


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_			(_lowerCamelCase						):




       '''simple docstring'''



       _a   							=							None
       _a   							=							None
       _a   							=							None
       _a   							=							None







class   SCREAMING_SNAKE_CASE_			(_lowerCamelCase						):




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int=1		,				__a				:				Union[str, Any]=0		,				__a				:				Optional[Any]=2		,				__a				:				Any=512		,				__a				:				Optional[Any]="cls"		,				__a				:				int=False		,				__a				:				Dict=True		,				**__a				:				int		,				)					->Dict:
          super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE							)
          lowerCamelCase_						:   Optional[int]		= project_dim
          lowerCamelCase_						:   Dict		= pooler_fn
          lowerCamelCase_						:   Optional[int]		= learn_encoder
          lowerCamelCase_						:   Tuple		= use_attention_mask







class   SCREAMING_SNAKE_CASE_			(_lowerCamelCase						):




       '''simple docstring'''



       _a   							=							[r'''pooler''', r'''logit_scale''']
       _a   							=							[r'''position_ids''', r'''predictions.decoder.bias''']
       _a   							=							'''roberta'''
       _a   							=							RobertaSeriesConfig



       def __init__(						self				:				str		,				__a				:				Dict							)					->Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE							)
          lowerCamelCase_						:   int		= XLMRobertaModel(_SCREAMING_SNAKE_CASE							)
          lowerCamelCase_						:   Optional[int]		= nn.Linear(config.hidden_size		,				config.project_dim							)
          lowerCamelCase_						:   List[Any]		= getattr(_SCREAMING_SNAKE_CASE		,				"""has_pre_transformation"""		,				_SCREAMING_SNAKE_CASE							)
          if self.has_pre_transformation:
             lowerCamelCase_						:   Union[str, Any]		= nn.Linear(config.hidden_size		,				config.project_dim							)
             lowerCamelCase_						:   Optional[Any]		= nn.LayerNorm(config.hidden_size		,				eps=config.layer_norm_eps							)
          self.post_init()




       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[torch.Tensor] = None		,				__a				:				Optional[bool] = None		,				__a				:				Optional[bool] = None		,				__a				:				Optional[bool] = None		,				)					->Optional[int]:
          lowerCamelCase_						:   Union[str, Any]		= return_dict if return_dict is not None else self.config.use_return_dict

          lowerCamelCase_						:   Tuple		= self.base_model(
              input_ids=_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		,				position_ids=_SCREAMING_SNAKE_CASE		,				head_mask=_SCREAMING_SNAKE_CASE		,				inputs_embeds=_SCREAMING_SNAKE_CASE		,				encoder_hidden_states=_SCREAMING_SNAKE_CASE		,				encoder_attention_mask=_SCREAMING_SNAKE_CASE		,				output_attentions=_SCREAMING_SNAKE_CASE		,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states		,				return_dict=_SCREAMING_SNAKE_CASE		,				)

          if self.has_pre_transformation:
             lowerCamelCase_						:   List[str]		= outputs['hidden_states'][-2]
             lowerCamelCase_						:   str		= self.pre_LN(_SCREAMING_SNAKE_CASE							)
             lowerCamelCase_						:   List[str]		= self.transformation_pre(_SCREAMING_SNAKE_CASE							)

             return TransformationModelOutput(
                 projection_state=_SCREAMING_SNAKE_CASE		,				last_hidden_state=outputs.last_hidden_state		,				hidden_states=outputs.hidden_states		,				attentions=outputs.attentions		,				)
          else:
             lowerCamelCase_						:   Optional[Any]		= self.transformation(outputs.last_hidden_state							)
             return TransformationModelOutput(
                 projection_state=_SCREAMING_SNAKE_CASE		,				last_hidden_state=outputs.last_hidden_state		,				hidden_states=outputs.hidden_states		,				attentions=outputs.attentions		,				)










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


A_						:    Any				=      logging.get_logger(__name__)

A_						:    Optional[int]				=      {
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= '''xmod'''





	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple=30_522     ,       _SCREAMING_SNAKE_CASE				:       Tuple=768     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=12     ,       _SCREAMING_SNAKE_CASE				:       List[str]=12     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=3_072     ,       _SCREAMING_SNAKE_CASE				:       Any="gelu"     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Dict=0.1     ,       _SCREAMING_SNAKE_CASE				:       str=512     ,       _SCREAMING_SNAKE_CASE				:       Tuple=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=1E-1_2     ,       _SCREAMING_SNAKE_CASE				:       List[str]=1     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="absolute"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       Dict=2     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       int=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Tuple=("en_XX",)     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]     ,       )						->	List[str]:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE						:							Any							=     vocab_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							str							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_act
					SCREAMING_SNAKE_CASE						:							Any							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							str							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							str							=     type_vocab_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     initializer_range
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Tuple							=     position_embedding_type
					SCREAMING_SNAKE_CASE						:							int							=     use_cache
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     classifier_dropout
					SCREAMING_SNAKE_CASE						:							List[Any]							=     pre_norm
					SCREAMING_SNAKE_CASE						:							int							=     adapter_reduction_factor
					SCREAMING_SNAKE_CASE						:							List[Any]							=     adapter_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     adapter_reuse_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     ln_before_adapter
					SCREAMING_SNAKE_CASE						:							Tuple							=     list(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     default_language




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@property
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Mapping[str, Mapping[int, str]]:





					"""simple docstring"""


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





