"""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)












from __future__ import annotations

import unittest

from transformers import 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 import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMModel,
		    TFXLMWithLMHeadModel,
		    XLMConfig,
		)




class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	Optional[Any]   , __lowerCamelCase							:	Any   , )   ->	Any:
							a       =     parent
							a       =     13
							a       =     7
							a       =     True
							a       =     True
							a       =     True
							a       =     True
							a       =     True
							a       =     False
							a       =     False
							a       =     False
							a       =     2
							a       =     99
							a       =     0
							a       =     32
							a       =     2
							a       =     4
							a       =     0.1
							a       =     0.1
							a       =     5_12
							a       =     16
							a       =     2
							a       =     0.02
							a       =     3
							a       =     4
							a       =     "last"
							a       =     True
							a       =     None
							a       =     0





				def 				__UpperCAmelCase				( self							:	int				)   ->	Tuple:
							a       =     ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size				)
							a       =     random_attention_mask([self.batch_size, self.seq_length]   , dtype=tf.floataa				)

							a       =     None
							if self.use_input_lengths:
										a       =     (
										    ids_tensor([self.batch_size]   , vocab_size=2				) + self.seq_length - 2
										)  # small variation of seq_length

							a       =     None
							if self.use_token_type_ids:
										a       =     ids_tensor([self.batch_size, self.seq_length]   , self.n_langs				)

							a       =     None
							a       =     None
							a       =     None
							if self.use_labels:
										a       =     ids_tensor([self.batch_size]   , self.type_sequence_label_size				)
										a       =     ids_tensor([self.batch_size, self.seq_length]   , self.num_labels				)
										a       =     ids_tensor([self.batch_size]   , 2   , dtype=tf.floataa				)
										a       =     ids_tensor([self.batch_size]   , self.num_choices				)

							a       =     XLMConfig(
							    vocab_size=self.vocab_size   , n_special=self.n_special   , emb_dim=self.hidden_size   , n_layers=self.num_hidden_layers   , n_heads=self.num_attention_heads   , dropout=self.hidden_dropout_prob   , attention_dropout=self.attention_probs_dropout_prob   , gelu_activation=self.gelu_activation   , sinusoidal_embeddings=self.sinusoidal_embeddings   , asm=self.asm   , causal=self.causal   , n_langs=self.n_langs   , max_position_embeddings=self.max_position_embeddings   , initializer_range=self.initializer_range   , summary_type=self.summary_type   , use_proj=self.use_proj   , bos_token_id=self.bos_token_id   , )

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	Any   , )   ->	Union[str, Any]:
							a       =     TFXLMModel(config=__lowerCamelCase				)
							a       =     {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
							a       =     model(__lowerCamelCase				)

							a       =     [input_ids, input_mask]
							a       =     model(__lowerCamelCase				)
							self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Any   , )   ->	Optional[Any]:
							a       =     TFXLMWithLMHeadModel(__lowerCamelCase				)

							a       =     {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
							a       =     model(__lowerCamelCase				)

							a       =     outputs

							self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)				)





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	List[Any]   , )   ->	int:
							a       =     TFXLMForQuestionAnsweringSimple(__lowerCamelCase				)

							a       =     {"input_ids": input_ids, "lengths": input_lengths}

							a       =     model(__lowerCamelCase				)

							self.parent.assertEqual(result.start_logits.shape   , (self.batch_size, self.seq_length)				)
							self.parent.assertEqual(result.end_logits.shape   , (self.batch_size, self.seq_length)				)





				def 				__UpperCAmelCase				( self							:	Dict   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Optional[int]   , )   ->	Optional[int]:
							a       =     TFXLMForSequenceClassification(__lowerCamelCase				)

							a       =     {"input_ids": input_ids, "lengths": input_lengths}

							a       =     model(__lowerCamelCase				)

							self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)				)





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Union[str, Any]   , )   ->	Optional[Any]:
							a       =     self.num_labels
							a       =     TFXLMForTokenClassification(config=__lowerCamelCase				)
							a       =     {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							a       =     model(__lowerCamelCase				)
							self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)				)





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	str   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Dict   , )   ->	List[Any]:
							a       =     self.num_choices
							a       =     TFXLMForMultipleChoice(config=__lowerCamelCase				)
							a       =     tf.tile(tf.expand_dims(__lowerCamelCase   , 1				)   , (1, self.num_choices, 1)				)
							a       =     tf.tile(tf.expand_dims(__lowerCamelCase   , 1				)   , (1, self.num_choices, 1)				)
							a       =     tf.tile(tf.expand_dims(__lowerCamelCase   , 1				)   , (1, self.num_choices, 1)				)
							a       =     {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							    "token_type_ids": multiple_choice_token_type_ids,
							}
							a       =     model(__lowerCamelCase				)
							self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)				)





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	str:
							a       =     self.prepare_config_and_inputs()
							(
							    (
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						
							)       =     config_and_inputs
							a       =     {
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "langs": token_type_ids,
							    "lengths": input_lengths,
							}
							return config, inputs_dict




@require_tf
class        snake_case__				(_UpperCamelCase       ,			_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				int									=   (
				    (
				        TFXLMModel,
				        TFXLMWithLMHeadModel,
				        TFXLMForSequenceClassification,
				        TFXLMForQuestionAnsweringSimple,
				        TFXLMForTokenClassification,
				        TFXLMForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_			:				List[str]									=   (
				    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				SCREAMING_SNAKE_CASE_			:				List[Any]									=   (
				    {
				        """feature-extraction""": TFXLMModel,
				        """fill-mask""": TFXLMWithLMHeadModel,
				        """question-answering""": TFXLMForQuestionAnsweringSimple,
				        """text-classification""": TFXLMForSequenceClassification,
				        """text-generation""": TFXLMWithLMHeadModel,
				        """token-classification""": TFXLMForTokenClassification,
				        """zero-shot""": TFXLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_			:				Tuple									=   False
				SCREAMING_SNAKE_CASE_			:				Dict									=   False





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	List[Any]				)   ->	Dict:
							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("Fast"				)
							):
										# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
										# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
										# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
										return True

							return False





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Tuple:
							a       =     TFXLMModelTester(self				)
							a       =     ConfigTester(self   , config_class=__lowerCamelCase   , emb_dim=37				)





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Optional[int]:
							self.config_tester.run_common_tests()





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	List[Any]:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Dict:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Dict:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Optional[int]:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[Any]:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_token_classification(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	str:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*__lowerCamelCase				)





				@slow
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[Any]:
							for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a       =     TFXLMModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)




@require_tf
class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				@slow
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	str:
							a       =     TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"				)
							a       =     tf.convert_to_tensor([[14, 4_47]]   , dtype=tf.intaa				)  # the president
							a       =     [
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							a       =     model.generate(__lowerCamelCase   , do_sample=__lowerCamelCase				)
							self.assertListEqual(output_ids[0].numpy().tolist()   , __lowerCamelCase				)






