
from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_		      =	logging.get_logger(__name__)

lowerCamelCase_		      =	{
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}




class       __A(							_a					):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__            =     """xlm"""
  SCREAMING_SNAKE_CASE__            =     {
      """hidden_size""": """emb_dim""",
      """num_attention_heads""": """n_heads""",
      """num_hidden_layers""": """n_layers""",
      """n_words""": """vocab_size""",  # For backward compatibility
  }







  def __init__(self			,     SCREAMING_SNAKE_CASE_=3_01_45			,     SCREAMING_SNAKE_CASE_=20_48			,     SCREAMING_SNAKE_CASE_=12			,     SCREAMING_SNAKE_CASE_=16			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=5_12			,     SCREAMING_SNAKE_CASE_=20_48**-0.5			,     SCREAMING_SNAKE_CASE_=1E-12			,     SCREAMING_SNAKE_CASE_=0.02			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=5			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_="first"			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=5			,     SCREAMING_SNAKE_CASE_=5			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=0			,     **SCREAMING_SNAKE_CASE_			,     ):
       UpperCamelCase__												=							vocab_size
       UpperCamelCase__												=							emb_dim
       UpperCamelCase__												=							n_layers
       UpperCamelCase__												=							n_heads
       UpperCamelCase__												=							dropout
       UpperCamelCase__												=							attention_dropout
       UpperCamelCase__												=							gelu_activation
       UpperCamelCase__												=							sinusoidal_embeddings
       UpperCamelCase__												=							causal
       UpperCamelCase__												=							asm
       UpperCamelCase__												=							n_langs
       UpperCamelCase__												=							use_lang_emb
       UpperCamelCase__												=							layer_norm_eps
       UpperCamelCase__												=							bos_index
       UpperCamelCase__												=							eos_index
       UpperCamelCase__												=							pad_index
       UpperCamelCase__												=							unk_index
       UpperCamelCase__												=							mask_index
       UpperCamelCase__												=							is_encoder
       UpperCamelCase__												=							max_position_embeddings
       UpperCamelCase__												=							embed_init_std
       UpperCamelCase__												=							init_std
       UpperCamelCase__												=							summary_type
       UpperCamelCase__												=							summary_use_proj
       UpperCamelCase__												=							summary_activation
       UpperCamelCase__												=							summary_proj_to_labels
       UpperCamelCase__												=							summary_first_dropout
       UpperCamelCase__												=							start_n_top
       UpperCamelCase__												=							end_n_top
       UpperCamelCase__												=							mask_token_id
       UpperCamelCase__												=							lang_id

       if "n_words" in kwargs:
            UpperCamelCase__												=							kwargs["""n_words"""]

       super().__init__(pad_token_id=__lowerCAmelCase			,     bos_token_id=__lowerCAmelCase			,     **__lowerCAmelCase		)






class       __A(							_a					):

  """simple docstring"""







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




