase(      *a_     :	Union[str, Any]     , **a_     :	int				)->       Tuple:




             """simple docstring"""
             pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       snake_case					(      unittest.TestCase						):
  lowercase_			=     MODEL_FOR_OBJECT_DETECTION_MAPPING




  def 	__lowercase(      self     :	str     , a_     :	List[str]     , a_     :	int     , a_     :	str				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ObjectDetectionPipeline(model=a_     , image_processor=a_				)
       return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




  def 	__lowercase(      self     :	Any     , a_     :	Tuple     , a_     :	List[str]				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'     , threshold=0.0				)

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

       import datasets

       SCREAMING_SNAKE_CASE__				:				Tuple			=		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     , 'image'     , split='test'				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		[
           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'],
       ]
       SCREAMING_SNAKE_CASE__				:				str			=		object_detector(a_     , threshold=0.0				)

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




  @require_tf
  @unittest.skip('Object detection not implemented in TF'				)
  def 	__lowercase(      self     :	Dict				)->       Any:




       """simple docstring"""
       pass




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		'hf-internal-testing/tiny-detr-mobilenetsv3'

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		AutoModelForObjectDetection.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		AutoFeatureExtractor.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		ObjectDetectionPipeline(model=a_     , feature_extractor=a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     , threshold=0.0				)

       self.assertEqual(
           nested_simplify(a_     , 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}},
           ]     , )

       SCREAMING_SNAKE_CASE__				:				List[str]			=		object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ]     , threshold=0.0     , )

       self.assertEqual(
           nested_simplify(a_     , 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 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'facebook/detr-resnet-50'

       SCREAMING_SNAKE_CASE__				:				List[str]			=		AutoModelForObjectDetection.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		AutoFeatureExtractor.from_pretrained(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ObjectDetectionPipeline(model=a_     , feature_extractor=a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'				)
       self.assertEqual(
           nested_simplify(a_     , 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}},
           ]     , )

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ]				)
       self.assertEqual(
           nested_simplify(a_     , 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 	__lowercase(      self     :	str				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'facebook/detr-resnet-50'

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		pipeline('object-detection'     , model=a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'				)
       self.assertEqual(
           nested_simplify(a_     , 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}},
           ]     , )

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ]				)
       self.assertEqual(
           nested_simplify(a_     , 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 	__lowercase(      self     :	List[str]				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0.9985
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'facebook/detr-resnet-50'

       SCREAMING_SNAKE_CASE__				:				Any			=		pipeline('object-detection'     , model=a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     , threshold=a_				)
       self.assertEqual(
           nested_simplify(a_     , 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 	__lowercase(      self     :	Optional[Any]				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		'Narsil/layoutlmv3-finetuned-funsd'
       SCREAMING_SNAKE_CASE__				:				str			=		0.9993

       SCREAMING_SNAKE_CASE__				:				List[str]			=		pipeline('object-detection'     , model=a_     , threshold=a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		object_detector(
           'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'				)
       self.assertEqual(
           nested_simplify(a_     , 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 math
import sys







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     try:
          with open(lowercase__     ,  'rb' ) as binary_file:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		binary_file.read()
          for dat in data:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{'0': '0', '1': '1'}
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		'', ''
     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

     for i in range(len(lowercase__ ) ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          SCREAMING_SNAKE_CASE__				:				int			=		lexicon[curr_string]
          result += last_match_id
          SCREAMING_SNAKE_CASE__				:				str			=		last_match_id + '0'

          if math.loga(lowercase__ ).is_integer():
               SCREAMING_SNAKE_CASE__				:				List[str]			=		{}
               for curr_key in list(lowercase__ ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		lexicon.pop(lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		new_lex

          SCREAMING_SNAKE_CASE__				:				Any			=		last_match_id + '1'
          index += 1
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     return result







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		8
     try:
          with open(lowercase__     ,  'wb' ) as opened_file:
               SCREAMING_SNAKE_CASE__				:				Dict			=		[
                   to_write[i : i + byte_length]
                   for i in range(0     ,  len(lowercase__ )     ,  lowercase__ )
               ]

               if len(result_byte_array[-1] ) % byte_length == 0:
                    result_byte_array.append('10000000' )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1] ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(lowercase__     ,  2 ).to_bytes(1     ,  byteorder='big' ) )
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		data_bits[counter:]
     SCREAMING_SNAKE_CASE__				:				int			=		data_bits[counter + 1 :]
     return data_bits







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		read_file_binary(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		remove_prefix(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		decompress_data(lowercase__ )
     write_file_binary(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      compress(sys.argv[1], sys.argv[2])



