



"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCAmelCase						:		str			= logging.get_logger(__name__)

UpperCAmelCase						:		Optional[int]			= OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

UpperCAmelCase						:		List[Any]			= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
    if class_name in extractors:
      lowercase_								= model_type_to_module_name(UpperCAmelCase__							)

      lowercase_								= importlib.import_module(F'''.{module_name}''' ,  """transformers.models"""							)
      try:
        return getattr(UpperCAmelCase__ ,  UpperCAmelCase__							)
      except AttributeError:
        continue

  for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
    if getattr(UpperCAmelCase__ ,  """__name__""" ,  UpperCAmelCase__							) == class_name:
      return extractor

       # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
       # init and we return the proper dummy to get an appropriate error message.
  lowercase_								= importlib.import_module("""transformers"""							)
  if hasattr(UpperCAmelCase__ ,  UpperCAmelCase__							):
    return getattr(UpperCAmelCase__ ,  UpperCAmelCase__							)

  return None


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase = None ,  __lowerCAmelCase = False ,  __lowerCAmelCase = False ,  __lowerCAmelCase = None ,  __lowerCAmelCase = None ,  __lowerCAmelCase = None ,  __lowerCAmelCase = False ,  **__lowerCAmelCase ,  ) ->						Union[str, Any]:







  '''simple docstring'''


  lowercase_								= get_file_from_repo(
      UpperCAmelCase__ ,  UpperCAmelCase__ ,  cache_dir=UpperCAmelCase__ ,  force_download=UpperCAmelCase__ ,  resume_download=UpperCAmelCase__ ,  proxies=UpperCAmelCase__ ,  use_auth_token=UpperCAmelCase__ ,  revision=UpperCAmelCase__ ,  local_files_only=UpperCAmelCase__ ,  )
  if resolved_config_file is None:
    logger.info(
        """Could not locate the feature extractor configuration file, will try to use the model config instead."""							)
    return {}

  with open(UpperCAmelCase__ ,  encoding="""utf-8"""							) as reader:
    return json.load(UpperCAmelCase__							)


class    SCREAMING_SNAKE_CASE__ :

    def __init__(				self			:				Optional[Any]):







      """simple docstring"""







      raise EnvironmentError(
          """AutoFeatureExtractor is designed to be instantiated """
          """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.""")

    @classmethod
    @replace_list_option_in_docstrings(__lowerCAmelCase)
    def 					_UpperCAmelCase	(				cls			:				List[str] ,  lowerCAmelCase_			:				Any ,  **lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= kwargs.pop("""config""" ,  __lowerCAmelCase)
      lowercase_								= kwargs.pop("""trust_remote_code""" ,  __lowerCAmelCase)
      lowercase_								= True

      lowercase_					,    lowercase_								= FeatureExtractionMixin.get_feature_extractor_dict(__lowerCAmelCase ,  **__lowerCAmelCase)
      lowercase_								= config_dict.get("""feature_extractor_type""" ,  __lowerCAmelCase)
      lowercase_								= None
      if "AutoFeatureExtractor" in config_dict.get("""auto_map""" ,  {}):
        lowercase_								= config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

      # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
      if feature_extractor_class is None and feature_extractor_auto_map is None:
        if not isinstance(__lowerCAmelCase ,  __lowerCAmelCase):
          lowercase_								= AutoConfig.from_pretrained(__lowerCAmelCase ,  **__lowerCAmelCase)
        # It could be in `config.feature_extractor_type``
        lowercase_								= getattr(__lowerCAmelCase ,  """feature_extractor_type""" ,  __lowerCAmelCase)
        if hasattr(__lowerCAmelCase ,  """auto_map""") and "AutoFeatureExtractor" in config.auto_map:
          lowercase_								= config.auto_map["""AutoFeatureExtractor"""]

      if feature_extractor_class is not None:
        lowercase_								= feature_extractor_class_from_name(__lowerCAmelCase)

      lowercase_								= feature_extractor_auto_map is not None
      lowercase_								= feature_extractor_class is not None or type(__lowerCAmelCase) in FEATURE_EXTRACTOR_MAPPING
      lowercase_								= resolve_trust_remote_code(
          __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase)

      if has_remote_code and trust_remote_code:
        lowercase_								= get_class_from_dynamic_module(
            __lowerCAmelCase ,  __lowerCAmelCase ,  **__lowerCAmelCase)
        lowercase_								= kwargs.pop("""code_revision""" ,  __lowerCAmelCase)
        if os.path.isdir(__lowerCAmelCase):
          feature_extractor_class.register_for_auto_class()
        return feature_extractor_class.from_dict(__lowerCAmelCase ,  **__lowerCAmelCase)
      elif feature_extractor_class is not None:
        return feature_extractor_class.from_dict(__lowerCAmelCase ,  **__lowerCAmelCase)
      # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
      elif type(__lowerCAmelCase) in FEATURE_EXTRACTOR_MAPPING:
        lowercase_								= FEATURE_EXTRACTOR_MAPPING[type(__lowerCAmelCase)]
        return feature_extractor_class.from_dict(__lowerCAmelCase ,  **__lowerCAmelCase)

      raise ValueError(
          F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
          F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
          F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}''')




    @staticmethod
    def 					_UpperCAmelCase	(				lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      FEATURE_EXTRACTOR_MAPPING.register(__lowerCAmelCase ,  __lowerCAmelCase)










"""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    SCREAMING_SNAKE_CASE__ :

           @staticmethod
           def 					_UpperCAmelCase	(				*lowerCAmelCase_			:				Tuple ,  **lowerCAmelCase_			:				Tuple):







             """simple docstring"""







             pass


@is_pipeline_test
@require_vision
@require_torch
class    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):
    lowercase__      =   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







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

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

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= object_detector(examples[0] ,  threshold=0.0)

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

    @require_tf
    @unittest.skip("""Zero Shot Object Detection not implemented in TF""")
    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      pass

    @require_torch
    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







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

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

      self.assertEqual(
          nested_simplify(lowerCAmelCase_ ,  decimals=4) ,  [
              {"""score""": 0.7_235, """label""": """cat""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
              {"""score""": 0.7_218, """label""": """remote""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
              {"""score""": 0.7_184, """label""": """couch""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
              {"""score""": 0.6_748, """label""": """remote""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
              {"""score""": 0.6_656, """label""": """cat""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
              {"""score""": 0.6_614, """label""": """couch""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
              {"""score""": 0.6_456, """label""": """remote""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
              {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 6_7, """ymin""": 2_7_4, """xmax""": 9_3, """ymax""": 2_9_7}},
              {"""score""": 0.6_419, """label""": """cat""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
          ] ,  )

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

      self.assertEqual(
          nested_simplify(lowerCAmelCase_ ,  decimals=4) ,  [
              [
                  {"""score""": 0.7_235, """label""": """cat""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
                  {"""score""": 0.7_218, """label""": """remote""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
                  {"""score""": 0.7_184, """label""": """couch""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
                  {"""score""": 0.6_748, """label""": """remote""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
                  {"""score""": 0.6_656, """label""": """cat""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
                  {"""score""": 0.6_614, """label""": """couch""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
                  {"""score""": 0.6_456, """label""": """remote""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
                  {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 6_7, """ymin""": 2_7_4, """xmax""": 9_3, """ymax""": 2_9_7}},
                  {"""score""": 0.6_419, """label""": """cat""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
              ]
          ] ,  )

    @require_torch
    @slow
    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= pipeline("""zero-shot-object-detection""")

      lowercase_								= object_detector(
          """http://images.cocodataset.org/val2017/000000039769.jpg""" ,  candidate_labels=["""cat""", """remote""", """couch"""] ,  )
      self.assertEqual(
          nested_simplify(lowerCAmelCase_ ,  decimals=4) ,  [
              {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
              {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
              {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
              {"""score""": 0.1_474, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
              {"""score""": 0.1_208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
          ] ,  )

      lowercase_								= 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(lowerCAmelCase_ ,  decimals=4) ,  [
              [
                  {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
                  {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
                  {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
                  {"""score""": 0.1_474, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
                  {"""score""": 0.1_208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
              ],
              [
                  {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
                  {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
                  {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
                  {"""score""": 0.1_474, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
                  {"""score""": 0.1_208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
              ],
          ] ,  )

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







      """simple docstring"""







      pass

    @require_torch
    @slow
    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_								= 0.2
      lowercase_								= pipeline("""zero-shot-object-detection""")

      lowercase_								= object_detector(
          """http://images.cocodataset.org/val2017/000000039769.jpg""" ,  candidate_labels=["""cat""", """remote""", """couch"""] ,  threshold=lowerCAmelCase_ ,  )
      self.assertEqual(
          nested_simplify(lowerCAmelCase_ ,  decimals=4) ,  [
              {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
              {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
              {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
          ] ,  )




    @require_torch
    @slow
    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= 2
      lowercase_								= pipeline("""zero-shot-object-detection""")

      lowercase_								= object_detector(
          """http://images.cocodataset.org/val2017/000000039769.jpg""" ,  candidate_labels=["""cat""", """remote""", """couch"""] ,  top_k=lowerCAmelCase_ ,  )
      self.assertEqual(
          nested_simplify(lowerCAmelCase_ ,  decimals=4) ,  [
              {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
              {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
          ] ,  )

