



'''simple docstring'''

import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel


class 	a__     :


	def __init__(self : Optional[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : str=13,					__UpperCAmelCase : Any=7,					__UpperCAmelCase : Any=True,					__UpperCAmelCase : List[Any]=True,					__UpperCAmelCase : Optional[Any]=True,					__UpperCAmelCase : List[str]=True,					__UpperCAmelCase : Optional[int]=99,					__UpperCAmelCase : Optional[int]=32,					__UpperCAmelCase : Union[str, Any]=5,					__UpperCAmelCase : Dict=4,					__UpperCAmelCase : List[Any]=4,					__UpperCAmelCase : Union[str, Any]="gelu",					__UpperCAmelCase : int=0.0,					__UpperCAmelCase : int=0.1,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : Optional[int]=512,					__UpperCAmelCase : Optional[int]=16,					__UpperCAmelCase : str=2,					__UpperCAmelCase : Any=0.02,					__UpperCAmelCase : Dict=3,					__UpperCAmelCase : Tuple=4,					__UpperCAmelCase : Dict=None,					)     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= parent
					SCREAMING_SNAKE_CASE  :						List[str]      					= batch_size
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= seq_length
					SCREAMING_SNAKE_CASE  :						Tuple      					= is_training
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= use_input_mask
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= use_token_type_ids
					SCREAMING_SNAKE_CASE  :						List[Any]      					= use_labels
					SCREAMING_SNAKE_CASE  :						Tuple      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= hidden_size
					SCREAMING_SNAKE_CASE  :						int      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						int      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= intermediate_multiple_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= hidden_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						Any      					= weight_tying
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Tuple      					= type_vocab_size
					SCREAMING_SNAKE_CASE  :						List[str]      					= type_sequence_label_size
					SCREAMING_SNAKE_CASE  :						Any      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Dict      					= num_labels
					SCREAMING_SNAKE_CASE  :						Any      					= num_choices
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= scope


	def 				lowercase__  (self : Dict      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ids_tensor([self.batch_size, self.seq_length],					self.vocab_size      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= None
					if self.use_input_mask:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= random_attention_mask([self.batch_size, self.seq_length]      )

					SCREAMING_SNAKE_CASE  :						str      					= None
					if self.use_labels:
									SCREAMING_SNAKE_CASE  :						int      					= ids_tensor([self.batch_size, self.seq_length],					self.num_labels      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_config()

					return config, input_ids, input_mask, token_labels


	def 				lowercase__  (self : Tuple      )     ->			Dict:

					"""simple docstring"""

					return GPTNeoXJapaneseConfig(
					    vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_multiple_size=self.intermediate_multiple_size,					hidden_act=self.hidden_act,					hidden_dropout=self.hidden_dropout,					attention_dropout=self.attention_dropout,					weight_tying=self.weight_tying,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					is_decoder=__UpperCAmelCase,					initializer_range=self.initializer_range,					)


	def 				lowercase__  (self : str      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.prepare_config_and_inputs()

					SCREAMING_SNAKE_CASE  :						str      					= True

					return config, input_ids, input_mask, token_labels


	def 				lowercase__  (self : str,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : Tuple      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= GPTNeoXJapaneseModel(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= model(__UpperCAmelCase      )
					self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)      )


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= True
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= GPTNeoXJapaneseModel(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase      )
					self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)      )


	def 				lowercase__  (self : int,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= GPTNeoXJapaneseForCausalLM(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					labels=__UpperCAmelCase      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)      )


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : Dict      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= True
					SCREAMING_SNAKE_CASE  :						int      					= GPTNeoXJapaneseForCausalLM(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()

					# first forward pass
					SCREAMING_SNAKE_CASE  :						List[str]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					use_cache=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					SCREAMING_SNAKE_CASE  :						Dict      					= ids_tensor((self.batch_size, 3),					config.vocab_size      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ids_tensor((self.batch_size, 3),					vocab_size=2      )

					# append to next input_ids and
					SCREAMING_SNAKE_CASE  :						Dict      					= torch.cat([input_ids, next_tokens],					dim=-1      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.cat([input_mask, next_mask],					dim=-1      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					output_hidden_states=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= output_from_no_past['''hidden_states'''][0]
					SCREAMING_SNAKE_CASE  :						Tuple      					= model(
					    __UpperCAmelCase,					attention_mask=__UpperCAmelCase,					past_key_values=__UpperCAmelCase,					output_hidden_states=__UpperCAmelCase,					)['''hidden_states'''][0]

					# select random slice
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ids_tensor((1,),					output_from_past.shape[-1]      ).item()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output_from_no_past[:, -3:, random_slice_idx].detach()
					SCREAMING_SNAKE_CASE  :						Any      					= 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(__UpperCAmelCase,					__UpperCAmelCase,					atol=1e-3      )      )






	def 				lowercase__  (self : Dict      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= config_and_inputs
					SCREAMING_SNAKE_CASE  :						int      					= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict



@require_torch
class 	a__     (				_lowercase,			_lowercase,			unittest.TestCase	):
	__magic_name__		:    Union[str, Any]	    =     (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
	__magic_name__		:    List[str]	    =     (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
	__magic_name__		:    Any	    =     (
	    {"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
	    if is_torch_available()
	    else {}
	)
	__magic_name__		:    List[Any]	    =     False
	__magic_name__		:    Dict	    =     False
	__magic_name__		:    Any	    =     False
	__magic_name__		:    int	    =     False


	def 				lowercase__  (self : str      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= GPTNeoXJapaneseModelTester(self      )
					SCREAMING_SNAKE_CASE  :						str      					= ConfigTester(self,					config_class=__UpperCAmelCase,					hidden_size=37      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					self.config_tester.run_common_tests()


	def 				lowercase__  (self : List[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : List[str]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Dict      					= self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : Tuple      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.model_tester.prepare_config_and_inputs_for_decoder()

					SCREAMING_SNAKE_CASE  :						Dict      					= None

					self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any]      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase      )






	@slow
	def 				lowercase__  (self : Dict      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''abeja/gpt-neox-japanese-2.7b'''

					SCREAMING_SNAKE_CASE  :						Tuple      					= ['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [
					    '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
					    '''100年後に必要とされる会社は、「人」が中心の会社です。''',
					    '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
					    '''国境の長いトンネルを抜けると、そこは雪国だった。''',
					    '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
					]

					SCREAMING_SNAKE_CASE  :						Tuple      					= GPTNeoXJapaneseTokenizer.from_pretrained(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= GPTNeoXJapaneseForCausalLM.from_pretrained(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
					for prompt in prompts:
									SCREAMING_SNAKE_CASE  :						int      					= tokenizer(__UpperCAmelCase,					return_tensors='''pt'''      ).input_ids
									SCREAMING_SNAKE_CASE  :						Any      					= model.generate(__UpperCAmelCase,					max_length=50      )
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= tokenizer.batch_decode(__UpperCAmelCase,					skip_special_tokens=__UpperCAmelCase      )
									predicted_outputs += generated_string
					self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )









'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     "wav2vec2"


	def __init__(self : Tuple,					__UpperCAmelCase : Tuple=32,					__UpperCAmelCase : int=768,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : Any=12,					__UpperCAmelCase : Any=3072,					__UpperCAmelCase : Optional[int]="gelu",					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : List[str]=0.1,					__UpperCAmelCase : List[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.0,					__UpperCAmelCase : str=0.0,					__UpperCAmelCase : Optional[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.02,					__UpperCAmelCase : Dict=1e-5,					__UpperCAmelCase : List[str]="group",					__UpperCAmelCase : str="gelu",					__UpperCAmelCase : str=(512, 512, 512, 512, 512, 512, 512),					__UpperCAmelCase : str=(5, 2, 2, 2, 2, 2, 2),					__UpperCAmelCase : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2),					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Optional[Any]=128,					__UpperCAmelCase : Tuple=16,					__UpperCAmelCase : str=False,					__UpperCAmelCase : int=True,					__UpperCAmelCase : Tuple=0.05,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : Optional[Any]=0.0,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Dict=0,					__UpperCAmelCase : Any=320,					__UpperCAmelCase : int=2,					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : Tuple=100,					__UpperCAmelCase : Any=256,					__UpperCAmelCase : Tuple=256,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Any="sum",					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : str=False,					__UpperCAmelCase : str=256,					__UpperCAmelCase : Union[str, Any]=(512, 512, 512, 512, 1500),					__UpperCAmelCase : Optional[int]=(5, 3, 3, 1, 1),					__UpperCAmelCase : Dict=(1, 2, 3, 1, 1),					__UpperCAmelCase : List[str]=512,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : List[Any]=1,					__UpperCAmelCase : List[str]=2,					__UpperCAmelCase : List[Any]=False,					__UpperCAmelCase : str=3,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : str=3,					__UpperCAmelCase : int=None,					__UpperCAmelCase : List[Any]=None,					**__UpperCAmelCase : int,					)     ->			Tuple:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase,					pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= feat_extract_norm
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= feat_extract_activation
					SCREAMING_SNAKE_CASE  :						Dict      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= conv_bias
					SCREAMING_SNAKE_CASE  :						Dict      					= num_conv_pos_embeddings
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_conv_pos_embedding_groups
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= len(self.conv_dim      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						str      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						str      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						int      					= hidden_dropout
					SCREAMING_SNAKE_CASE  :						Any      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						int      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						int      					= feat_proj_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= final_dropout
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= layerdrop
					SCREAMING_SNAKE_CASE  :						Tuple      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						Dict      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= do_stable_layer_norm
					SCREAMING_SNAKE_CASE  :						Dict      					= use_weighted_layer_sum

					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
					SCREAMING_SNAKE_CASE  :						str      					= apply_spec_augment
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_time_prob
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= mask_time_length
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= mask_time_min_masks
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_feature_prob
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_length
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					SCREAMING_SNAKE_CASE  :						Any      					= num_codevectors_per_group
					SCREAMING_SNAKE_CASE  :						int      					= num_codevector_groups
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= contrastive_logits_temperature
					SCREAMING_SNAKE_CASE  :						List[Any]      					= feat_quantizer_dropout
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_negatives
					SCREAMING_SNAKE_CASE  :						Any      					= codevector_dim
					SCREAMING_SNAKE_CASE  :						str      					= proj_codevector_dim
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= diversity_loss_weight

					# ctc loss
					SCREAMING_SNAKE_CASE  :						Tuple      					= ctc_loss_reduction
					SCREAMING_SNAKE_CASE  :						Any      					= ctc_zero_infinity

					# adapter
					SCREAMING_SNAKE_CASE  :						int      					= add_adapter
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_kernel_size
					SCREAMING_SNAKE_CASE  :						int      					= adapter_stride
					SCREAMING_SNAKE_CASE  :						List[str]      					= num_adapter_layers
					SCREAMING_SNAKE_CASE  :						Tuple      					= output_hidden_size or hidden_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_attn_dim

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						Dict      					= classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= xvector_output_dim






	@property
	def 				lowercase__  (self : Tuple      )     ->			Optional[Any]:

					"""simple docstring"""

					return functools.reduce(operator.mul,					self.conv_stride,					1      )




