code: 
  
  
  
  
  
  
  import unittest
  from unittest.util import safe_repr
  
  from transformers import AutoTokenizer, RwkvConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
     import torch
  
     from transformers import (
         RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
         RwkvForCausalLM,
         RwkvModel,
     )
     from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
  else:
     lowerCamelCase										=  False
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	List[str]   ,       snake_case__:	List[Any]   ,       snake_case__:	int=14   ,       snake_case__:	str=7   ,       snake_case__:	Union[str, Any]=True   ,       snake_case__:	Tuple=False   ,       snake_case__:	int=True   ,       snake_case__:	List[str]=True   ,       snake_case__:	List[Any]=True   ,       snake_case__:	str=99   ,       snake_case__:	str=32   ,       snake_case__:	Any=5   ,       snake_case__:	List[str]=37   ,       snake_case__:	Optional[Any]="gelu"   ,       snake_case__:	int=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	Optional[int]=512   ,       snake_case__:	List[Any]=16   ,       snake_case__:	Union[str, Any]=2   ,       snake_case__:	int=3   ,       snake_case__:	Union[str, Any]=4   ,       snake_case__:	int=None   ,       )  ->		int:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	parent
           _lowercase     =	batch_size
           _lowercase     =	seq_length
           _lowercase     =	is_training
           _lowercase     =	use_token_type_ids
           _lowercase     =	use_input_mask
           _lowercase     =	use_labels
           _lowercase     =	use_mc_token_ids
           _lowercase     =	vocab_size
           _lowercase     =	hidden_size
           _lowercase     =	num_hidden_layers
           _lowercase     =	intermediate_size
           _lowercase     =	hidden_act
           _lowercase     =	hidden_dropout_prob
           _lowercase     =	attention_probs_dropout_prob
           _lowercase     =	max_position_embeddings
           _lowercase     =	type_vocab_size
           _lowercase     =	type_sequence_label_size
           _lowercase     =	num_labels
           _lowercase     =	num_choices
           _lowercase     =	scope
           _lowercase     =	vocab_size - 1
           _lowercase     =	vocab_size - 1
           _lowercase     =	vocab_size - 1
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Union[str, Any]:
  
  
           """simple docstring"""
  
  
  
           return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b'	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       snake_case__:	Tuple=False   ,       snake_case__:	int=False   ,       snake_case__:	Optional[int]=False	)  ->		Dict:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size	)
  
           _lowercase     =	None
           if self.use_input_mask:
                 _lowercase     =	random_attention_mask([self.batch_size, self.seq_length]	)
  
           _lowercase     =	None
           if self.use_token_type_ids:
                 _lowercase     =	ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size	)
  
           _lowercase     =	None
           if self.use_mc_token_ids:
                 _lowercase     =	ids_tensor([self.batch_size, self.num_choices]   ,       self.seq_length	)
  
           _lowercase     =	None
           _lowercase     =	None
           _lowercase     =	None
           if self.use_labels:
                 _lowercase     =	ids_tensor([self.batch_size]   ,       self.type_sequence_label_size	)
                 _lowercase     =	ids_tensor([self.batch_size, self.seq_length]   ,       self.num_labels	)
                 _lowercase     =	ids_tensor([self.batch_size]   ,       self.num_choices	)
  
           _lowercase     =	self.get_config(
               gradient_checkpointing=snake_case__   ,       scale_attn_by_inverse_layer_idx=snake_case__   ,       reorder_and_upcast_attn=snake_case__   ,       )
  
           return (
               config,
               input_ids,
               input_mask,
               None,
               token_type_ids,
               mc_token_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       snake_case__:	Tuple=False   ,       snake_case__:	Dict=False   ,       snake_case__:	int=False	)  ->		List[Any]:
  
  
           """simple docstring"""
  
  
  
           return RwkvConfig(
               vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       intermediate_size=self.intermediate_size   ,       activation_function=self.hidden_act   ,       resid_pdrop=self.hidden_dropout_prob   ,       attn_pdrop=self.attention_probs_dropout_prob   ,       n_positions=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       use_cache=snake_case__   ,       bos_token_id=self.bos_token_id   ,       eos_token_id=self.eos_token_id   ,       pad_token_id=self.pad_token_id   ,       gradient_checkpointing=snake_case__   ,       scale_attn_by_inverse_layer_idx=snake_case__   ,       reorder_and_upcast_attn=snake_case__   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Any:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	self.get_config()
           _lowercase     =	300
           return config
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:
  
  
           """simple docstring"""
  
  
  
           (
               (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,        
           )     =	self.prepare_config_and_inputs()
  
           _lowercase     =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
           _lowercase     =	ids_tensor([self.batch_size, self.seq_length]   ,       vocab_size=2	)
  
           return (
               config,
               input_ids,
               input_mask,
               head_mask,
               token_type_ids,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int   ,       snake_case__:	Dict   ,       snake_case__:	Optional[Any]   ,       snake_case__:	List[Any]   ,       snake_case__:	Tuple   ,       snake_case__:	Optional[Any]   ,       *snake_case__:	List[str]	)  ->		int:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	True
           _lowercase     =	RwkvModel(config=snake_case__	)
           model.to(snake_case__	)
           model.eval()
  
           _lowercase     =	model(snake_case__	)
  
           self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)	)
           self.parent.assertEqual(len(result.hidden_states	)   ,       config.num_hidden_layers + 1	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	int   ,       snake_case__:	Tuple   ,       snake_case__:	str   ,       snake_case__:	str   ,       *snake_case__:	Tuple	)  ->		Union[str, Any]:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	RwkvForCausalLM(snake_case__	)
           model.to(snake_case__	)
           model.eval()
  
           _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
           self.parent.assertEqual(result.loss.shape   ,       ()	)
           self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]   ,       snake_case__:	Dict   ,       snake_case__:	List[str]   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	int   ,       *snake_case__:	Tuple	)  ->		Union[str, Any]:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	RwkvModel(config=snake_case__	)
           model.to(snake_case__	)
           model.eval()
  
           _lowercase     =	model(snake_case__	)
           _lowercase     =	outputs.last_hidden_state
  
           _lowercase     =	model(input_ids[:, :2]	)
           _lowercase     =	outputs.last_hidden_state
  
           # Using the state computed on the first inputs, we will get the same output
           _lowercase     =	model(input_ids[:, 2:]   ,       state=outputs.state	)
           _lowercase     =	outputs.last_hidden_state
  
           self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]   ,       dim=1	)   ,       snake_case__   ,       atol=1E-5	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	int   ,       snake_case__:	List[str]   ,       snake_case__:	int   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	int   ,       *snake_case__:	List[str]   ,       snake_case__:	Any=False	)  ->		Dict:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	RwkvForCausalLM(snake_case__	)
           model.to(snake_case__	)
           if gradient_checkpointing:
                 model.gradient_checkpointing_enable()
  
           _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
           self.parent.assertEqual(result.loss.shape   ,       ()	)
           self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)	)
           result.loss.backward()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Union[str, Any]:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	self.prepare_config_and_inputs()
  
           (
               (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,    (
               _lowercase    
           ) ,        
           )     =	config_and_inputs
  
           _lowercase     =	{'input_ids': input_ids}
  
           return config, inputs_dict
  
  
  
  
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_2_0      ,			reason="""See https://github.com/huggingface/transformers/pull/24204"""      )
  @require_torch
  class 					snake_case_							(       lowercase_      ,			lowercase_      ,			lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
     __lowerCAmelCase      =						(
         {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
     )
     # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False  # Rwkv does not support head masking
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Dict:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	RwkvModelTester(self	)
           _lowercase     =	ConfigTester(
               self   ,       config_class=snake_case__   ,       n_embd=37   ,       common_properties=['hidden_size', 'num_hidden_layers']	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	int   ,       snake_case__:	Optional[int]   ,       snake_case__:	Optional[Any]=None	)  ->		Any:
  
  
           """simple docstring"""
  
  
  
           if isinstance(snake_case__   ,       torch.Tensor	):
                 _lowercase ,    _lowercase     =	member.max().item(), member.min().item()
           elif isinstance(snake_case__   ,       snake_case__	) or isinstance(snake_case__   ,       snake_case__	):
                 _lowercase ,    _lowercase     =	max(snake_case__	), min(snake_case__	)
  
           if not isinstance(snake_case__   ,       snake_case__	):
                 raise TypeError('container should be a list or tuple'	)
           elif len(snake_case__	) != 2:
                 raise ValueError('container should have 2 elements'	)
  
           _lowercase ,    _lowercase     =	container
  
           _lowercase     =	(min_value >= expected_min) and (max_value <= expected_max)
  
           if not is_inside_interval:
                 _lowercase     =	'%s not found in %s' % (safe_repr(snake_case__	), safe_repr(snake_case__	))
                 self.fail(self._formatMessage(snake_case__   ,       snake_case__	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Optional[Any]:
  
  
           """simple docstring"""
  
  
  
           self.config_tester.run_common_tests()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[int]:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_rwkv_model(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Union[str, Any]:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_causl_lm(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Dict:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_state_equivalency(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Dict:
  
  
           """simple docstring"""
  
  
  
           _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                 _lowercase     =	model_class(config=snake_case__	)
                 for name, param in model.named_parameters():
                       if "time_decay" in name:
                             if param.requires_grad:
                                   self.assertTrue(param.data.max().item() == 3.0	)
                                   self.assertTrue(param.data.min().item() == -5.0	)
                       elif "time_first" in name:
                             if param.requires_grad:
                                   # check if it's a ones like
                                   self.assertTrue(torch.allclose(param.data   ,       torch.ones_like(param.data	)   ,       atol=1E-5   ,       rtol=1E-5	)	)
                       elif any(x in name for x in ['time_mix_key', 'time_mix_receptance']	):
                             if param.requires_grad:
                                   self.assertInterval(
                                       param.data   ,       [0.0, 1.0]   ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''   ,       )
                       elif "time_mix_value" in name:
                             if param.requires_grad:
                                   self.assertInterval(
                                       param.data   ,       [0.0, 1.3]   ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Any:
  
  
           """simple docstring"""
  
  
  
           _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase     =	True
  
           _lowercase     =	getattr(self.model_tester   ,       'seq_length'   ,       snake_case__	)
  
           for model_class in self.all_model_classes:
                 _lowercase     =	True
                 _lowercase     =	False
                 _lowercase     =	True
                 _lowercase     =	model_class(snake_case__	)
                 model.to(snake_case__	)
                 model.eval()
  
                 _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__	)
                 _lowercase     =	inputs['input_ids'].shape[0]
                 with torch.no_grad():
                       _lowercase     =	model(**snake_case__	)
                 _lowercase     =	outputs.attentions
                 self.assertEqual(len(snake_case__	)   ,       self.model_tester.num_hidden_layers	)
  
                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 _lowercase     =	True
                 _lowercase     =	model_class(snake_case__	)
                 model.to(snake_case__	)
                 model.eval()
  
                 _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__	)
                 _lowercase     =	inputs['input_ids'].shape[0]
                 with torch.no_grad():
                       _lowercase     =	model(**snake_case__	)
                 _lowercase     =	outputs.attentions
                 self.assertEqual(len(snake_case__	)   ,       self.model_tester.num_hidden_layers	)
  
                 self.assertListEqual(
                     list(attentions[0].shape[-3:]	)   ,       [batch_size, seq_len, config.hidden_size]   ,       )
                 _lowercase     =	len(snake_case__	)
  
                 # Check attention is always last and order is fine
                 _lowercase     =	True
                 _lowercase     =	True
                 _lowercase     =	model_class(snake_case__	)
                 model.to(snake_case__	)
                 model.eval()
  
                 _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__	)
                 _lowercase     =	inputs['input_ids'].shape[0]
                 with torch.no_grad():
                       _lowercase     =	model(**snake_case__	)
  
                 _lowercase     =	1
                 self.assertEqual(out_len + added_hidden_states   ,       len(snake_case__	)	)
  
                 _lowercase     =	outputs.attentions
  
                 self.assertEqual(len(snake_case__	)   ,       self.model_tester.num_hidden_layers	)
                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:]	)   ,       [batch_size, seq_len, config.hidden_size]   ,       )
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Tuple:
  
  
           """simple docstring"""
  
  
  
           for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _lowercase     =	RwkvModel.from_pretrained(snake_case__	)
                 self.assertIsNotNone(snake_case__	)
  
  
  
  
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_2_0      ,			reason="""See https://github.com/huggingface/transformers/pull/24204"""      )
  @slow
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Tuple:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	'RWKV/rwkv-4-169m-pile'
           _lowercase     =	AutoTokenizer.from_pretrained(self.model_id	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Union[str, Any]:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	'Hello my name is Jasmine and I am a newbie to the'
           _lowercase     =	RwkvForCausalLM.from_pretrained(self.model_id	).to(snake_case__	)
  
           _lowercase     =	self.tokenizer('Hello my name is'   ,       return_tensors='pt'	).input_ids.to(snake_case__	)
           _lowercase     =	model.generate(snake_case__   ,       max_new_tokens=10	)
           _lowercase     =	self.tokenizer.decode(output[0].tolist()	)
  
           self.assertEqual(snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		int:
  
  
           """simple docstring"""
  
  
  
           _lowercase     =	'Hello my name is Jasmine and I am a newbie to the'
  
           _lowercase     =	self.tokenizer('Hello my name is'   ,       return_tensors='pt'	).input_ids.to(snake_case__	)
           _lowercase     =	RwkvForCausalLM.from_pretrained(self.model_id   ,       torch_dtype=torch.bfloataa	).to(snake_case__	)
  
           _lowercase     =	model.generate(snake_case__   ,       max_new_tokens=10	)
           _lowercase     =	self.tokenizer.decode(output[0].tolist()	)
  
           self.assertEqual(snake_case__   ,       snake_case__	)
  
style_context: 
  
  
  
  
  
  
  import functools
  import operator
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
      # See all Hubert models at https://huggingface.co/models?filter=hubert
  }
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""hubert"""
  
     def __init__(    self:	Any   ,       snake_case__:	str=32   ,       snake_case__:	int=768   ,       snake_case__:	Dict=12   ,       snake_case__:	Optional[int]=12   ,       snake_case__:	List[str]=3_072   ,       snake_case__:	Optional[Any]="gelu"   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=True   ,       snake_case__:	Union[str, Any]=0.0   ,       snake_case__:	Union[str, Any]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	List[Any]=0.02   ,       snake_case__:	Dict=1E-5   ,       snake_case__:	Any="group"   ,       snake_case__:	Union[str, Any]="gelu"   ,       snake_case__:	List[str]=(512, 512, 512, 512, 512, 512, 512)   ,       snake_case__:	Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,       snake_case__:	Any=(10, 3, 3, 3, 3, 2, 2)   ,       snake_case__:	int=False   ,       snake_case__:	Any=128   ,       snake_case__:	Dict=16   ,       snake_case__:	Optional[Any]=False   ,       snake_case__:	Tuple=True   ,       snake_case__:	int=0.05   ,       snake_case__:	List[Any]=10   ,       snake_case__:	Tuple=2   ,       snake_case__:	List[Any]=0.0   ,       snake_case__:	Optional[int]=10   ,       snake_case__:	Optional[int]=0   ,       snake_case__:	Union[str, Any]="sum"   ,       snake_case__:	Union[str, Any]=False   ,       snake_case__:	int=False   ,       snake_case__:	Any=256   ,       snake_case__:	Union[str, Any]=0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	List[str]=2   ,       **snake_case__:	Optional[int]   ,       )  ->		Any:
  
  
         """simple docstring"""
  
  
  
         super().__init__(**snake_case__   ,       pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__	)
         _lowercase     =	hidden_size
         _lowercase     =	feat_extract_norm
         _lowercase     =	feat_extract_activation
         _lowercase     =	list(snake_case__	)
         _lowercase     =	list(snake_case__	)
         _lowercase     =	list(snake_case__	)
         _lowercase     =	conv_bias
         _lowercase     =	num_conv_pos_embeddings
         _lowercase     =	num_conv_pos_embedding_groups
         _lowercase     =	len(self.conv_dim	)
         _lowercase     =	num_hidden_layers
         _lowercase     =	intermediate_size
         _lowercase     =	hidden_act
         _lowercase     =	num_attention_heads
         _lowercase     =	hidden_dropout
         _lowercase     =	attention_dropout
         _lowercase     =	activation_dropout
         _lowercase     =	feat_proj_layer_norm
         _lowercase     =	feat_proj_dropout
         _lowercase     =	final_dropout
         _lowercase     =	layerdrop
         _lowercase     =	layer_norm_eps
         _lowercase     =	initializer_range
         _lowercase     =	vocab_size
         _lowercase     =	do_stable_layer_norm
         _lowercase     =	use_weighted_layer_sum
         _lowercase     =	classifier_proj_size
  
         if (
             (len(self.conv_stride	) != self.num_feat_extract_layers)
             or (len(self.conv_kernel	) != self.num_feat_extract_layers)
             or (len(self.conv_dim	) != self.num_feat_extract_layers)
         ):
             raise ValueError(
                 'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
                 ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
                 f''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
                 f''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)
  
         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         _lowercase     =	apply_spec_augment
         _lowercase     =	mask_time_prob
         _lowercase     =	mask_time_length
         _lowercase     =	mask_time_min_masks
         _lowercase     =	mask_feature_prob
         _lowercase     =	mask_feature_length
         _lowercase     =	mask_feature_min_masks
  
         # ctc loss
         _lowercase     =	ctc_loss_reduction
         _lowercase     =	ctc_zero_infinity
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         return functools.reduce(operator.mul   ,       self.conv_stride   ,       1	)
  