





"""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[Any]							=				{
    """flaubert/flaubert_small_cased""": """https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json""",
    """flaubert/flaubert_base_uncased""": """https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json""",
    """flaubert/flaubert_base_cased""": """https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json""",
    """flaubert/flaubert_large_cased""": """https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							int											=     """flaubert"""
       snake_case__				:							Tuple											=     {
           """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=False   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=3_0145   ,					__SCREAMING_SNAKE_CASE=2048   ,					__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=512   ,					__SCREAMING_SNAKE_CASE=2048**-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   ,					):






        '''simple docstring'''

        snake_case__    							=				pre_norm
        snake_case__    							=				layerdrop
        snake_case__    							=				vocab_size
        snake_case__    							=				emb_dim
        snake_case__    							=				n_layers
        snake_case__    							=				n_heads
        snake_case__    							=				dropout
        snake_case__    							=				attention_dropout
        snake_case__    							=				gelu_activation
        snake_case__    							=				sinusoidal_embeddings
        snake_case__    							=				causal
        snake_case__    							=				asm
        snake_case__    							=				n_langs
        snake_case__    							=				use_lang_emb
        snake_case__    							=				layer_norm_eps
        snake_case__    							=				bos_index
        snake_case__    							=				eos_index
        snake_case__    							=				pad_index
        snake_case__    							=				unk_index
        snake_case__    							=				mask_index
        snake_case__    							=				is_encoder
        snake_case__    							=				max_position_embeddings
        snake_case__    							=				embed_init_std
        snake_case__    							=				init_std
        snake_case__    							=				summary_type
        snake_case__    							=				summary_use_proj
        snake_case__    							=				summary_activation
        snake_case__    							=				summary_proj_to_labels
        snake_case__    							=				summary_first_dropout
        snake_case__    							=				start_n_top
        snake_case__    							=				end_n_top
        snake_case__    							=				mask_token_id
        snake_case__    							=				lang_id

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

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



class      lowercase_				(     __lowerCAmelCase							):

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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),
            ]     )








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

