





'''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 			_lowercase						:







      @staticmethod
      def     UpperCamelCase			( *A__    ,   **A__    ) ->	Optional[int]:
       pass
@is_pipeline_test
@require_vision
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	List[str]:
      snake_case				=			pipeline(
          '''zero-shot-object-detection'''    ,   model='''hf-internal-testing/tiny-random-owlvit-object-detection'''    )

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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Any:
      snake_case				=			object_detector(examples[0]    ,   threshold=0.0    )

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







     @require_tf
     @unittest.skip('''Zero Shot Object Detection not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      pass







     @require_torch
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			pipeline(
          '''zero-shot-object-detection'''    ,   model='''hf-internal-testing/tiny-random-owlvit-object-detection'''    )

      snake_case				=			object_detector(
          '''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ,   candidate_labels=['''cat''', '''remote''', '''couch''']    ,   threshold=0.6_4    ,   )

      self.assertEqual(
          nested_simplify(A__    ,   decimals=4    )    ,   [
              {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
              {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
              {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
              {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
              {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
              {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
              {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
              {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
              {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
          ]    ,   )

      snake_case				=			object_detector(
          [
              {
                  '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                  '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
              }
          ]    ,   threshold=0.6_4    ,   )

      self.assertEqual(
          nested_simplify(A__    ,   decimals=4    )    ,   [
              [
                  {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                  {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                  {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
                  {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                  {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                  {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
                  {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
                  {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
                  {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
              ]
          ]    ,   )







     @require_torch
     @slow
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			pipeline('''zero-shot-object-detection'''    )

      snake_case				=			object_detector(
          '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,   candidate_labels=['''cat''', '''remote''', '''couch''']    ,   )
      self.assertEqual(
          nested_simplify(A__    ,   decimals=4    )    ,   [
              {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
              {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
              {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
              {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
              {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
          ]    ,   )

      snake_case				=			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(A__    ,   decimals=4    )    ,   [
              [
                  {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
                  {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
                  {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
                  {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
                  {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
              ],
              [
                  {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
                  {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
                  {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
                  {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
                  {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
              ],
          ]    ,   )







     @require_tf
     @unittest.skip('''Zero Shot Object Detection not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Any:
      pass







     @require_torch
     @slow
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			0.2
      snake_case				=			pipeline('''zero-shot-object-detection'''    )

      snake_case				=			object_detector(
          '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,   candidate_labels=['''cat''', '''remote''', '''couch''']    ,   threshold=A__    ,   )
      self.assertEqual(
          nested_simplify(A__    ,   decimals=4    )    ,   [
              {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
              {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
              {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
          ]    ,   )







     @require_torch
     @slow
     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			2
      snake_case				=			pipeline('''zero-shot-object-detection'''    )

      snake_case				=			object_detector(
          '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,   candidate_labels=['''cat''', '''remote''', '''couch''']    ,   top_k=A__    ,   )
      self.assertEqual(
          nested_simplify(A__    ,   decimals=4    )    ,   [
              {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
              {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
          ]    ,   )








'''simple docstring'''







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

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   from .features import FeatureType


_lowercase						,				_lowercase						,				_lowercase       						=			False, False, False
@dataclass
class 			_lowercase						:
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					None
     # Automatically constructed
     _UpperCAmelCase										=					"dict"
     _UpperCAmelCase										=					pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}					)
     _UpperCAmelCase										=					field(default='''Audio'''				,		init=__a				,		repr=__a					)







     def __call__( self    ) ->	Optional[Any]:
      return self.pa_type







     def     UpperCamelCase			( self    ,   A__    ) ->	dict:
      try:
       import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
      except ImportError as err:
       raise ImportError('''To support encoding audio data, please install \'soundfile\'.'''    ) from err
      if isinstance(A__    ,   A__    ):
       return {"bytes": None, "path": value}
      elif isinstance(A__    ,   A__    ):
       return {"bytes": value, "path": None}
      elif "array" in value:
       # convert the audio array to wav bytes
       snake_case				=			BytesIO()
       sf.write(A__    ,   value['''array''']    ,   value['''sampling_rate''']    ,   format='''wav'''    )
       return {"bytes": buffer.getvalue(), "path": None}
      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
       if value["path"].endswith('''pcm'''    ):
        # "PCM" only has raw audio bytes
        if value.get('''sampling_rate'''    ) is None:
         # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
         raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object'''    )
        if value.get('''bytes'''    ):
         # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
         snake_case				=			np.frombuffer(value['''bytes''']    ,   dtype=np.intaa    ).astype(np.floataa    ) / 3_27_67
        else:
         snake_case				=			np.memmap(value['''path''']    ,   dtype='''h'''    ,   mode='''r'''    ).astype(np.floataa    ) / 3_27_67

        snake_case				=			BytesIO(bytes()    )
        sf.write(A__    ,   A__    ,   value['''sampling_rate''']    ,   format='''wav'''    )
        return {"bytes": buffer.getvalue(), "path": None}
       else:
        return {"bytes": None, "path": value.get('''path'''    )}
      elif value.get('''bytes'''    ) is not None or value.get('''path'''    ) is not None:
       # store the audio bytes, and path is used to infer the audio format using the file extension
       return {"bytes": value.get('''bytes'''    ), "path": value.get('''path'''    )}
      else:
       raise ValueError(
           F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	dict:
      if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.'''    )

      snake_case				,		snake_case				=			(value['''path'''], BytesIO(value['''bytes''']    )) if value['''bytes'''] is not None else (value['''path'''], None)
      if path is None and file is None:
       raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""    )

      try:
       import librosa
       import soundfile as sf
      except ImportError as err:
       raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.'''    ) from err

      snake_case				=			xsplitext(A__    )[1][1:].lower() if path is not None else None
      if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
       raise RuntimeError(
           '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )
      elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
       raise RuntimeError(
           '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )

      if file is None:
       snake_case				=			token_per_repo_id or {}
       snake_case				=			path.split('''::'''    )[-1]
       try:
        snake_case				=			string_to_dict(A__    ,   config.HUB_DATASETS_URL    )['''repo_id''']
        snake_case				=			token_per_repo_id[repo_id]
       except (ValueError, KeyError):
        snake_case				=			None

       with xopen(A__    ,   '''rb'''    ,   use_auth_token=A__    ) as f:
        snake_case				,		snake_case				=			sf.read(A__    )

      else:
       snake_case				,		snake_case				=			sf.read(A__    )

      snake_case				=			array.T
      if self.mono:
       snake_case				=			librosa.to_mono(A__    )
      if self.sampling_rate and self.sampling_rate != sampling_rate:
       snake_case				=			librosa.resample(A__    ,   orig_sr=A__    ,   target_sr=self.sampling_rate    )
       snake_case				=			self.sampling_rate

      return {"path": path, "array": array, "sampling_rate": sampling_rate}







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

      if self.decode:
       raise ValueError('''Cannot flatten a decoded Audio feature.'''    )
      return {
          "bytes": Value('''binary'''    ),
          "path": Value('''string'''    ),
      }







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:
      if pa.types.is_string(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, storage]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_binary(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([storage, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_struct(storage.type    ) and storage.type.get_all_field_indices('''array'''    ):
       snake_case				=			pa.array([Audio().encode_example(A__    ) if x is not None else None for x in storage.to_pylist()]    )
      elif pa.types.is_struct(storage.type    ):
       if storage.type.get_field_index('''bytes'''    ) >= 0:
        snake_case				=			storage.field('''bytes'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       if storage.type.get_field_index('''path'''    ) >= 0:
        snake_case				=			storage.field('''path'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      return array_cast(A__    ,   self.pa_type    )







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:

      @no_op_if_value_is_null
      def path_to_bytes(A__    ):
       with xopen(A__    ,   '''rb'''    ) as f:
        snake_case				=			f.read()
       return bytes_

      snake_case				=			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()    ,   )
      snake_case				=			pa.array(
          [os.path.basename(A__    ) if path is not None else None for path in storage.field('''path'''    ).to_pylist()]    ,   type=pa.string()    ,   )
      snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=bytes_array.is_null()    )
      return array_cast(A__    ,   self.pa_type    )

