






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__	)








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	)
