






'''simple docstring'''




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class    A_	(a_						,					a_						,					a_							):
							"""simple docstring"""




							@register_to_config
							def __init__(		self			:int       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:float       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:bool = False       ,							)				->  int:


										'''simple docstring'''




										super().__init__()

										snake_case_				:					Union[str, Any]   =       nn.Embedding(lowerCAmelCase__       ,							lowerCAmelCase__				)

										snake_case_				:					Dict   =       nn.Embedding(lowerCAmelCase__       ,							lowerCAmelCase__				)
										snake_case_				:					Any   =       False

										snake_case_				:					Optional[int]   =       nn.Dropout(p=lowerCAmelCase__				)

										snake_case_				:					int   =       TaConfig(
										    vocab_size=lowerCAmelCase__       ,							d_model=lowerCAmelCase__       ,							num_heads=lowerCAmelCase__       ,							d_kv=lowerCAmelCase__       ,							d_ff=lowerCAmelCase__       ,							dropout_rate=lowerCAmelCase__       ,							feed_forward_proj=lowerCAmelCase__       ,							is_decoder=lowerCAmelCase__       ,							is_encoder_decoder=lowerCAmelCase__       ,							)

										snake_case_				:					str   =       nn.ModuleList()
										for lyr_num in range(lowerCAmelCase__				):
													snake_case_				:					List[Any]   =       TaBlock(lowerCAmelCase__				)
													self.encoders.append(lowerCAmelCase__				)

										snake_case_				:					Dict   =       TaLayerNorm(lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       nn.Dropout(p=lowerCAmelCase__				)






							def 							_A	(		self			:Optional[Any]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Any				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.token_embedder(lowerCAmelCase__				)

										snake_case_				:					Tuple   =       encoder_input_tokens.shape[1]
										snake_case_				:					Union[str, Any]   =       torch.arange(lowerCAmelCase__       ,							device=encoder_input_tokens.device				)
										x += self.position_encoding(lowerCAmelCase__				)

										snake_case_				:					List[str]   =       self.dropout_pre(lowerCAmelCase__				)

										# inverted the attention mask
										snake_case_				:					int   =       encoder_input_tokens.size()
										snake_case_				:					str   =       self.get_extended_attention_mask(lowerCAmelCase__       ,							lowerCAmelCase__				)

										for lyr in self.encoders:
													snake_case_				:					Optional[int]   =       lyr(lowerCAmelCase__       ,							lowerCAmelCase__				)[0]
										snake_case_				:					Any   =       self.layer_norm(lowerCAmelCase__				)

										return self.dropout_post(lowerCAmelCase__				), encoder_inputs_mask












'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__lowerCamelCase   :					Dict   							=					{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					int   							=					[
		    '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoXJapaneseForCausalLM''',
		    '''GPTNeoXJapaneseLayer''',
		    '''GPTNeoXJapaneseModel''',
		    '''GPTNeoXJapanesePreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
		from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neox_japanese import (
				    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoXJapaneseForCausalLM,
				    GPTNeoXJapaneseLayer,
				    GPTNeoXJapaneseModel,
				    GPTNeoXJapanesePreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Optional[int]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




