




import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__       :      Any  =   logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE__       :      Tuple  =   {
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


SCREAMING_SNAKE_CASE__       :      List[Any]  =   {
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

SCREAMING_SNAKE_CASE__       :      List[Any]  =   os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   os.path.join(os.path.expanduser('~'), '.cache')
SCREAMING_SNAKE_CASE__       :      str  =   os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> Any:
       lowerCamelCase   :      List[str]			 =					model_type
       if use_small:
              key += "_small"
       return os.path.join(_SCREAMING_SNAKE_CASE ,REMOTE_MODEL_PATHS[key]["file_name"]    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Union[str, Any]:
       os.makedirs(_SCREAMING_SNAKE_CASE ,exist_ok=_SCREAMING_SNAKE_CASE    )
       hf_hub_download(repo_id=_SCREAMING_SNAKE_CASE ,filename=_SCREAMING_SNAKE_CASE ,local_dir=_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE="text"    )	-> Optional[int]:
       if model_type == "text":
              lowerCamelCase   :      Optional[int]			 =					BarkSemanticModel
              lowerCamelCase   :      int			 =					BarkSemanticConfig
              lowerCamelCase   :      Any			 =					BarkSemanticGenerationConfig
       elif model_type == "coarse":
              lowerCamelCase   :      Optional[Any]			 =					BarkCoarseModel
              lowerCamelCase   :      List[str]			 =					BarkCoarseConfig
              lowerCamelCase   :      str			 =					BarkCoarseGenerationConfig
       elif model_type == "fine":
              lowerCamelCase   :      Any			 =					BarkFineModel
              lowerCamelCase   :      List[Any]			 =					BarkFineConfig
              lowerCamelCase   :      Union[str, Any]			 =					BarkFineGenerationConfig
       else:
              raise NotImplementedError()
       lowerCamelCase   :      int			 =					f'''{model_type}_small''' if use_small else model_type
       lowerCamelCase   :      str			 =					REMOTE_MODEL_PATHS[model_key]
       if not os.path.exists(_SCREAMING_SNAKE_CASE    ):
              logger.info(f'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''    )
              _download(model_info["repo_id"] ,model_info["file_name"]    )
       lowerCamelCase   :      Tuple			 =					torch.load(_SCREAMING_SNAKE_CASE ,map_location=_SCREAMING_SNAKE_CASE    )
       # this is a hack
       lowerCamelCase   :      List[Any]			 =					checkpoint["model_args"]
       if "input_vocab_size" not in model_args:
              lowerCamelCase   :      Optional[int]			 =					model_args["vocab_size"]
              lowerCamelCase   :      Dict			 =					model_args["vocab_size"]
              del model_args["vocab_size"]

       # convert Bark model arguments to HF Bark model arguments
       lowerCamelCase   :      Union[str, Any]			 =					model_args.pop("n_head"    )
       lowerCamelCase   :      List[Any]			 =					model_args.pop("n_embd"    )
       lowerCamelCase   :      List[Any]			 =					model_args.pop("n_layer"    )

       lowerCamelCase   :      int			 =					ConfigClass(**checkpoint["model_args"]    )
       lowerCamelCase   :      Optional[Any]			 =					ModelClass(config=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					GenerationConfigClass()

       lowerCamelCase   :      Dict			 =					model_generation_config
       lowerCamelCase   :      Optional[Any]			 =					checkpoint["model"]
       # fixup checkpoint
       lowerCamelCase   :      List[str]			 =					"_orig_mod."
       for k, v in list(state_dict.items()    ):
              if k.startswith(_SCREAMING_SNAKE_CASE    ):
                     # replace part of the key with corresponding layer name in HF implementation
                     lowerCamelCase   :      Union[str, Any]			 =					k[len(_SCREAMING_SNAKE_CASE    ) :]
                     for old_layer_name in new_layer_name_dict:
                            lowerCamelCase   :      List[Any]			 =					new_k.replace(_SCREAMING_SNAKE_CASE ,new_layer_name_dict[old_layer_name]    )

                     lowerCamelCase   :      int			 =					state_dict.pop(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Any			 =					set(state_dict.keys()    ) - set(model.state_dict().keys()    )
       lowerCamelCase   :      Dict			 =					{k for k in extra_keys if not k.endswith(".attn.bias"    )}
       lowerCamelCase   :      Optional[Any]			 =					set(model.state_dict().keys()    ) - set(state_dict.keys()    )
       lowerCamelCase   :      Tuple			 =					{k for k in missing_keys if not k.endswith(".attn.bias"    )}
       if len(_SCREAMING_SNAKE_CASE    ) != 0:
              raise ValueError(f'''extra keys found: {extra_keys}'''    )
       if len(_SCREAMING_SNAKE_CASE    ) != 0:
              raise ValueError(f'''missing keys: {missing_keys}'''    )
       model.load_state_dict(_SCREAMING_SNAKE_CASE ,strict=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					checkpoint["best_val_loss"].item()
       logger.info(f'''model loaded: {round(n_params/1e6 ,1    )}M params, {round(_SCREAMING_SNAKE_CASE ,3    )} loss'''    )
       model.eval()
       model.to(_SCREAMING_SNAKE_CASE    )
       del checkpoint, state_dict

       return model

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE="text"    )	-> Optional[int]:
       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

       lowerCamelCase   :      Optional[Any]			 =					"cpu"  # do conversion on cpu

       lowerCamelCase   :      Tuple			 =					_get_ckpt_path(_SCREAMING_SNAKE_CASE ,use_small=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[Any]			 =					_load_model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,model_type=_SCREAMING_SNAKE_CASE ,use_small=_SCREAMING_SNAKE_CASE    )

       # load bark initial model
       lowerCamelCase   :      Optional[int]			 =					_bark_load_model(_SCREAMING_SNAKE_CASE ,"cpu" ,model_type=_SCREAMING_SNAKE_CASE ,use_small=_SCREAMING_SNAKE_CASE    )

       if model_type == "text":
              lowerCamelCase   :      int			 =					bark_model["model"]

       if model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE    ) != bark_model.get_num_params():
              raise ValueError("initial and new models don't have the same number of parameters"    )

       # check if same output as the bark model
       lowerCamelCase   :      Optional[int]			 =					5
       lowerCamelCase   :      int			 =					10

       if model_type in ["text", "coarse"]:
              lowerCamelCase   :      Union[str, Any]			 =					torch.randint(256 ,(batch_size, sequence_length) ,dtype=torch.int    )
              lowerCamelCase   :      Tuple			 =					bark_model(_SCREAMING_SNAKE_CASE    )[0]

              lowerCamelCase   :      Tuple			 =					model(_SCREAMING_SNAKE_CASE    )

              # take last logits
              lowerCamelCase   :      str			 =					output_new_model_total.logits[:, [-1], :]

       else:
              lowerCamelCase   :      str			 =					3
              lowerCamelCase   :      Union[str, Any]			 =					8
              lowerCamelCase   :      Optional[int]			 =					torch.randint(256 ,(batch_size, sequence_length, n_codes_total) ,dtype=torch.int    )

              lowerCamelCase   :      int			 =					model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      List[str]			 =					bark_model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

              lowerCamelCase   :      str			 =					output_new_model_total.logits

       # output difference should come from the difference of self-attention implementation design
       if output_new_model.shape != output_old_model.shape:
              raise ValueError("initial and new outputs don't have the same shape"    )
       if (output_new_model - output_old_model).abs().max().item() > 1e-3:
              raise ValueError("initial and new outputs are not equal"    )

       Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,)	-> List[str]:
       lowerCamelCase   :      Dict			 =					os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      int			 =					BarkSemanticConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE ,"config.json"    )    )
       lowerCamelCase   :      Optional[int]			 =					BarkCoarseConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE ,"config.json"    )    )
       lowerCamelCase   :      Tuple			 =					BarkFineConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE ,"config.json"    )    )
       lowerCamelCase   :      Tuple			 =					EncodecConfig.from_pretrained("facebook/encodec_24khz"    )

       lowerCamelCase   :      str			 =					BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					EncodecModel.from_pretrained("facebook/encodec_24khz"    )

       lowerCamelCase   :      Union[str, Any]			 =					BarkConfig.from_sub_model_configs(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config ,coarseAcoustic.generation_config ,fineAcoustic.generation_config    )

       lowerCamelCase   :      List[str]			 =					BarkModel(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					semantic
       lowerCamelCase   :      Optional[Any]			 =					coarseAcoustic
       lowerCamelCase   :      Union[str, Any]			 =					fineAcoustic
       lowerCamelCase   :      str			 =					codec

       lowerCamelCase   :      Tuple			 =					bark_generation_config

       Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
       bark.save_pretrained(_SCREAMING_SNAKE_CASE ,repo_id=_SCREAMING_SNAKE_CASE ,push_to_hub=_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   argparse.ArgumentParser()
       # Required parameters

       parser.add_argument('model_type', type=str, help='text, coarse or fine.')
       parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()

       load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)













'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    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 							SCREAMING_SNAKE_CASE  (						*_UpperCAmelCase						,	**_UpperCAmelCase):

            '''simple docstring'''






            pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       MODEL_FOR_OBJECT_DETECTION_MAPPING


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'						,	threshold=0.0)

          self.assertGreater(len(_UpperCAmelCase)						,	0)
          for detected_object in outputs:
               self.assertEqual(
                   _UpperCAmelCase						,	{
                       'score': ANY(_UpperCAmelCase),
                       'label': ANY(_UpperCAmelCase),
                       'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
                   }						,	)

          import datasets

          __A						: Tuple      =       datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,	'image'						,	split='test')

          __A						: List[str]      =       [
              Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'),
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              # RGBA
              dataset[0]['file'],
              # LA
              dataset[1]['file'],
              # L
              dataset[2]['file'],
          ]
          __A						: Union[str, Any]      =       object_detector(_UpperCAmelCase						,	threshold=0.0)

          self.assertEqual(len(_UpperCAmelCase)						,	len(_UpperCAmelCase))
          for outputs in batch_outputs:
               self.assertGreater(len(_UpperCAmelCase)						,	0)
               for detected_object in outputs:
                    self.assertEqual(
                        _UpperCAmelCase						,	{
                            'score': ANY(_UpperCAmelCase),
                            'label': ANY(_UpperCAmelCase),
                            'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
                        }						,	)


     @require_tf
     @unittest.skip('Object detection not implemented in TF')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       'hf-internal-testing/tiny-detr-mobilenetsv3'

          __A						: Any      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: int      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: Any      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Dict      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,	threshold=0.0)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
              ]						,	)

          __A						: Optional[Any]      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ]						,	threshold=0.0						,	)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  ],
                  [
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       'facebook/detr-resnet-50'

          __A						: Union[str, Any]      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: Tuple      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: List[Any]      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Union[str, Any]      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)

          __A						: Tuple      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ])
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: Any      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)

          __A						: str      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ])
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       0.9985
          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: List[str]      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: List[Any]      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,	threshold=_UpperCAmelCase)
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)


     @require_torch
     @require_pytesseract
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       'Narsil/layoutlmv3-finetuned-funsd'
          __A						: Tuple      =       0.9993

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase						,	threshold=_UpperCAmelCase)

          __A						: Optional[int]      =       object_detector(
              'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
                  {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
              ]						,	)