

"""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 		_A       :





           @staticmethod
           def 					_lowerCamelCase  (  *SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
              pass


@is_pipeline_test
@require_vision
@require_torch
class 		_A       (			unittest.TestCase					):
      __a								=	MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			pipeline(
             "zero-shot-object-detection"    ,						model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         lowerCamelCase__						=			object_detector(examples[0]    ,						threshold=0.0		)

         lowerCamelCase__						=			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]:
         pass





      @require_torch
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			pipeline(
             "zero-shot-object-detection"    ,						model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

         lowerCamelCase__						=			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": 0.72_35, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.72_18, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.71_84, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.67_48, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.66_56, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.66_14, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.64_56, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 {"score": 0.6_42, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                 {"score": 0.64_19, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
             ]    ,						)

         lowerCamelCase__						=			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": 0.72_35, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.72_18, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.71_84, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.67_48, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.66_56, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.66_14, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.64_56, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                     {"score": 0.6_42, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                     {"score": 0.64_19, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 ]
             ]    ,						)





      @require_torch
      @slow
      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			pipeline("zero-shot-object-detection"		)

         lowerCamelCase__						=			object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"    ,						candidate_labels=["cat", "remote", "couch"]    ,						)
         self.assertEqual(
             nested_simplify(UpperCamelCase_    ,						decimals=4		)    ,						[
                 {"score": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                 {"score": 0.14_74, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                 {"score": 0.12_08, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
             ]    ,						)

         lowerCamelCase__						=			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": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.14_74, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.12_08, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
                 [
                     {"score": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.14_74, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.12_08, "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		)   ->					int:
         pass





      @require_torch
      @slow
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			0.2
         lowerCamelCase__						=			pipeline("zero-shot-object-detection"		)

         lowerCamelCase__						=			object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"    ,						candidate_labels=["cat", "remote", "couch"]    ,						threshold=UpperCamelCase_    ,						)
         self.assertEqual(
             nested_simplify(UpperCamelCase_    ,						decimals=4		)    ,						[
                 {"score": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
             ]    ,						)





      @require_torch
      @slow
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			2
         lowerCamelCase__						=			pipeline("zero-shot-object-detection"		)

         lowerCamelCase__						=			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": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
             ]    ,						)







"""simple docstring"""




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


SCREAMING_SNAKE_CASE_     :			Optional[List[str]]	  =			None
SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE_     :			int	  =			[
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]


@dataclass
class 		_A       :
      __a								=	True
      __a								=	None
      # Automatically constructed
      __a								=	"PIL.Image.Image"
      __a								=	pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
      __a								=	field(default='Image'				,       init=__a				,       repr=__a					)





      def __call__(  self		)   ->					Dict:
         return self.pa_type





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					dict:
         if config.PIL_AVAILABLE:
            import PIL.Image
         else:
            raise ImportError("To support encoding images, please install 'Pillow'."		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": value, "bytes": None}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": None, "bytes": value}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						np.ndarray		):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(SCREAMING_SNAKE_CASE__		)
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						PIL.Image.Image		):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(SCREAMING_SNAKE_CASE__		)
         elif value.get("path"		) is not None and os.path.isfile(value["path"]		):
            # we set "bytes": None to not duplicate the data if they're already available locally
            return {"bytes": None, "path": value.get("path"		)}
         elif value.get("bytes"		) is not None or value.get("path"		) is not None:
            # store the image bytes, and path is used to infer the image format using the file extension
            return {"bytes": value.get("bytes"		), "path": value.get("path"		)}
         else:
            raise ValueError(
                f'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					"PIL.Image.Image":
         if not self.decode:
            raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."		)

         if config.PIL_AVAILABLE:
            import PIL.Image
         else:
            raise ImportError("To support decoding images, please install 'Pillow'."		)

         if token_per_repo_id is None:
            lowerCamelCase__						=			{}

         lowerCamelCase__					,       lowerCamelCase__						=			value["path"], value["bytes"]
         if bytes_ is None:
            if path is None:
               raise ValueError(f'An image should have one of \'path\' or \'bytes\' but both are None in {value}.'		)
            else:
               if is_local_path(SCREAMING_SNAKE_CASE__		):
                  lowerCamelCase__						=			PIL.Image.open(SCREAMING_SNAKE_CASE__		)
               else:
                  lowerCamelCase__						=			path.split("::"		)[-1]
                  try:
                     lowerCamelCase__						=			string_to_dict(SCREAMING_SNAKE_CASE__    ,						config.HUB_DATASETS_URL		)["repo_id"]
                     lowerCamelCase__						=			token_per_repo_id.get(SCREAMING_SNAKE_CASE__		)
                  except ValueError:
                     lowerCamelCase__						=			None
                  with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"    ,						use_auth_token=SCREAMING_SNAKE_CASE__		) as f:
                     lowerCamelCase__						=			BytesIO(f.read()		)
                  lowerCamelCase__						=			PIL.Image.open(bytes_		)
         else:
            lowerCamelCase__						=			PIL.Image.open(BytesIO(bytes_		)		)
         image.load()  # to avoid "Too many open files" errors
         return image





      def 					_lowerCamelCase  (  self		)   ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value("binary"		),
                 "path": Value("string"		),
             }
         )





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:
         if pa.types.is_string(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, storage]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([storage, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_struct(storage.type		):
            if storage.type.get_field_index("bytes"		) >= 0:
               lowerCamelCase__						=			storage.field("bytes"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            if storage.type.get_field_index("path"		) >= 0:
               lowerCamelCase__						=			storage.field("path"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
            lowerCamelCase__						=			pa.array(
                [encode_np_array(np.array(SCREAMING_SNAKE_CASE__		)		)["bytes"] if arr is not None else None for arr in storage.to_pylist()]    ,						type=pa.binary()    ,						)
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays(
                [bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null()		)
         return array_cast(SCREAMING_SNAKE_CASE__    ,						self.pa_type		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(SCREAMING_SNAKE_CASE__		):
            with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"		) as f:
               lowerCamelCase__						=			f.read()
            return bytes_

         lowerCamelCase__						=			pa.array(
             [
                 (path_to_bytes(x["path"]		) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]    ,						type=pa.binary()    ,						)
         lowerCamelCase__						=			pa.array(
             [os.path.basename(SCREAMING_SNAKE_CASE__		) if path is not None else None for path in storage.field("path"		).to_pylist()]    ,						type=pa.string()    ,						)
         lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null()		)
         return array_cast(SCREAMING_SNAKE_CASE__    ,						self.pa_type		)






def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      lowerCamelCase__						=			list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
   return _IMAGE_COMPRESSION_FORMATS



def 	UpperCAmelCase__   (				A__   )      ->      bytes:




   """simple docstring"""
   lowerCamelCase__						=			BytesIO()
   if image.format in list_image_compression_formats():
      lowerCamelCase__						=			image.format
   else:
      lowerCamelCase__						=			"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
   image.save(A__	,    format=A__   )
   return buffer.getvalue()



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if hasattr(A__	,    "filename"   ) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   lowerCamelCase__						=			array.dtype
   lowerCamelCase__						=			dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
   lowerCamelCase__						=			dtype.kind
   lowerCamelCase__						=			dtype.itemsize

   lowerCamelCase__						=			None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      lowerCamelCase__						=			np.dtype("|u1"   )
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'   )
      if dtype is not dest_dtype:
         warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''   )
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      lowerCamelCase__						=			dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         lowerCamelCase__						=			dtype_byteorder + dtype_kind + str(A__   )
         lowerCamelCase__						=			np.dtype(A__   )
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''   )
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'   )
   lowerCamelCase__						=			PIL.Image.fromarray(array.astype(A__   )   )
   return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      List[dict]:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   if objs:
      lowerCamelCase__					,       lowerCamelCase__						=			first_non_null_value(A__   )
      if isinstance(A__	,    A__   ):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(A__	,    np.ndarray   ):
         lowerCamelCase__						=			no_op_if_value_is_null(A__   )
         return [obj_to_image_dict_func(A__   ) for obj in objs]
      elif isinstance(A__	,    PIL.Image.Image   ):
         lowerCamelCase__						=			no_op_if_value_is_null(A__   )
         return [obj_to_image_dict_func(A__   ) for obj in objs]
      else:
         return objs
   else:
      return objs




