

'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__						:       Optional[int]											=						logging.get_logger(__name__)

lowerCAmelCase__						:       Optional[Any]											=						{
    """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       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """xlm"""
						__UpperCAmelCase			     =    {
						    """hidden_size""": """emb_dim""",
						    """num_attention_heads""": """n_heads""",
						    """num_hidden_layers""": """n_layers""",
						    """n_words""": """vocab_size""",  # For backward compatibility
						}







						def __init__(							self					:      Dict			,			snake_case_					:      Optional[int]=3_0_1_4_5			,			snake_case_					:      int=2_0_4_8			,			snake_case_					:      Any=1_2			,			snake_case_					:      Optional[int]=1_6			,			snake_case_					:      Tuple=0.1			,			snake_case_					:      Optional[Any]=0.1			,			snake_case_					:      str=True			,			snake_case_					:      str=False			,			snake_case_					:      List[Any]=False			,			snake_case_					:      List[str]=False			,			snake_case_					:      List[Any]=1			,			snake_case_					:      Union[str, Any]=True			,			snake_case_					:      Tuple=5_1_2			,			snake_case_					:      Dict=2_0_4_8**-0.5			,			snake_case_					:      Any=1e-12			,			snake_case_					:      str=0.0_2			,			snake_case_					:      str=0			,			snake_case_					:      Dict=1			,			snake_case_					:      int=2			,			snake_case_					:      Dict=3			,			snake_case_					:      Optional[Any]=5			,			snake_case_					:      Optional[Any]=True			,			snake_case_					:      Any="first"			,			snake_case_					:      str=True			,			snake_case_					:      List[str]=None			,			snake_case_					:      Optional[Any]=True			,			snake_case_					:      Optional[Any]=0.1			,			snake_case_					:      str=5			,			snake_case_					:      Any=5			,			snake_case_					:      int=0			,			snake_case_					:      Optional[int]=0			,			snake_case_					:      int=2			,			snake_case_					:      List[Any]=0			,			**snake_case_					:      int			,			):



								'''simple docstring'''



								snake_case__						:       Dict       						=  vocab_size
								snake_case__						:       Union[str, Any]       						=  emb_dim
								snake_case__						:       str       						=  n_layers
								snake_case__						:       Optional[int]       						=  n_heads
								snake_case__						:       int       						=  dropout
								snake_case__						:       Union[str, Any]       						=  attention_dropout
								snake_case__						:       Any       						=  gelu_activation
								snake_case__						:       Optional[int]       						=  sinusoidal_embeddings
								snake_case__						:       List[Any]       						=  causal
								snake_case__						:       Optional[int]       						=  asm
								snake_case__						:       Optional[Any]       						=  n_langs
								snake_case__						:       Tuple       						=  use_lang_emb
								snake_case__						:       str       						=  layer_norm_eps
								snake_case__						:       Any       						=  bos_index
								snake_case__						:       Tuple       						=  eos_index
								snake_case__						:       Dict       						=  pad_index
								snake_case__						:       List[str]       						=  unk_index
								snake_case__						:       Optional[int]       						=  mask_index
								snake_case__						:       List[Any]       						=  is_encoder
								snake_case__						:       int       						=  max_position_embeddings
								snake_case__						:       str       						=  embed_init_std
								snake_case__						:       Tuple       						=  init_std
								snake_case__						:       Union[str, Any]       						=  summary_type
								snake_case__						:       List[Any]       						=  summary_use_proj
								snake_case__						:       int       						=  summary_activation
								snake_case__						:       str       						=  summary_proj_to_labels
								snake_case__						:       List[str]       						=  summary_first_dropout
								snake_case__						:       Dict       						=  start_n_top
								snake_case__						:       Optional[Any]       						=  end_n_top
								snake_case__						:       Optional[Any]       						=  mask_token_id
								snake_case__						:       Optional[int]       						=  lang_id

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

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





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



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






'''simple docstring'''


from bisect import bisect
from itertools import accumulate




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  sorted(zip(__lowerCAmelCase   ,							__lowerCAmelCase     )   ,							key=lambda __lowerCAmelCase     : x[0] / x[1]   ,							reverse=__lowerCAmelCase     )
		snake_case__						,       snake_case__						:       Union[str, Any]       						=  [i[0] for i in r], [i[1] for i in r]
		snake_case__						:       Dict       						=  list(accumulate(__lowerCAmelCase     )     )
		snake_case__						:       Optional[int]       						=  bisect(__lowerCAmelCase   ,							__lowerCAmelCase     )
		return (
		    0
		    if k == 0
		    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
		    if k != n
		    else sum(vl[:k]     )
		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



