





'''simple docstring'''







import argparse

import torch

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


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

def    __UpperCamelCase	(	a    : List[Any]					,   a    : Optional[Any]					,   a    : Optional[int]     ) ->Union[str, Any]:
 snake_case				=			UniSpeechSatForSequenceClassification.from_pretrained(a					,   config=a     )
 snake_case				=			downstream_dict['''projector.weight''']
 snake_case				=			downstream_dict['''projector.bias''']
 snake_case				=			downstream_dict['''model.post_net.linear.weight''']
 snake_case				=			downstream_dict['''model.post_net.linear.bias''']
 return model

def    __UpperCamelCase	(	a    : Tuple					,   a    : int					,   a    : Optional[int]     ) ->str:
 snake_case				=			UniSpeechSatForAudioFrameClassification.from_pretrained(a					,   config=a     )
 snake_case				=			downstream_dict['''model.linear.weight''']
 snake_case				=			downstream_dict['''model.linear.bias''']
 return model

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : Optional[Any]					,   a    : Optional[int]     ) ->Optional[Any]:
 snake_case				=			UniSpeechSatForXVector.from_pretrained(a					,   config=a     )
 snake_case				=			downstream_dict['''connector.weight''']
 snake_case				=			downstream_dict['''connector.bias''']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
  snake_case				=			downstream_dict[
      f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  snake_case				=			downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

 snake_case				=			downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
 snake_case				=			downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
 snake_case				=			downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
 snake_case				=			downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
 snake_case				=			downstream_dict['''objective.W''']
 return model

@torch.no_grad()
def    __UpperCamelCase	(	a    : Any					,   a    : str					,   a    : Any					,   a    : Tuple     ) ->List[Any]:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )

 snake_case				=			checkpoint['''Downstream''']

 snake_case				=			UniSpeechSatConfig.from_pretrained(a     )
 snake_case				=			WavaVecaFeatureExtractor.from_pretrained(
     a					,   return_attention_mask=a					,   do_normalize=a     )

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

 if hf_config.use_weighted_layer_sum:
  snake_case				=			checkpoint['''Featurizer''']['''weights''']

 hf_feature_extractor.save_pretrained(a     )
 hf_model.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			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.')
   _lowercase       						=			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 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			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     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

