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 SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase						):







   @register_to_config
   def __init__(			self		:		str					,					lowerCamelCase_		:		List[str]					,					lowerCamelCase_		:		List[str]					,					lowerCamelCase_		:		int					,					lowerCamelCase_		:		int					,					lowerCamelCase_		:		List[str]					,					lowerCamelCase_		:		Optional[Any]					,					lowerCamelCase_		:		Tuple					,					lowerCamelCase_		:		List[Any]					,					lowerCamelCase_		:		Union[str, Any]					,					lowerCamelCase_		:		List[str] = False					,					):





    """simple docstring"""


    super().__init__()

    UpperCamelCase          =	nn.Embedding(A__					,					A__							)

    UpperCamelCase          =	nn.Embedding(A__					,					A__							)
    UpperCamelCase          =	False

    UpperCamelCase          =	nn.Dropout(p=A__							)

    UpperCamelCase          =	TaConfig(
        vocab_size=A__					,					d_model=A__					,					num_heads=A__					,					d_kv=A__					,					d_ff=A__					,					dropout_rate=A__					,					feed_forward_proj=A__					,					is_decoder=A__					,					is_encoder_decoder=A__					,					)

    UpperCamelCase          =	nn.ModuleList()
    for lyr_num in range(A__							):
     UpperCamelCase          =	TaBlock(A__							)
     self.encoders.append(A__							)

    UpperCamelCase          =	TaLayerNorm(A__							)
    UpperCamelCase          =	nn.Dropout(p=A__							)







   def 		lowerCamelCase_     (			self		:		Optional[Any]					,					lowerCamelCase_		:		List[str]					,					lowerCamelCase_		:		Optional[int]							):





    """simple docstring"""


    UpperCamelCase          =	self.token_embedder(A__							)

    UpperCamelCase          =	encoder_input_tokens.shape[1]
    UpperCamelCase          =	torch.arange(A__					,					device=encoder_input_tokens.device							)
    x += self.position_encoding(A__							)

    UpperCamelCase          =	self.dropout_pre(A__							)

    # inverted the attention mask
    UpperCamelCase          =	encoder_input_tokens.size()
    UpperCamelCase          =	self.get_extended_attention_mask(A__					,					A__							)

    for lyr in self.encoders:
     UpperCamelCase          =	lyr(A__					,					A__							)[0]
    UpperCamelCase          =	self.layer_norm(A__							)

    return self.dropout_post(A__							), encoder_inputs_mask



from typing import TYPE_CHECKING

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


A_       : List[str]         =			{
    '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:
	A_       : Optional[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

	A_       : Any         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



