
"""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      :	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 		a__						(				a_						):
			__lowerCAmelCase						=    """flaubert"""
			__lowerCAmelCase						=    {
			    """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=False     ,   _a=0.0     ,   _a=30_145     ,   _a=2_048     ,   _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=2_048**-0.5     ,   _a=1E-12     ,   _a=0.0_2     ,   _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     ,   ):
										lowercase   :    Union[str, Any]			=							pre_norm
										lowercase   :    int			=							layerdrop
										lowercase   :    List[Any]			=							vocab_size
										lowercase   :    int			=							emb_dim
										lowercase   :    Union[str, Any]			=							n_layers
										lowercase   :    List[str]			=							n_heads
										lowercase   :    int			=							dropout
										lowercase   :    Optional[int]			=							attention_dropout
										lowercase   :    Dict			=							gelu_activation
										lowercase   :    List[str]			=							sinusoidal_embeddings
										lowercase   :    List[str]			=							causal
										lowercase   :    Optional[Any]			=							asm
										lowercase   :    Tuple			=							n_langs
										lowercase   :    Optional[Any]			=							use_lang_emb
										lowercase   :    List[str]			=							layer_norm_eps
										lowercase   :    str			=							bos_index
										lowercase   :    Dict			=							eos_index
										lowercase   :    Union[str, Any]			=							pad_index
										lowercase   :    int			=							unk_index
										lowercase   :    str			=							mask_index
										lowercase   :    Union[str, Any]			=							is_encoder
										lowercase   :    List[str]			=							max_position_embeddings
										lowercase   :    Union[str, Any]			=							embed_init_std
										lowercase   :    Any			=							init_std
										lowercase   :    Tuple			=							summary_type
										lowercase   :    Tuple			=							summary_use_proj
										lowercase   :    int			=							summary_activation
										lowercase   :    Optional[Any]			=							summary_proj_to_labels
										lowercase   :    Optional[Any]			=							summary_first_dropout
										lowercase   :    List[Any]			=							start_n_top
										lowercase   :    Tuple			=							end_n_top
										lowercase   :    List[str]			=							mask_token_id
										lowercase   :    Union[str, Any]			=							lang_id

										if "n_words" in kwargs:
																	lowercase   :    Any			=							kwargs["n_words"]

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


class 		a__						(				a_						):
			@property
			def       __magic_name__      (      self    ):
										if self.task == "multiple-choice":
																	lowercase   :    Dict			=							{0: "batch", 1: "choice", 2: "sequence"}
										else:
																	lowercase   :    Any			=							{0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										    ]    )




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_A      :	List[str]       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   def __init__(      self     ,   *_a     ,   **_a    ):
          warnings.warn(
              "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use ConvNextImageProcessor instead."     ,   _a     ,   )
          super().__init__(*_a     ,   **_a    )


