





"""simple docstring"""
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import (
      AutoProcessor,
      BertTokenizerFast,
      BlipImageProcessor,
      GPTaTokenizer,
      InstructBlipProcessor,
      PreTrainedTokenizerFast,
  )






@require_vision
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  tempfile.mkdtemp()

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  BlipImageProcessor()
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)

             __SCREAMING_SNAKE_CASE       :int		    =  InstructBlipProcessor(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             processor.save_pretrained(self.tmpdirname							)




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             return AutoProcessor.from_pretrained(self.tmpdirname		,**SCREAMING_SNAKE_CASE__							).tokenizer




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             return AutoProcessor.from_pretrained(self.tmpdirname		,**SCREAMING_SNAKE_CASE__							).image_processor




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             return AutoProcessor.from_pretrained(self.tmpdirname		,**SCREAMING_SNAKE_CASE__							).qformer_tokenizer




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             shutil.rmtree(self.tmpdirname							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  [np.random.randint(2_55		,size=(3, 30, 4_00)		,dtype=np.uinta							)]

             __SCREAMING_SNAKE_CASE       :Dict		    =  [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__		,0		,-1							)							) for x in image_inputs]

             return image_inputs




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  InstructBlipProcessor(
                 tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()		,qformer_tokenizer=self.get_qformer_tokenizer()		,)
             processor.save_pretrained(self.tmpdirname							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_tokenizer(bos_token='''(BOS)'''		,eos_token='''(EOS)'''							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__		,padding_value=1.0							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  InstructBlipProcessor.from_pretrained(
                 self.tmpdirname		,bos_token='''(BOS)'''		,eos_token='''(EOS)'''		,do_normalize=SCREAMING_SNAKE_CASE__		,padding_value=1.0							)

             self.assertEqual(processor.tokenizer.get_vocab()		,tokenizer_add_kwargs.get_vocab()							)
             self.assertIsInstance(processor.tokenizer		,SCREAMING_SNAKE_CASE__							)

             self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()							)
             self.assertIsInstance(processor.image_processor		,SCREAMING_SNAKE_CASE__							)
             self.assertIsInstance(processor.qformer_tokenizer		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_image_processor()
             __SCREAMING_SNAKE_CASE       :str		    =  self.get_tokenizer()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_qformer_tokenizer()

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  InstructBlipProcessor(
                 tokenizer=SCREAMING_SNAKE_CASE__		,image_processor=SCREAMING_SNAKE_CASE__		,qformer_tokenizer=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  self.prepare_image_inputs()

             __SCREAMING_SNAKE_CASE       :Dict		    =  image_processor(SCREAMING_SNAKE_CASE__		,return_tensors='''np'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  processor(images=SCREAMING_SNAKE_CASE__		,return_tensors='''np'''							)

             for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum()		,input_processor[key].sum()		,delta=1E-2							)




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_image_processor()
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.get_tokenizer()
             __SCREAMING_SNAKE_CASE       :Any		    =  self.get_qformer_tokenizer()

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  InstructBlipProcessor(
                 tokenizer=SCREAMING_SNAKE_CASE__		,image_processor=SCREAMING_SNAKE_CASE__		,qformer_tokenizer=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  """lower newer"""

             __SCREAMING_SNAKE_CASE       :Tuple		    =  processor(text=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  tokenizer(SCREAMING_SNAKE_CASE__		,return_token_type_ids=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  qformer_tokenizer(SCREAMING_SNAKE_CASE__		,return_token_type_ids=SCREAMING_SNAKE_CASE__							)

             for key in encoded_tokens.keys():
                   self.assertListEqual(encoded_tokens[key]		,encoded_processor[key]							)

             for key in encoded_tokens_qformer.keys():
                   self.assertListEqual(encoded_tokens_qformer[key]		,encoded_processor['''qformer_''' + key]							)




       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  self.get_image_processor()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_tokenizer()
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.get_qformer_tokenizer()

             __SCREAMING_SNAKE_CASE       :Tuple		    =  InstructBlipProcessor(
                 tokenizer=SCREAMING_SNAKE_CASE__		,image_processor=SCREAMING_SNAKE_CASE__		,qformer_tokenizer=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  """lower newer"""
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.prepare_image_inputs()

             __SCREAMING_SNAKE_CASE       :int		    =  processor(text=SCREAMING_SNAKE_CASE__		,images=SCREAMING_SNAKE_CASE__							)

             self.assertListEqual(
                 list(inputs.keys()							)		,['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']		,)

             # test if it raises when no input is passed
             with pytest.raises(SCREAMING_SNAKE_CASE__							):
                   processor()




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.get_image_processor()
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.get_tokenizer()
             __SCREAMING_SNAKE_CASE       :int		    =  self.get_qformer_tokenizer()

             __SCREAMING_SNAKE_CASE       :List[str]		    =  InstructBlipProcessor(
                 tokenizer=SCREAMING_SNAKE_CASE__		,image_processor=SCREAMING_SNAKE_CASE__		,qformer_tokenizer=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  processor.batch_decode(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)

             self.assertListEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.get_image_processor()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_tokenizer()
             __SCREAMING_SNAKE_CASE       :int		    =  self.get_qformer_tokenizer()

             __SCREAMING_SNAKE_CASE       :int		    =  InstructBlipProcessor(
                 tokenizer=SCREAMING_SNAKE_CASE__		,image_processor=SCREAMING_SNAKE_CASE__		,qformer_tokenizer=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  """lower newer"""
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.prepare_image_inputs()

             __SCREAMING_SNAKE_CASE       :Dict		    =  processor(text=SCREAMING_SNAKE_CASE__		,images=SCREAMING_SNAKE_CASE__							)

             self.assertListEqual(
                 list(inputs.keys()							)		,['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']		,)






"""simple docstring"""
import argparse

import torch

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


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



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



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		List[Any]			,       a_					:		List[Any]							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForAudioFrameClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.linear.weight''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.linear.bias''']
      return model



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

      __SCREAMING_SNAKE_CASE       :Any		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      __SCREAMING_SNAKE_CASE       :Dict		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict['''objective.W''']
      return model



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Union[str, Any]			,       a_					:		Any			,       a_					:		Union[str, Any]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :str		    =  torch.load(a_			,       map_location='''cpu'''							)

      __SCREAMING_SNAKE_CASE       :str		    =  checkpoint['''Downstream''']

      __SCREAMING_SNAKE_CASE       :str		    =  UniSpeechSatConfig.from_pretrained(a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  WavaVecaFeatureExtractor.from_pretrained(
          a_			,       return_attention_mask=a_			,       do_normalize=a_							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hf_config.architectures[0]
      if arch.endswith('''ForSequenceClassification'''							):
            __SCREAMING_SNAKE_CASE       :str		    =  convert_classification(a_			,       a_			,       a_							)
      elif arch.endswith('''ForAudioFrameClassification'''							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  convert_diarization(a_			,       a_			,       a_							)
      elif arch.endswith('''ForXVector'''							):
            __SCREAMING_SNAKE_CASE       :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:
            __SCREAMING_SNAKE_CASE       :Dict		    =  checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(a_							)
      hf_model.save_pretrained(a_							)


if __name__ == "__main__":
  lowerCamelCase_       =		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.")
  lowerCamelCase_       =		parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)