


'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


a_   :      Dict				    =      logging.get_logger(__name__)

a_   :      Dict				    =      {
    "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       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""xlm"""
 _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 ,			__magic_name__=3_01_45 ,			__magic_name__=20_48 ,			__magic_name__=12 ,			__magic_name__=16 ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=True ,			__magic_name__=False ,			__magic_name__=False ,			__magic_name__=False ,			__magic_name__=1 ,			__magic_name__=True ,			__magic_name__=5_12 ,			__magic_name__=20_48**-0.5 ,			__magic_name__=1e-12 ,			__magic_name__=0.0_2 ,			__magic_name__=0 ,			__magic_name__=1 ,			__magic_name__=2 ,			__magic_name__=3 ,			__magic_name__=5 ,			__magic_name__=True ,			__magic_name__="first" ,			__magic_name__=True ,			__magic_name__=None ,			__magic_name__=True ,			__magic_name__=0.1 ,			__magic_name__=5 ,			__magic_name__=5 ,			__magic_name__=0 ,			__magic_name__=0 ,			__magic_name__=2 ,			__magic_name__=0 ,			**__magic_name__ ,			)   ->							str:
      _a      =							vocab_size
      _a      =							emb_dim
      _a      =							n_layers
      _a      =							n_heads
      _a      =							dropout
      _a      =							attention_dropout
      _a      =							gelu_activation
      _a      =							sinusoidal_embeddings
      _a      =							causal
      _a      =							asm
      _a      =							n_langs
      _a      =							use_lang_emb
      _a      =							layer_norm_eps
      _a      =							bos_index
      _a      =							eos_index
      _a      =							pad_index
      _a      =							unk_index
      _a      =							mask_index
      _a      =							is_encoder
      _a      =							max_position_embeddings
      _a      =							embed_init_std
      _a      =							init_std
      _a      =							summary_type
      _a      =							summary_use_proj
      _a      =							summary_activation
      _a      =							summary_proj_to_labels
      _a      =							summary_first_dropout
      _a      =							start_n_top
      _a      =							end_n_top
      _a      =							mask_token_id
      _a      =							lang_id

      if "n_words" in kwargs:
           _a      =							kwargs['n_words']

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





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





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

