


import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf

if is_vision_available():
		from PIL import Image


def     lowercase	(    a="" ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						tempfile.mkdtemp()
		return os.path.join(A__     , str(uuid.uuida() ) + suffix )



@require_soundfile
@require_torch
class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.rand(12   ,  dtype=torch.floataa) - 0.5
				SCREAMING_SNAKE_CASE_				:int				     =						AgentAudio(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:int				     =						str(agent_type.to_string())

				# Ensure that the tensor and the agent_type's tensor are the same
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  agent_type.to_raw()   ,  atol=1E-4))

				del agent_type

				# Ensure the path remains even after the object deletion
				self.assertTrue(os.path.exists(UpperCAmelCase))

				# Ensure that the file contains the same value as the original tensor
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						sf.read(UpperCAmelCase)
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  torch.tensor(UpperCAmelCase)   ,  atol=1E-4))







		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.rand(12   ,  dtype=torch.floataa) - 0.5
				SCREAMING_SNAKE_CASE_				:List[str]				     =						get_new_path(suffix=".wav")
				sf.write(UpperCAmelCase   ,  UpperCAmelCase   ,  1_60_00)

				SCREAMING_SNAKE_CASE_				:str				     =						AgentAudio(UpperCAmelCase)

				self.assertTrue(torch.allclose(UpperCAmelCase   ,  agent_type.to_raw()   ,  atol=1E-4))
				self.assertEqual(agent_type.to_string()   ,  UpperCAmelCase)




@require_vision
@require_torch
class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:str				     =						torch.randint(0   ,  2_56   ,  (64, 64, 3))
				SCREAMING_SNAKE_CASE_				:List[str]				     =						AgentImage(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						str(agent_type.to_string())

				# Ensure that the tensor and the agent_type's tensor are the same
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  agent_type._tensor   ,  atol=1E-4))

				self.assertIsInstance(agent_type.to_raw()   ,  Image.Image)

				# Ensure the path remains even after the object deletion
				del agent_type
				self.assertTrue(os.path.exists(UpperCAmelCase))






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:Dict				     =						Path(get_tests_dir("fixtures/tests_samples/COCO")) / """000000039769.png"""
				SCREAMING_SNAKE_CASE_				:Tuple				     =						Image.open(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						AgentImage(UpperCAmelCase)

				self.assertTrue(path.samefile(agent_type.to_string()))
				self.assertTrue(image == agent_type.to_raw())

				# Ensure the path remains even after the object deletion
				del agent_type
				self.assertTrue(os.path.exists(UpperCAmelCase))







		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						Path(get_tests_dir("fixtures/tests_samples/COCO")) / """000000039769.png"""
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Image.open(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						AgentImage(UpperCAmelCase)

				self.assertFalse(path.samefile(agent_type.to_string()))
				self.assertTrue(image == agent_type.to_raw())

				# Ensure the path remains even after the object deletion
				del agent_type
				self.assertTrue(os.path.exists(UpperCAmelCase))




class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"""Hey!"""
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						AgentText(UpperCAmelCase)

				self.assertEqual(UpperCAmelCase   ,  agent_type.to_string())
				self.assertEqual(UpperCAmelCase   ,  agent_type.to_raw())
				self.assertEqual(UpperCAmelCase   ,  UpperCAmelCase)










'''simple docstring'''






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  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_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

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

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[Any]											= 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.")
  __A   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



