





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    None
					A__				 =    None
					A__				 =    None
					A__				 =    None

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase=1	,  _lowerCAmelCase=0	,  _lowerCAmelCase=2	,  _lowerCAmelCase=512	,  _lowerCAmelCase="cls"	,  _lowerCAmelCase=False	,  _lowerCAmelCase=True	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)
										lowerCamelCase__       =       project_dim
										lowerCamelCase__       =       pooler_fn
										lowerCamelCase__       =       learn_encoder
										lowerCamelCase__       =       use_attention_mask

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    [r"pooler", r"logit_scale"]
					A__				 =    [r"position_ids", r"predictions.decoder.bias"]
					A__				 =    "roberta"
					A__				 =    RobertaSeriesConfig





					def __init__( self	,  _lowerCAmelCase	):
										super().__init__(_lowerCAmelCase	)
										lowerCamelCase__       =       XLMRobertaModel(_lowerCAmelCase	)
										lowerCamelCase__       =       nn.Linear(config.hidden_size	,  config.project_dim	)
										lowerCamelCase__       =       getattr(_lowerCAmelCase	,  "has_pre_transformation"	,  _lowerCAmelCase	)
										if self.has_pre_transformation:
															lowerCamelCase__       =       nn.Linear(config.hidden_size	,  config.project_dim	)
															lowerCamelCase__       =       nn.LayerNorm(config.hidden_size	,  eps=config.layer_norm_eps	)
										self.post_init()






					def 					__magic_name__ ( self	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  ):
										lowerCamelCase__       =       return_dict if return_dict is not None else self.config.use_return_dict

										lowerCamelCase__       =       self.base_model(
										    input_ids=_lowerCAmelCase	,  attention_mask=_lowerCAmelCase	,  token_type_ids=_lowerCAmelCase	,  position_ids=_lowerCAmelCase	,  head_mask=_lowerCAmelCase	,  inputs_embeds=_lowerCAmelCase	,  encoder_hidden_states=_lowerCAmelCase	,  encoder_attention_mask=_lowerCAmelCase	,  output_attentions=_lowerCAmelCase	,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,  return_dict=_lowerCAmelCase	,  )

										if self.has_pre_transformation:
															lowerCamelCase__       =       outputs["hidden_states"][-2]
															lowerCamelCase__       =       self.pre_LN(_lowerCAmelCase	)
															lowerCamelCase__       =       self.transformation_pre(_lowerCAmelCase	)

															return TransformationModelOutput(
															    projection_state=_lowerCAmelCase	,  last_hidden_state=outputs.last_hidden_state	,  hidden_states=outputs.hidden_states	,  attentions=outputs.attentions	,  )
										else:
															lowerCamelCase__       =       self.transformation(outputs.last_hidden_state	)
															return TransformationModelOutput(
															    projection_state=_lowerCAmelCase	,  last_hidden_state=outputs.last_hidden_state	,  hidden_states=outputs.hidden_states	,  attentions=outputs.attentions	,  )








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A_												=     {
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "albert"





					def __init__( self	,  _lowerCAmelCase=3_0000	,  _lowerCAmelCase=128	,  _lowerCAmelCase=4096	,  _lowerCAmelCase=12	,  _lowerCAmelCase=1	,  _lowerCAmelCase=64	,  _lowerCAmelCase=1_6384	,  _lowerCAmelCase=1	,  _lowerCAmelCase="gelu_new"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=0	,  _lowerCAmelCase=512	,  _lowerCAmelCase=2	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=1E-12	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase="absolute"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=2	,  _lowerCAmelCase=3	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)

										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       embedding_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_hidden_groups
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       inner_group_num
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       type_vocab_size
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       classifier_dropout_prob
										lowerCamelCase__       =       position_embedding_type

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


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

