
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


lowercase		:						List[str] =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''ernie'''






							def __init__(					self						:			Optional[int]						, snake_case__						:			Union[str, Any]=3_0_5_2_2						, snake_case__						:			str=7_6_8						, snake_case__						:			Optional[int]=1_2						, snake_case__						:			Optional[Any]=1_2						, snake_case__						:			str=3_0_7_2						, snake_case__						:			Union[str, Any]="gelu"						, snake_case__						:			Tuple=0.1						, snake_case__						:			Optional[int]=0.1						, snake_case__						:			Optional[int]=5_1_2						, snake_case__						:			Union[str, Any]=2						, snake_case__						:			int=3						, snake_case__						:			Tuple=False						, snake_case__						:			List[str]=0.02						, snake_case__						:			str=1e-12						, snake_case__						:			Union[str, Any]=0						, snake_case__						:			Tuple="absolute"						, snake_case__						:			Optional[int]=True						, snake_case__						:			Tuple=None						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



												super().__init__(pad_token_id=snake_case__						, **snake_case__    )

												__a:						int		    =					vocab_size
												__a:						List[str]		    =					hidden_size
												__a:						List[str]		    =					num_hidden_layers
												__a:						List[Any]		    =					num_attention_heads
												__a:						Tuple		    =					hidden_act
												__a:						Dict		    =					intermediate_size
												__a:						int		    =					hidden_dropout_prob
												__a:						List[Any]		    =					attention_probs_dropout_prob
												__a:						Tuple		    =					max_position_embeddings
												__a:						Optional[int]		    =					type_vocab_size
												__a:						int		    =					task_type_vocab_size
												__a:						Tuple		    =					use_task_id
												__a:						Optional[Any]		    =					initializer_range
												__a:						Optional[Any]		    =					layer_norm_eps
												__a:						str		    =					position_embedding_type
												__a:						int		    =					use_cache
												__a:						int		    =					classifier_dropout






class    _snake_case  (						_lowercase       ):






							@property
							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												if self.task == "multiple-choice":
																	__a:						Dict		    =					{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																	__a:						Union[str, Any]		    =					{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												        ("""task_type_ids""", dynamic_axis),
												    ]    )


"""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						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

												__a:						Union[str, Any]		    =					encoder_inputs_mask.sum(-1    )
												__a:						str		    =					torch.roll(input_positions.unsqueeze(0    )						, tuple(seq_lens.tolist()    )						, dims=0    )
												x += self.position_encoding(snake_case__    )

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
