
'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
snake_case									=    logging.get_logger(__name__)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_      ,			config=lowerCamelCase_			)
 lowerCAmelCase__     :   str     		=	downstream_dict["projector.weight"]
 lowerCAmelCase__     :   Optional[Any]     		=	downstream_dict["projector.bias"]
 lowerCAmelCase__     :   Optional[Any]     		=	downstream_dict["model.post_net.linear.weight"]
 lowerCAmelCase__     :   Union[str, Any]     		=	downstream_dict["model.post_net.linear.bias"]
 return model


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   int     		=	UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_      ,			config=lowerCamelCase_			)
 lowerCAmelCase__     :   Any     		=	downstream_dict["model.linear.weight"]
 lowerCAmelCase__     :   int     		=	downstream_dict["model.linear.bias"]
 return model


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Any     		=	UniSpeechSatForXVector.from_pretrained(lowerCamelCase_      ,			config=lowerCamelCase_			)
 lowerCAmelCase__     :   Tuple     		=	downstream_dict["connector.weight"]
 lowerCAmelCase__     :   Any     		=	downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
  lowerCAmelCase__     :   Optional[int]     		=	downstream_dict[
      f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
  ]
  lowerCAmelCase__     :   List[str]     		=	downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

 lowerCAmelCase__     :   Union[str, Any]     		=	downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
 lowerCAmelCase__     :   Union[str, Any]     		=	downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
 lowerCAmelCase__     :   Optional[Any]     		=	downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
 lowerCAmelCase__     :   Any     		=	downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
 lowerCAmelCase__     :   Any     		=	downstream_dict["objective.W"]
 return model


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)

 lowerCAmelCase__     :   Dict     		=	checkpoint["Downstream"]

 lowerCAmelCase__     :   Union[str, Any]     		=	UniSpeechSatConfig.from_pretrained(lowerCamelCase_			)
 lowerCAmelCase__     :   Union[str, Any]     		=	WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase_      ,			return_attention_mask=lowerCamelCase_      ,			do_normalize=lowerCamelCase_			)

 lowerCAmelCase__     :   int     		=	hf_config.architectures[0]
 if arch.endswith("ForSequenceClassification"			):
  lowerCAmelCase__     :   List[Any]     		=	convert_classification(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
 elif arch.endswith("ForAudioFrameClassification"			):
  lowerCAmelCase__     :   List[Any]     		=	convert_diarization(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
 elif arch.endswith("ForXVector"			):
  lowerCAmelCase__     :   Union[str, Any]     		=	convert_xvector(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
 else:
  raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''			)

 if hf_config.use_weighted_layer_sum:
  lowerCAmelCase__     :   Tuple     		=	checkpoint["Featurizer"]["weights"]

 hf_feature_extractor.save_pretrained(lowerCamelCase_			)
 hf_model.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
     snake_case									=    argparse.ArgumentParser()
     parser.add_argument(
         """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
     )
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
     parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
     snake_case									=    parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          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"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





