





from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[int]			=			"xlm-roberta"






     def __init__(							self						:    Any						,lowerCamelCase__						:    Optional[int]=30522						,lowerCamelCase__						:    Optional[Any]=768						,lowerCamelCase__						:    Optional[int]=12						,lowerCamelCase__						:    Any=12						,lowerCamelCase__						:    Optional[Any]=3072						,lowerCamelCase__						:    Optional[Any]="gelu"						,lowerCamelCase__						:    List[str]=0.1						,lowerCamelCase__						:    List[str]=0.1						,lowerCamelCase__						:    List[str]=512						,lowerCamelCase__						:    Any=2						,lowerCamelCase__						:    str=0.02						,lowerCamelCase__						:    List[Any]=1e-1_2						,lowerCamelCase__						:    int=1						,lowerCamelCase__						:    str=0						,lowerCamelCase__						:    List[str]=2						,lowerCamelCase__						:    int="absolute"						,lowerCamelCase__						:    Optional[int]=True						,lowerCamelCase__						:    List[str]=None						,**lowerCamelCase__						:    Union[str, Any]						,)     ->		List[Any]:




           '''simple docstring'''


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

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

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

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




           '''simple docstring'''


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						   =       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       ["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						   =       [
          """LlamaForCausalLM""",
          """LlamaModel""",
          """LlamaPreTrainedModel""",
          """LlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_llama_fast import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
      import sys

      SCREAMING_SNAKE_CASE_						   =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


