


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_a   :   Optional[Any]												=   logging.get_logger(__name__)

_a   :   Tuple												=   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Union[str, Any]          =	"camembert"
   def __init__(						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=3_0522       ,   SCREAMING_SNAKE_CASE_       :      str=768       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=12       ,   SCREAMING_SNAKE_CASE_       :      Dict=12       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=3072       ,   SCREAMING_SNAKE_CASE_       :      Tuple="gelu"       ,   SCREAMING_SNAKE_CASE_       :      List[str]=0.1       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=0.1       ,   SCREAMING_SNAKE_CASE_       :      List[str]=512       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=2       ,   SCREAMING_SNAKE_CASE_       :      Any=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      Tuple=1e-12       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=1       ,   SCREAMING_SNAKE_CASE_       :      Dict=0       ,   SCREAMING_SNAKE_CASE_       :      int=2       ,   SCREAMING_SNAKE_CASE_       :      Dict="absolute"       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=True       ,   SCREAMING_SNAKE_CASE_       :      Tuple=None       ,   **SCREAMING_SNAKE_CASE_       :      Dict       ,   ) ->							int:
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,   bos_token_id=SCREAMING_SNAKE_CASE_       ,   eos_token_id=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )

        __snake_case           =						vocab_size
        __snake_case           =						hidden_size
        __snake_case           =						num_hidden_layers
        __snake_case           =						num_attention_heads
        __snake_case           =						hidden_act
        __snake_case           =						intermediate_size
        __snake_case           =						hidden_dropout_prob
        __snake_case           =						attention_probs_dropout_prob
        __snake_case           =						max_position_embeddings
        __snake_case           =						type_vocab_size
        __snake_case           =						initializer_range
        __snake_case           =						layer_norm_eps
        __snake_case           =						position_embedding_type
        __snake_case           =						use_cache
        __snake_case           =						classifier_dropout




class 			_lowercase (	__lowercase							):
   @property
   def a  (						self       :      List[str]      ) ->							Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             __snake_case           =						{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             __snake_case           =						{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]      )











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       (  _a							):
							snake_case  :   Optional[torch.FloatTensor]      =							None
							snake_case  :   torch.FloatTensor      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None






class 							__SCREAMING_SNAKE_CASE       (  _a							):






							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	)
														UpperCamelCase__					  =      project_dim
														UpperCamelCase__					  =      pooler_fn
														UpperCamelCase__					  =      learn_encoder
														UpperCamelCase__					  =      use_attention_mask






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   int      =							[r"""pooler""", r"""logit_scale"""]
							snake_case  :   Tuple      =							[r"""position_ids""", r"""predictions.decoder.bias"""]
							snake_case  :   str      =							"""roberta"""
							snake_case  :   Dict      =							RobertaSeriesConfig






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







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict

														UpperCamelCase__					  =      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:
																					UpperCamelCase__					  =      outputs["""hidden_states"""][-2]
																					UpperCamelCase__					  =      self.pre_LN(__lowerCAmelCase	)
																					UpperCamelCase__					  =      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:
																					UpperCamelCase__					  =      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	,			)




