


'''simple docstring'''







import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
a__  :					List[str]        =       'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
a__  :					int        =       direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a__  :					str        =       re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a__  :					List[str]        =       re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a__  :					Optional[int]        =       re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a__  :					Tuple        =       [
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]







def _UpperCamelCase       (     __A    )							->		Any:


      '''simple docstring'''



      UpperCamelCase__										=				re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"      ,			__A    )
      return [m.group(0    ) for m in matches]







def _UpperCamelCase       (     )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__										=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      UpperCamelCase__										=				{
          config.replace("Config"      ,			""    ): model_type for model_type, config in config_maping_names.items()
      }

      # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
      UpperCamelCase__										=				collections.defaultdict(__A    )
      UpperCamelCase__										=				collections.defaultdict(__A    )
      UpperCamelCase__										=				collections.defaultdict(__A    )

      # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
      for attr_name in dir(__A    ):
            UpperCamelCase__										=				None
            if _re_tf_models.match(__A    ) is not None:
                  UpperCamelCase__										=				tf_models
                  UpperCamelCase__										=				_re_tf_models.match(__A    ).groups()[0]
            elif _re_flax_models.match(__A    ) is not None:
                  UpperCamelCase__										=				flax_models
                  UpperCamelCase__										=				_re_flax_models.match(__A    ).groups()[0]
            elif _re_pt_models.match(__A    ) is not None:
                  UpperCamelCase__										=				pt_models
                  UpperCamelCase__										=				_re_pt_models.match(__A    ).groups()[0]

            if lookup_dict is not None:
                  while len(__A    ) > 0:
                        if attr_name in model_prefix_to_model_type:
                              UpperCamelCase__										=				True
                              break
                        # Try again after removing the last word in the name
                        UpperCamelCase__										=				"".join(camel_case_split(__A    )[:-1]    )

      UpperCamelCase__										=				set(list(pt_models.keys()    ) + list(tf_models.keys()    ) + list(flax_models.keys()    )    )
      UpperCamelCase__										=				list(__A    )
      all_models.sort()

      UpperCamelCase__										=				{"model_type": all_models}
      UpperCamelCase__										=				[pt_models[t] for t in all_models]
      UpperCamelCase__										=				[tf_models[t] for t in all_models]
      UpperCamelCase__										=				[flax_models[t] for t in all_models]

      # Now let's use the auto-mapping names to make sure
      UpperCamelCase__										=				{}
      for t in all_models:
            if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                  UpperCamelCase__										=				"AutoProcessor"
            elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                  UpperCamelCase__										=				"AutoTokenizer"
            elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                  UpperCamelCase__										=				"AutoFeatureExtractor"
            else:
                  # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                  UpperCamelCase__										=				"AutoTokenizer"

      UpperCamelCase__										=				[processors[t] for t in all_models]

      return pd.DataFrame(__A    )







def _UpperCamelCase       (     __A    )							->		int:


      '''simple docstring'''



      UpperCamelCase__										=				[
          transformers_module.models.auto.modeling_auto,
          transformers_module.models.auto.modeling_tf_auto,
          transformers_module.models.auto.modeling_flax_auto,
      ]
      for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
            UpperCamelCase__										=				[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
            UpperCamelCase__										=				[auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
            # Loop through all three frameworks
            for module, cls, mapping in zip(__A      ,			__A      ,			__A    ):
                  # The type of pipeline may not exist in this framework
                  if not hasattr(__A      ,			__A    ):
                        continue
                  # First extract all model_names
                  UpperCamelCase__										=				[]
                  for name in getattr(__A      ,			__A    ).values():
                        if isinstance(__A      ,			__A    ):
                              model_names.append(__A    )
                        else:
                              model_names.extend(list(__A    )    )

            # Add pipeline tag and auto model class for those models
                  table.update({model_name: (pipeline_tag, cls) for model_name in model_names}    )

      return table







def _UpperCamelCase       (     __A      ,			__A    )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__										=				get_frameworks_table()
      UpperCamelCase__										=				Dataset.from_pandas(__A    )

      UpperCamelCase__										=				hf_hub_download(
          "huggingface/transformers-metadata"      ,			"pipeline_tags.json"      ,			repo_type="dataset"      ,			token=__A    )
      UpperCamelCase__										=				Dataset.from_json(__A    )
      UpperCamelCase__										=				{
          tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
          for i in range(len(__A    )    )
      }
      UpperCamelCase__										=				update_pipeline_and_auto_class_table(__A    )

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      UpperCamelCase__										=				sorted(table.keys()    )
      UpperCamelCase__										=				pd.DataFrame(
          {
              "model_class": model_classes,
              "pipeline_tag": [table[m][0] for m in model_classes],
              "auto_class": [table[m][1] for m in model_classes],
          }    )
      UpperCamelCase__										=				Dataset.from_pandas(__A    )

      with tempfile.TemporaryDirectory() as tmp_dir:
            frameworks_dataset.to_json(os.path.join(__A      ,			"frameworks.json"    )    )
            tags_dataset.to_json(os.path.join(__A      ,			"pipeline_tags.json"    )    )

            if commit_sha is not None:
                  UpperCamelCase__										=				(
                      F'''Update with commit {commit_sha}\n\nSee: '''
                      F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
                  )
            else:
                  UpperCamelCase__										=				"Update"

            upload_folder(
                repo_id="huggingface/transformers-metadata"      ,			folder_path=__A      ,			repo_type="dataset"      ,			token=__A      ,			commit_message=__A      ,			)







def _UpperCamelCase       (     )							->		int:


      '''simple docstring'''



      UpperCamelCase__										=				{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
      UpperCamelCase__										=				transformers_module.pipelines.SUPPORTED_TASKS
      UpperCamelCase__										=				[]
      for key in pipeline_tasks:
            if key not in in_table:
                  UpperCamelCase__										=				pipeline_tasks[key]["pt"]
                  if isinstance(__A      ,			(list, tuple)    ):
                        UpperCamelCase__										=				model[0]
                  UpperCamelCase__										=				model.__name__
                  if model not in in_table.values():
                        missing.append(__A    )

      if len(__A    ) > 0:
            UpperCamelCase__										=				", ".join(__A    )
            raise ValueError(
                "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
                F'''`utils/update_metadata.py`: {msg}. Please add them!'''    )


if __name__ == "__main__":
  a__  :					Union[str, Any]        =       argparse.ArgumentParser()
  parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
  parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
  parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
  a__  :					List[Any]        =       parser.parse_args()

  if args.check_only:
    check_pipeline_tags()
  else:
    update_metadata(args.token, args.commit_sha)









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  snake_case__:







												'''simple docstring'''




												@staticmethod
												def   lowercase_       ( *__lowercase      ,  **__lowercase							)    ->			Union[str, Any]:
																pass







@is_pipeline_test
@require_vision
@require_torch
class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Union[str, Any]    =							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = pipeline(
										    '''zero-shot-object-detection'''      ,  model='''hf-internal-testing/tiny-random-owlvit-object-detection'''							)

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




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase							)    ->			str:
										lowerCAmelCase_       :					Tuple			 = object_detector(examples[0]      ,  threshold=0.0							)

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




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




						@require_torch
						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					Union[str, Any]			 = pipeline(
										    '''zero-shot-object-detection'''      ,  model='''hf-internal-testing/tiny-random-owlvit-object-detection'''							)

										lowerCAmelCase_       :					Union[str, Any]			 = object_detector(
										    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  threshold=0.64      ,  )

										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										        {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										        {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										        {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										        {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										        {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										        {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										        {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
										        {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										    ]      ,  )

										lowerCAmelCase_       :					Union[str, Any]			 = object_detector(
										    [
										        {
										            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
										            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
										        }
										    ]      ,  threshold=0.64      ,  )

										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        [
										            {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										            {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										            {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										            {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										            {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										            {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										            {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										            {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
										            {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										        ]
										    ]      ,  )




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

										lowerCAmelCase_       :					Dict			 = object_detector(
										    '''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  )
										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										        {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										        {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
										        {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
										    ]      ,  )

										lowerCAmelCase_       :					Tuple			 = 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(__lowercase      ,  decimals=4							)      ,  [
										        [
										            {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										            {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										            {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										            {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
										            {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
										        ],
										        [
										            {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										            {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										            {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										            {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
										            {'''score''': 0.12_08, '''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   lowercase_       ( self							)    ->			List[str]:
										pass




						@require_torch
						@slow
						def   lowercase_       ( self							)    ->			Optional[int]:
										lowerCAmelCase_       :					Any			 = 0.2
										lowerCAmelCase_       :					List[Any]			 = pipeline('''zero-shot-object-detection'''							)

										lowerCAmelCase_       :					Optional[Any]			 = object_detector(
										    '''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  threshold=__lowercase      ,  )
										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										        {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										    ]      ,  )




						@require_torch
						@slow
						def   lowercase_       ( self							)    ->			Optional[int]:
										lowerCAmelCase_       :					Dict			 = 2
										lowerCAmelCase_       :					Union[str, Any]			 = pipeline('''zero-shot-object-detection'''							)

										lowerCAmelCase_       :					Optional[Any]			 = object_detector(
										    '''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  top_k=__lowercase      ,  )
										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										    ]      ,  )