




from collections import OrderedDict
from typing import Mapping

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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "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 						lowercase							(   UpperCamelCase__		):
     _a							 =						"xlm"
     _a							 =						{
         "hidden_size": "emb_dim",
         "num_attention_heads": "n_heads",
         "num_hidden_layers": "n_layers",
         "n_words": "vocab_size",  # For backward compatibility
     }

     def __init__(  self		,     _a=3_0145		,     _a=2048		,     _a=12		,     _a=16		,     _a=0.1		,     _a=0.1		,     _a=True		,     _a=False		,     _a=False		,     _a=False		,     _a=1		,     _a=True		,     _a=512		,     _a=2048**-0.5		,     _a=1e-12		,     _a=0.02		,     _a=0		,     _a=1		,     _a=2		,     _a=3		,     _a=5		,     _a=True		,     _a="first"		,     _a=True		,     _a=None		,     _a=True		,     _a=0.1		,     _a=5		,     _a=5		,     _a=0		,     _a=0		,     _a=2		,     _a=0		,     **_a		,     )						->							Optional[int]:
           _A							:     Optional[int]			=						vocab_size
           _A							:     Optional[Any]			=						emb_dim
           _A							:     Optional[int]			=						n_layers
           _A							:     Optional[int]			=						n_heads
           _A							:     List[str]			=						dropout
           _A							:     Optional[int]			=						attention_dropout
           _A							:     Optional[Any]			=						gelu_activation
           _A							:     Union[str, Any]			=						sinusoidal_embeddings
           _A							:     Union[str, Any]			=						causal
           _A							:     List[str]			=						asm
           _A							:     int			=						n_langs
           _A							:     List[Any]			=						use_lang_emb
           _A							:     Any			=						layer_norm_eps
           _A							:     str			=						bos_index
           _A							:     Union[str, Any]			=						eos_index
           _A							:     Optional[Any]			=						pad_index
           _A							:     Optional[int]			=						unk_index
           _A							:     str			=						mask_index
           _A							:     Tuple			=						is_encoder
           _A							:     Dict			=						max_position_embeddings
           _A							:     Tuple			=						embed_init_std
           _A							:     Optional[Any]			=						init_std
           _A							:     Tuple			=						summary_type
           _A							:     Optional[int]			=						summary_use_proj
           _A							:     Optional[Any]			=						summary_activation
           _A							:     Dict			=						summary_proj_to_labels
           _A							:     Union[str, Any]			=						summary_first_dropout
           _A							:     Tuple			=						start_n_top
           _A							:     int			=						end_n_top
           _A							:     Optional[Any]			=						mask_token_id
           _A							:     Union[str, Any]			=						lang_id

           if "n_words" in kwargs:
                 _A							:     List[str]			=						kwargs["""n_words"""]

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







class 						lowercase							(   UpperCamelCase__		):

     @property
     def 					a__	(  self				)						->							Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
                 _A							:     Any			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                 _A							:     str			=						{0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]				)









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowercase  							=					logging.getLogger(__name__)




@dataclass
class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= field(
	    default=0.0       ,					metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''whether to use adafactor'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default='''linear'''       ,					metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}       ,					)





