






'''simple docstring'''
import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
  from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def        _a(     UpperCamelCase__ :  str,		UpperCamelCase__ :  Union[str, Any],		UpperCamelCase__ :  Dict,		UpperCamelCase__ :  int=None,		UpperCamelCase__ :  List[Any]=None,		UpperCamelCase__ :  Optional[int]=None,		UpperCamelCase__ :  Union[str, Any]=None,		UpperCamelCase__ :  Any=None,		):




      '''simple docstring'''






      if attention_mask is None:
            SCREAMING_SNAKE_CASE__   : Dict													=input_ids.ne(config.pad_token_id							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__   : str													=decoder_input_ids.ne(config.pad_token_id							)
      if head_mask is None:
            SCREAMING_SNAKE_CASE__   : Union[str, Any]													=torch.ones(config.encoder_layers,		config.encoder_attention_heads,		device=lowerCamelCase__							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE__   : Union[str, Any]													=torch.ones(config.decoder_layers,		config.decoder_attention_heads,		device=lowerCamelCase__							)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.ones(config.decoder_layers,		config.decoder_attention_heads,		device=lowerCamelCase__							)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }






class    __SCREAMING_SNAKE_CASE					:




 def __init__(					self		:							Tuple    ,					__lowercase		:							str    ,					__lowercase		:							List[Any]=13    ,					__lowercase		:							Union[str, Any]=7    ,					__lowercase		:							Any=True    ,					__lowercase		:							str=False    ,					__lowercase		:							Optional[Any]=99    ,					__lowercase		:							Optional[int]=16    ,					__lowercase		:							Union[str, Any]=2    ,					__lowercase		:							Tuple=4    ,					__lowercase		:							int=4    ,					__lowercase		:							Dict="relu"    ,					__lowercase		:							Optional[int]=0.1    ,					__lowercase		:							List[Any]=0.1    ,					__lowercase		:							str=0.0    ,					__lowercase		:							Dict=0.0    ,					__lowercase		:							Any=20    ,					__lowercase		:							List[str]=2    ,					__lowercase		:							str=1    ,					__lowercase		:							Dict=0    ,					)		->  int:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=parent
       SCREAMING_SNAKE_CASE__   : int													=batch_size
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=seq_length
       SCREAMING_SNAKE_CASE__   : List[str]													=is_training
       SCREAMING_SNAKE_CASE__   : str													=use_labels
       SCREAMING_SNAKE_CASE__   : int													=vocab_size
       SCREAMING_SNAKE_CASE__   : Optional[int]													=hidden_size
       SCREAMING_SNAKE_CASE__   : str													=num_hidden_layers
       SCREAMING_SNAKE_CASE__   : Tuple													=num_attention_heads
       SCREAMING_SNAKE_CASE__   : Dict													=intermediate_size
       SCREAMING_SNAKE_CASE__   : str													=hidden_act
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   : Any													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   : int													=encoder_layerdrop
       SCREAMING_SNAKE_CASE__   : List[Any]													=decoder_layerdrop
       SCREAMING_SNAKE_CASE__   : int													=max_position_embeddings
       SCREAMING_SNAKE_CASE__   : Tuple													=eos_token_id
       SCREAMING_SNAKE_CASE__   : Tuple													=pad_token_id
       SCREAMING_SNAKE_CASE__   : List[Any]													=bos_token_id




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Optional[int]:
       SCREAMING_SNAKE_CASE__   : Any													=ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size					)
       SCREAMING_SNAKE_CASE__   : str													=self.eos_token_id  # Eos Token
       SCREAMING_SNAKE_CASE__   : Optional[int]													=ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size					)

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for M2M100 the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       SCREAMING_SNAKE_CASE__   : List[str]													=input_ids.clamp(self.pad_token_id + 1					)
       SCREAMING_SNAKE_CASE__   : Any													=decoder_input_ids.clamp(self.pad_token_id + 1					)

       SCREAMING_SNAKE_CASE__   : Optional[Any]													=self.get_config()
       SCREAMING_SNAKE_CASE__   : Dict													=prepare_mam_aaa_inputs_dict(__lowercase    ,					__lowercase    ,					__lowercase					)
       return config, inputs_dict




 def    __magic_name__		(					self		:							Tuple					)		->  Any:
       return MaMaaaConfig(
           vocab_size=self.vocab_size    ,					d_model=self.hidden_size    ,					encoder_layers=self.num_hidden_layers    ,					decoder_layers=self.num_hidden_layers    ,					encoder_attention_heads=self.num_attention_heads    ,					decoder_attention_heads=self.num_attention_heads    ,					encoder_ffn_dim=self.intermediate_size    ,					decoder_ffn_dim=self.intermediate_size    ,					dropout=self.hidden_dropout_prob    ,					attention_dropout=self.attention_probs_dropout_prob    ,					encoder_layerdrop=self.encoder_layerdrop    ,					decoder_layerdrop=self.decoder_layerdrop    ,					max_position_embeddings=self.max_position_embeddings    ,					eos_token_id=self.eos_token_id    ,					bos_token_id=self.bos_token_id    ,					pad_token_id=self.pad_token_id    ,					)




 def    __magic_name__		(					self		:							Dict					)		->  Tuple:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Optional[int]													=self.prepare_config_and_inputs()
       return config, inputs_dict




 def    __magic_name__		(					self		:							Tuple    ,					__lowercase		:							int    ,					__lowercase		:							List[str]					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Tuple													=MaMaaaModel(config=__lowercase					).get_decoder().to(__lowercase					).eval()
       SCREAMING_SNAKE_CASE__   : str													=inputs_dict['''input_ids''']
       SCREAMING_SNAKE_CASE__   : List[str]													=inputs_dict['''attention_mask''']
       SCREAMING_SNAKE_CASE__   : str													=inputs_dict['''head_mask''']

       # first forward pass
       SCREAMING_SNAKE_CASE__   : str													=model(__lowercase    ,					attention_mask=__lowercase    ,					head_mask=__lowercase    ,					use_cache=__lowercase					)

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Optional[Any]													=outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__   : int													=ids_tensor((self.batch_size, 3)    ,					config.vocab_size					)
       SCREAMING_SNAKE_CASE__   : Tuple													=ids_tensor((self.batch_size, 3)    ,					2					)

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.cat([input_ids, next_tokens]    ,					dim=-1					)
       SCREAMING_SNAKE_CASE__   : Tuple													=torch.cat([attention_mask, next_attn_mask]    ,					dim=-1					)

       SCREAMING_SNAKE_CASE__   : Optional[int]													=model(__lowercase    ,					attention_mask=__lowercase					)['''last_hidden_state''']
       SCREAMING_SNAKE_CASE__   : Tuple													=model(__lowercase    ,					attention_mask=__lowercase    ,					past_key_values=__lowercase					)[
           '''last_hidden_state'''
       ]

       # select random slice
       SCREAMING_SNAKE_CASE__   : int													=ids_tensor((1,)    ,					output_from_past.shape[-1]					).item()
       SCREAMING_SNAKE_CASE__   : Dict													=output_from_no_past[:, -3:, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE__   : List[str]													=output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(__lowercase    ,					__lowercase    ,					atol=1e-2					)					)




 def    __magic_name__		(					self		:							List[Any]    ,					__lowercase		:							Optional[int]    ,					__lowercase		:							List[str]					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : List[str]													=MaMaaaModel(config=__lowercase					).to(__lowercase					).eval()
       SCREAMING_SNAKE_CASE__   : List[str]													=model(**__lowercase					)

       SCREAMING_SNAKE_CASE__   : Optional[int]													=outputs.encoder_last_hidden_state
       SCREAMING_SNAKE_CASE__   : Optional[int]													=outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__   : Optional[int]													=model.get_encoder()
             encoder.save_pretrained(__lowercase					)
             SCREAMING_SNAKE_CASE__   : int													=MaMaaaEncoder.from_pretrained(__lowercase					).to(__lowercase					)

       SCREAMING_SNAKE_CASE__   : List[str]													=encoder(inputs_dict['''input_ids''']    ,					attention_mask=inputs_dict['''attention_mask''']					)[
           0
       ]

       self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3					)

       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__   : int													=model.get_decoder()
             decoder.save_pretrained(__lowercase					)
             SCREAMING_SNAKE_CASE__   : Any													=MaMaaaDecoder.from_pretrained(__lowercase					).to(__lowercase					)

       SCREAMING_SNAKE_CASE__   : Dict													=decoder(
           input_ids=inputs_dict['''decoder_input_ids''']    ,					attention_mask=inputs_dict['''decoder_attention_mask''']    ,					encoder_hidden_states=__lowercase    ,					encoder_attention_mask=inputs_dict['''attention_mask''']    ,					)[0]

       self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3					)






@require_torch
class    __SCREAMING_SNAKE_CASE					(	snake_case_		, snake_case_		, snake_case_		, unittest.TestCase ):
 snake_case_     							=							(
     (
         MaMaaaModel,
         MaMaaaForConditionalGeneration,
     )
     if is_torch_available()
     else ()
 )
 snake_case_     							=							(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
 snake_case_     							=							(
     {
         """conversational""": MaMaaaForConditionalGeneration,
         """feature-extraction""": MaMaaaModel,
         """summarization""": MaMaaaForConditionalGeneration,
         """text2text-generation""": MaMaaaForConditionalGeneration,
         """translation""": MaMaaaForConditionalGeneration,
     }
     if is_torch_available()
     else {}
 )
 snake_case_     							=							True
 snake_case_     							=							True
 snake_case_     							=							False
 snake_case_     							=							False




 def    __magic_name__		(					self		:							Any    ,					__lowercase		:							int    ,					__lowercase		:							Union[str, Any]    ,					__lowercase		:							Any    ,					__lowercase		:							int    ,					__lowercase		:							Optional[Any]					)		->  int:
       if pipeline_test_casse_name == "TranslationPipelineTests":
             # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
             # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
             return True

       return False




 def    __magic_name__		(					self		:							Dict					)		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=MaMaaaModelTester(self					)
       SCREAMING_SNAKE_CASE__   : List[Any]													=ConfigTester(self    ,					config_class=__lowercase					)




 def    __magic_name__		(					self		:							int					)		->  str:
       self.config_tester.run_common_tests()




 def    __magic_name__		(					self		:							Optional[Any]					)		->  Optional[int]:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Optional[int]													=self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__   : int													=model_class(__lowercase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(__lowercase					)
                   SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : int													=model_class.from_pretrained(__lowercase    ,					output_loading_info=__lowercase					)
             self.assertEqual(info['''missing_keys''']    ,					[]					)




 def    __magic_name__		(					self		:							int					)		->  int:
       SCREAMING_SNAKE_CASE__   : Any													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowercase					)




 def    __magic_name__		(					self		:							str					)		->  Any:
       SCREAMING_SNAKE_CASE__   : List[Any]													=self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*__lowercase					)




 def    __magic_name__		(					self		:							List[Any]					)		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : Tuple													=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
             SCREAMING_SNAKE_CASE__   : str													=model_class(__lowercase					)
             model.to(__lowercase					)
             model.eval()

             SCREAMING_SNAKE_CASE__   : Optional[int]													=copy.deepcopy(self._prepare_for_class(__lowercase    ,					__lowercase					)					)

             if not self.is_encoder_decoder:
                   SCREAMING_SNAKE_CASE__   : Tuple													=inputs['''input_ids''']
                   del inputs["input_ids"]
             else:
                   SCREAMING_SNAKE_CASE__   : Tuple													=inputs['''input_ids''']
                   SCREAMING_SNAKE_CASE__   : Optional[Any]													=inputs.get('''decoder_input_ids'''    ,					__lowercase					)
                   del inputs["input_ids"]
                   inputs.pop('''decoder_input_ids'''    ,					__lowercase					)

             SCREAMING_SNAKE_CASE__   : Tuple													=model.get_input_embeddings()
             if not self.is_encoder_decoder:
                   SCREAMING_SNAKE_CASE__   : Dict													=wte(__lowercase					)
             else:
                   SCREAMING_SNAKE_CASE__   : List[Any]													=wte(__lowercase					)
                   SCREAMING_SNAKE_CASE__   : str													=wte(__lowercase					)

             with torch.no_grad():
                   model(**__lowercase					)[0]




 def    __magic_name__		(					self		:							List[str]					)		->  int:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : str													=self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=input_dict['''input_ids''']
       SCREAMING_SNAKE_CASE__   : Tuple													=input_ids.ne(1					).to(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=MaMaaaForConditionalGeneration(__lowercase					).eval().to(__lowercase					)
       if torch_device == "cuda":
             model.half()
       model.generate(__lowercase    ,					attention_mask=__lowercase					)
       model.generate(num_beams=4    ,					do_sample=__lowercase    ,					early_stopping=__lowercase    ,					num_return_sequences=3					)





def        _a(     UpperCamelCase__ :  Dict							):




      '''simple docstring'''






      return torch.tensor(lowerCamelCase__,		dtype=torch.long,		device=lowerCamelCase__							)


a_ 		=       1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):




 @cached_property
 def    __magic_name__		(					self		:							Dict					)		->  Optional[int]:
       return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''					)




 def    __magic_name__		(					self		:							int					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : int													=MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''					).to(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Any													=_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]					)
       SCREAMING_SNAKE_CASE__   : List[Any]													=_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=prepare_mam_aaa_inputs_dict(model.config    ,					__lowercase    ,					__lowercase					)
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__   : Any													=model(**__lowercase					)[0]
       SCREAMING_SNAKE_CASE__   : Any													=torch.Size((1, 11, 10_24)					)
       self.assertEqual(output.shape    ,					__lowercase					)
       # change to expected output here
       SCREAMING_SNAKE_CASE__   : List[str]													=torch.tensor(
           [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]    ,					device=__lowercase					)
       self.assertTrue(torch.allclose(output[:, :3, :3]    ,					__lowercase    ,					atol=__lowercase					)					)




 def    __magic_name__		(					self		:							int					)		->  Optional[int]:
       SCREAMING_SNAKE_CASE__   : str													=MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''					).to(__lowercase					)

       # change to intended input
       SCREAMING_SNAKE_CASE__   : Tuple													=_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]					)
       SCREAMING_SNAKE_CASE__   : Any													=_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]					)
       SCREAMING_SNAKE_CASE__   : int													=prepare_mam_aaa_inputs_dict(model.config    ,					__lowercase    ,					__lowercase					)
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__   : Any													=model(**__lowercase					)[0]
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=torch.Size((1, 11, model.config.vocab_size)					)
       self.assertEqual(output.shape    ,					__lowercase					)
       # change to expected output here
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=torch.tensor(
           [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]    ,					device=__lowercase					)
       self.assertTrue(torch.allclose(output[:, :3, :3]    ,					__lowercase    ,					atol=__lowercase					)					)




 def    __magic_name__		(					self		:							int					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : Dict													=MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''					).to(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''    ,					src_lang='''fr'''    ,					tgt_lang='''en'''					)

       SCREAMING_SNAKE_CASE__   : Optional[int]													=[
           '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
           '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
           '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
           ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
           ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
       ]

       # The below article tests that we don't add any hypotheses outside of the top n_beams
       SCREAMING_SNAKE_CASE__   : List[str]													=tokenizer(__lowercase    ,					padding=__lowercase    ,					return_tensors='''pt'''					)

       SCREAMING_SNAKE_CASE__   : List[str]													=model.generate(
           input_ids=dct['''input_ids'''].to(__lowercase					)    ,					attention_mask=dct['''attention_mask'''].to(__lowercase					)    ,					num_beams=5    ,					forced_bos_token_id=tokenizer.get_lang_id('''en'''					)    ,					)

       SCREAMING_SNAKE_CASE__   : Optional[Any]													=[
           '''The NSA case highlights the total absence of intelligence debate''',
           '''I think there are two levels of response from the French government.''',
           '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
           ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
           ''' communications in France.''',
       ]

       SCREAMING_SNAKE_CASE__   : str													=tokenizer.batch_decode(
           hypotheses_batch.tolist()    ,					clean_up_tokenization_spaces=__lowercase    ,					skip_special_tokens=__lowercase					)
       assert generated == expected_en

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A				={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''WavLMForAudioFrameClassification''',
			    '''WavLMForCTC''',
			    '''WavLMForSequenceClassification''',
			    '''WavLMForXVector''',
			    '''WavLMModel''',
			    '''WavLMPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


