





"""simple docstring"""
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
  import tensorflow as tf

  from transformers import TFBlipTextModel
  from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=7		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=99		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=37		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=None		,)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  parent
             __SCREAMING_SNAKE_CASE       :Any		    =  batch_size
             __SCREAMING_SNAKE_CASE       :List[str]		    =  seq_length
             __SCREAMING_SNAKE_CASE       :int		    =  is_training
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  use_input_mask
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  use_labels
             __SCREAMING_SNAKE_CASE       :int		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :int		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  projection_dim
             __SCREAMING_SNAKE_CASE       :str		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :Dict		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :int		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Any		    =  dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Dict		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :int		    =  scope
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  bos_token_id




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  None
             if self.use_input_mask:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  random_attention_mask([self.batch_size, self.seq_length]							)

             if input_mask is not None:
                   __SCREAMING_SNAKE_CASE       :Any		    =  input_mask.numpy()
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :str		    =  input_mask.shape
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  np.random.randint(1		,seq_length - 1		,size=(batch_size,)							)
                   for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE__							):
                         __SCREAMING_SNAKE_CASE       :Dict		    =  1
                         __SCREAMING_SNAKE_CASE       :Any		    =  0

             __SCREAMING_SNAKE_CASE       :Dict		    =  self.get_config()

             return config, input_ids, tf.convert_to_tensor(SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             return BlipTextConfig(
                 vocab_size=self.vocab_size		,hidden_size=self.hidden_size		,projection_dim=self.projection_dim		,num_hidden_layers=self.num_hidden_layers		,num_attention_heads=self.num_attention_heads		,intermediate_size=self.intermediate_size		,dropout=self.dropout		,attention_dropout=self.attention_dropout		,max_position_embeddings=self.max_position_embeddings		,initializer_range=self.initializer_range		,bos_token_id=self.bos_token_id		,)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  TFBlipTextModel(config=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,training=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Any		    =  model(SCREAMING_SNAKE_CASE__		,training=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)							)
             self.parent.assertEqual(result.pooler_output.shape		,(self.batch_size, self.hidden_size)							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.prepare_config_and_inputs()
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  config_and_inputs
             __SCREAMING_SNAKE_CASE       :List[str]		    =  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_tf
class  _SCREAMING_SNAKE_CASE(       A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  (TFBlipTextModel,) if is_tf_available() else ()
       SCREAMING_SNAKE_CASE_       :				Dict     						=  False
       SCREAMING_SNAKE_CASE_       :				Dict     						=  False
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  False




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  BlipTextModelTester(self							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  ConfigTester(self		,config_class=SCREAMING_SNAKE_CASE__		,hidden_size=37							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             pass




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





             """simple docstring"""






             pass




       @unittest.skip(reason='''Blip does not use inputs_embeds'''							)
       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             pass




       @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''							)
       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             pass




       @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''							)
       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             pass




       @slow
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  TFBlipTextModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__=True							)      ->				Union[str, Any]:





             """simple docstring"""






             super().test_pt_tf_model_equivalence(allow_missing_keys=SCREAMING_SNAKE_CASE__							)






"""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)