


"""simple docstring"""



import argparse
import os
import re


__snake_case             =   """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__snake_case             =   re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__snake_case             =   re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  bool = False			)     ->						int:


    """simple docstring"""





    with open(lowercase						,							"r"						,							encoding="utf-8"			) as f:
        snake_case   :				str				 =   f.read()

    snake_case   :				Any				 =   content.split("\n"			)
    snake_case   :				List[Any]				 =   []
    snake_case   :				Dict				 =   0
    while line_idx < len(lowercase			):
        if _re_intro_mapping.search(lines[line_idx]			) is not None:
            snake_case   :				str				 =   len(re.search(R"^(\s*)\S"						,							lines[line_idx]			).groups()[0]			) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(" " * indent + "("			):
                new_lines.append(lines[line_idx]			)
                line_idx += 1

            snake_case   :				int				 =   []
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    snake_case   :				Tuple				 =   line_idx
                    while not lines[line_idx].startswith(" " * indent + ")"			):
                        line_idx += 1
                    blocks.append("\n".join(lines[start_idx : line_idx + 1]			)			)
                else:
                    blocks.append(lines[line_idx]			)
                line_idx += 1

            # Sort blocks by their identifiers
            snake_case   :				Dict				 =   sorted(lowercase						,							key=lambda lowercase			: _re_identifier.search(lowercase			).groups()[0]			)
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx]			)
            line_idx += 1

    if overwrite:
        with open(lowercase						,							"w"						,							encoding="utf-8"			) as f:
            f.write("\n".join(lowercase			)			)
    elif "\n".join(lowercase			) != content:
        return True







def   __lowerCAmelCase						(						lowercase  :  bool = False			)     ->						Any:


    """simple docstring"""





    snake_case   :				Dict				 =   [os.path.join(lowercase						,							lowercase			) for f in os.listdir(lowercase			) if f.endswith(".py"			)]
    snake_case   :				List[Any]				 =   [sort_auto_mapping(lowercase						,							overwrite=lowercase			) for fname in fnames]

    if not overwrite and any(lowercase			):
        snake_case   :				Tuple				 =   [f for f, d in zip(lowercase						,							lowercase			) if d]
        raise ValueError(
            F'The following files have auto mappings that need sorting: {", ".join(lowercase			)}. Run `make style` to fix'
            " this."			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  __snake_case             =   parser.parse_args()

  sort_all_auto_mappings(not args.check_only)









"""simple docstring"""



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class _lowerCAmelCase    :







     @staticmethod
     def 			lowerCamelCase		(     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

         '''simple docstring'''




         pass







@is_pipeline_test
@require_vision
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				int				 =   [
           {
               "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "candidate_labels": ["cat", "remote", "couch"],
           }
       ]
       return object_detector, examples







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				str				 =   object_detector(examples[0]						,     threshold=0.0						)

       snake_case   :				str				 =   len(UpperCamelCase__						)
       self.assertGreater(UpperCamelCase__						,     0						)
       self.assertEqual(
           UpperCamelCase__						,     [
               {
                   "score": ANY(UpperCamelCase__						),
                   "label": ANY(UpperCamelCase__						),
                   "box": {"xmin": ANY(UpperCamelCase__						), "ymin": ANY(UpperCamelCase__						), "xmax": ANY(UpperCamelCase__						), "ymax": ANY(UpperCamelCase__						)},
               }
               for i in range(UpperCamelCase__						)
           ]						,     )







   @require_tf
   @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       pass







   @require_torch
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Dict				 =   pipeline(
           "zero-shot-object-detection"						,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

       snake_case   :				Optional[Any]				 =   object_detector(
           "./tests/fixtures/tests_samples/COCO/000000039769.png"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
               {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
               {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
               {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
           ]						,     )

       snake_case   :				Dict				 =   object_detector(
           [
               {
                   "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                   "candidate_labels": ["cat", "remote", "couch"],
               }
           ]						,     threshold=0.64						,     )

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                   {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                   {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                   {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                   {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
               ]
           ]						,     )







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Tuple				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
               {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
               {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
           ]						,     )

       snake_case   :				List[Any]				 =   object_detector(
           [
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
               {
                   "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                   "candidate_labels": ["cat", "remote", "couch"],
               },
           ]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
               [
                   {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                   {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                   {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                   {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                   {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
               ],
           ]						,     )







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

       '''simple docstring'''




       pass







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

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   0.2
       snake_case   :				List[str]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				List[Any]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
               {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
           ]						,     )







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

       '''simple docstring'''




       snake_case   :				List[Any]				 =   2
       snake_case   :				Optional[Any]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Optional[int]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     top_k=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     decimals=4						)						,     [
               {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
               {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
           ]						,     )





