



"""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 			__snake_case			(      _lowercase    ,	_lowercase    ,	_lowercase):




  @register_to_config
  def __init__(					self	:		Optional[int]		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		float		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		bool = False		,   ):






         """simple docstring"""






         super().__init__()

         _lowerCamelCase		:							str            =					nn.Embedding(__lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							Dict            =					nn.Embedding(__lowerCAmelCase		,   __lowerCAmelCase						)
         _lowerCamelCase		:							str            =					False

         _lowerCamelCase		:							List[Any]            =					nn.Dropout(p=__lowerCAmelCase						)

         _lowerCamelCase		:							Tuple            =					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		,   )

         _lowerCamelCase		:							Union[str, Any]            =					nn.ModuleList()
         for lyr_num in range(__lowerCAmelCase						):
                _lowerCamelCase		:							str            =					TaBlock(__lowerCAmelCase						)
                self.encoders.append(__lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					TaLayerNorm(__lowerCAmelCase						)
         _lowerCamelCase		:							Union[str, Any]            =					nn.Dropout(p=__lowerCAmelCase						)




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






         """simple docstring"""






         _lowerCamelCase		:							int            =					self.token_embedder(__lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					encoder_input_tokens.shape[1]
         _lowerCamelCase		:							int            =					torch.arange(__lowerCAmelCase		,   device=encoder_input_tokens.device						)
         x += self.position_encoding(__lowerCAmelCase						)

         _lowerCamelCase		:							Any            =					self.dropout_pre(__lowerCAmelCase						)

         # inverted the attention mask
         _lowerCamelCase		:							List[str]            =					encoder_input_tokens.size()
         _lowerCamelCase		:							Optional[int]            =					self.get_extended_attention_mask(__lowerCAmelCase		,   __lowerCAmelCase						)

         for lyr in self.encoders:
                _lowerCamelCase		:							Union[str, Any]            =					lyr(__lowerCAmelCase		,   __lowerCAmelCase						)[0]
         _lowerCamelCase		:							Any            =					self.layer_norm(__lowerCAmelCase						)

         return self.dropout_post(__lowerCAmelCase						), encoder_inputs_mask




from typing import TYPE_CHECKING

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


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			__A							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


