


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				(   _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase):
					@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	,      )							->					Tuple:



											'''simple docstring'''





											super().__init__()

											__magic_name__					=      nn.Linear(_lowerCAmelCase	,      _lowerCAmelCase	,      bias=_lowerCAmelCase  )

											__magic_name__					=      nn.Embedding(_lowerCAmelCase	,      _lowerCAmelCase  )
											__magic_name__					=      False

											__magic_name__					=      nn.Dropout(p=_lowerCAmelCase  )

											__magic_name__					=      TaConfig(
											    d_model=_lowerCAmelCase	,      num_heads=_lowerCAmelCase	,      d_kv=_lowerCAmelCase	,      d_ff=_lowerCAmelCase	,      feed_forward_proj=_lowerCAmelCase	,      dropout_rate=_lowerCAmelCase	,      is_decoder=_lowerCAmelCase	,      is_encoder_decoder=_lowerCAmelCase	,      )
											__magic_name__					=      nn.ModuleList()
											for lyr_num in range(_lowerCAmelCase  ):
																	__magic_name__					=      TaBlock(_lowerCAmelCase  )
																	self.encoders.append(_lowerCAmelCase  )

											__magic_name__					=      TaLayerNorm(_lowerCAmelCase  )
											__magic_name__					=      nn.Dropout(p=_lowerCAmelCase  )







					def 	a			(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Tuple	,      _lowerCAmelCase       :						Optional[int]  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      self.input_proj(_lowerCAmelCase  )

											# terminal relative positional encodings
											__magic_name__					=      encoder_inputs.shape[1]
											__magic_name__					=      torch.arange(_lowerCAmelCase	,      device=encoder_inputs.device  )

											__magic_name__					=      encoder_inputs_mask.sum(-1  )
											__magic_name__					=      torch.roll(input_positions.unsqueeze(0  )	,      tuple(seq_lens.tolist()  )	,      dims=0  )
											x += self.position_encoding(_lowerCAmelCase  )

											__magic_name__					=      self.dropout_pre(_lowerCAmelCase  )

											# inverted the attention mask
											__magic_name__					=      encoder_inputs.size()
											__magic_name__					=      self.get_extended_attention_mask(_lowerCAmelCase	,      _lowerCAmelCase  )

											for lyr in self.encoders:
																	__magic_name__					=      lyr(_lowerCAmelCase	,      _lowerCAmelCase  )[0]
											__magic_name__					=      self.layer_norm(_lowerCAmelCase  )

											return self.dropout_post(_lowerCAmelCase  ), encoder_inputs_mask





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase			:					List[Any]         =							logging.get_logger(__name__)

UpperCamelCase			:					Tuple         =							{
    '''mnaylor/mega-base-wikitext''': '''https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json''',
}

class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			"mega"
					def __init__(						self       :						Optional[int]	,      _lowerCAmelCase       :						List[Any]=3_0_5_2_2	,      _lowerCAmelCase       :						List[str]=1_2_8	,      _lowerCAmelCase       :						Dict=4	,      _lowerCAmelCase       :						Optional[Any]=2_5_6	,      _lowerCAmelCase       :						Optional[Any]=1_6	,      _lowerCAmelCase       :						Dict=True	,      _lowerCAmelCase       :						Optional[Any]=6_4	,      _lowerCAmelCase       :						Optional[Any]=False	,      _lowerCAmelCase       :						Optional[int]=-1	,      _lowerCAmelCase       :						Any=None	,      _lowerCAmelCase       :						List[Any]=True	,      _lowerCAmelCase       :						List[Any]="scalenorm"	,      _lowerCAmelCase       :						str=True	,      _lowerCAmelCase       :						Union[str, Any]="silu"	,      _lowerCAmelCase       :						Optional[Any]="softmax"	,      _lowerCAmelCase       :						Optional[Any]=0.1	,      _lowerCAmelCase       :						Dict=0.1	,      _lowerCAmelCase       :						Any=0.1	,      _lowerCAmelCase       :						Optional[Any]=False	,      _lowerCAmelCase       :						Any=True	,      _lowerCAmelCase       :						int=2_5_6	,      _lowerCAmelCase       :						List[str]=True	,      _lowerCAmelCase       :						str=0.1	,      _lowerCAmelCase       :						List[str]=2_0_4_8	,      _lowerCAmelCase       :						Optional[Any]=False	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Dict=0.02	,      _lowerCAmelCase       :						str=0.2	,      _lowerCAmelCase       :						List[str]=0.02	,      _lowerCAmelCase       :						int=1.0	,      _lowerCAmelCase       :						Optional[int]=1	,      _lowerCAmelCase       :						List[str]=0	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Optional[Any]="rotary"	,      _lowerCAmelCase       :						int=None	,      _lowerCAmelCase       :						int=True	,      _lowerCAmelCase       :						Optional[Any]=True	,      **_lowerCAmelCase       :						str	,      )							->					Any:



											'''simple docstring'''





											super().__init__(pad_token_id=_lowerCAmelCase	,      bos_token_id=_lowerCAmelCase	,      eos_token_id=_lowerCAmelCase	,      **_lowerCAmelCase  )

											__magic_name__					=      vocab_size
											__magic_name__					=      hidden_size
											__magic_name__					=      num_hidden_layers
											__magic_name__					=      activation
											__magic_name__					=      attention_activation
											__magic_name__					=      intermediate_size
											__magic_name__					=      ema_projection_size
											__magic_name__					=      bidirectional
											__magic_name__					=      shared_representation_size
											__magic_name__					=      use_chunking
											__magic_name__					=      chunk_size
											__magic_name__					=      truncation
											__magic_name__					=      normalize_before_mega
											__magic_name__					=      normalization_type
											__magic_name__					=      norm_affine
											__magic_name__					=      dropout_prob
											__magic_name__					=      hidden_dropout_prob
											__magic_name__					=      attention_probs_dropout_prob
											__magic_name__					=      use_feature_dropout
											__magic_name__					=      use_normalized_ffn
											__magic_name__					=      nffn_hidden_size
											__magic_name__					=      normalize_before_ffn
											__magic_name__					=      nffn_activation_dropout_prob
											__magic_name__					=      max_positions
											__magic_name__					=      add_token_type_embeddings
											__magic_name__					=      type_vocab_size
											__magic_name__					=      initializer_range
											__magic_name__					=      ema_delta_alpha_range
											__magic_name__					=      ema_beta_range
											__magic_name__					=      ema_gamma_omega_range
											__magic_name__					=      relative_positional_bias
											__magic_name__					=      use_cache
											__magic_name__					=      classifier_dropout
											__magic_name__					=      add_lm_hidden_dense_layer
											__magic_name__					=      1  # not used but required by Hugging Face







class      __snake_case				(   _UpperCAmelCase):
					@property
					def 	a			(						self       :						int  )							->					Mapping[str, Mapping[int, str]]:



											'''simple docstring'''





											if self.task == "multiple-choice":
																	__magic_name__					=      {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																	__magic_name__					=      {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]  )

