





'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]









'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


__magic_name__   :						int    =			logging.get_logger(__name__)

__magic_name__   :						Tuple    =			{
    """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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   '''xlm'''
     UpperCAmelCase__       :						Dict      =   {
         '''hidden_size''': '''emb_dim''',
         '''num_attention_heads''': '''n_heads''',
         '''num_hidden_layers''': '''n_layers''',
         '''n_words''': '''vocab_size''',  # For backward compatibility
     }

     def __init__(					self						,   lowerCamelCase=30_145						,   lowerCamelCase=2_048						,   lowerCamelCase=12						,   lowerCamelCase=16						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=True						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=1						,   lowerCamelCase=True						,   lowerCamelCase=512						,   lowerCamelCase=2_048**-0.5						,   lowerCamelCase=1e-12						,   lowerCamelCase=0.02						,   lowerCamelCase=0						,   lowerCamelCase=1						,   lowerCamelCase=2						,   lowerCamelCase=3						,   lowerCamelCase=5						,   lowerCamelCase=True						,   lowerCamelCase="first"						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=0.1						,   lowerCamelCase=5						,   lowerCamelCase=5						,   lowerCamelCase=0						,   lowerCamelCase=0						,   lowerCamelCase=2						,   lowerCamelCase=0						,   **lowerCamelCase						,   ):
       _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=lowerCamelCase						,   bos_token_id=lowerCamelCase						,   **lowerCamelCase	)







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     @property
     def 	UpperCamelCase(					self	):
       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),
               ("token_type_ids", dynamic_axis),
           ]	)


