"""simple docstring"""







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)



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



def       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					WavLMForAudioFrameClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['model.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.linear.bias']
   return model



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Tuple:
   __lowerCamelCase    		=					WavLMForXVector.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['connector.weight']
   __lowerCamelCase    		=					downstream_dict['connector.bias']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      __lowerCamelCase    		=					downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      __lowerCamelCase    		=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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



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

   __lowerCamelCase    		=					checkpoint['Downstream']

   __lowerCamelCase    		=					WavLMConfig.from_pretrained(lowercase_						)
   __lowerCamelCase    		=					WavaVecaFeatureExtractor.from_pretrained(
       lowercase_      , return_attention_mask=lowercase_      , do_normalize=lowercase_						)

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

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

   hf_feature_extractor.save_pretrained(lowercase_						)
   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Optional[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.''')
     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers.models.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					13
      __lowerCamelCase    		=					7
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					True
      __lowerCamelCase    		=					99
      __lowerCamelCase    		=					32
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					37
      __lowerCamelCase    		=					'gelu'
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					0.1
      __lowerCamelCase    		=					512
      __lowerCamelCase    		=					16
      __lowerCamelCase    		=					2
      __lowerCamelCase    		=					0.02
      __lowerCamelCase    		=					3
      __lowerCamelCase    		=					4
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					self.vocab_size - 1







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

      __lowerCamelCase    		=					None
      if self.use_input_mask:
         __lowerCamelCase    		=					random_attention_mask([self.batch_size, self.seq_length]	)

      __lowerCamelCase    		=					None
      if self.use_token_type_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

      __lowerCamelCase    		=					None
      if self.use_mc_token_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.num_choices] ,				self.seq_length	)

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					CTRLConfig(
          vocab_size=self.vocab_size ,				n_embd=self.hidden_size ,				n_layer=self.num_hidden_layers ,				n_head=self.num_attention_heads ,				n_positions=self.max_position_embeddings ,				pad_token_id=self.pad_token_id ,				)

      __lowerCamelCase    		=					ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,				2	)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					[input_ids, None, input_mask]  # None is the input for 'past'
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)

      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel(config=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				*_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'token_type_ids': token_type_ids,
          'labels': sequence_labels,
      }
      __lowerCamelCase    		=					TFCTRLForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()

      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs

      __lowerCamelCase    		=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_tf
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   __UpperCAmelCase	:							List[str]    =    (TFCTRLLMHeadModel,) if is_tf_available() else ()
   __UpperCAmelCase	:							Dict    =    (
       {
           "feature-extraction": TFCTRLModel,
           "text-classification": TFCTRLForSequenceClassification,
           "text-generation": TFCTRLLMHeadModel,
           "zero-shot": TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase	:							str    =    False
   __UpperCAmelCase	:							Tuple    =    False







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
         # config could not be created.
         return True

      return False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				n_embd=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_lm_head(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					[TFCTRLLMHeadModel]
      __lowerCamelCase    		=					[TFCTRLForSequenceClassification]

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.build()  # may be needed for the get_bias() call below
         assert isinstance(model.get_input_embeddings() ,				tf.keras.layers.Layer	)

         if model_class in list_lm_models:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            for k, v in name.items():
               assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.Variable	)
         elif model_class in list_other_models_with_output_ebd:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert isinstance(_SCREAMING_SNAKE_CASE ,				tf.keras.layers.Layer	)
            __lowerCamelCase    		=					model.get_bias()
            assert name is None
         else:
            __lowerCamelCase    		=					model.get_output_embeddings()
            assert x is None
            __lowerCamelCase    		=					model.get_bias()
            assert name is None







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					TFCTRLModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)






@require_tf
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					TFCTRLLMHeadModel.from_pretrained('ctrl'	)
      __lowerCamelCase    		=					tf.convert_to_tensor([[11859, 0, 1611, 8]] ,				dtype=tf.intaa	)  # Legal the president is
      __lowerCamelCase    		=					[
          11859,
          0,
          1611,
          8,
          5,
          150,
          26449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          20740,
          246533,
          246533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      __lowerCamelCase    		=					model.generate(_SCREAMING_SNAKE_CASE ,				do_sample=_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(output_ids[0].numpy().tolist() ,				_SCREAMING_SNAKE_CASE	)






