from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase      :			Dict							       =		logging.get_logger(__name__)

__lowerCamelCase      :			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 __snake_case    (						lowerCamelCase_		):
   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      :    Union[str, Any]	,     _lowercase      :    Optional[Any]=3_01_45	,     _lowercase      :    List[Any]=20_48	,     _lowercase      :    Tuple=12	,     _lowercase      :    Union[str, Any]=16	,     _lowercase      :    List[Any]=0.1	,     _lowercase      :    List[str]=0.1	,     _lowercase      :    int=True	,     _lowercase      :    Optional[Any]=False	,     _lowercase      :    List[Any]=False	,     _lowercase      :    str=False	,     _lowercase      :    Dict=1	,     _lowercase      :    Optional[int]=True	,     _lowercase      :    Dict=5_12	,     _lowercase      :    Optional[Any]=20_48**-0.5	,     _lowercase      :    str=1E-12	,     _lowercase      :    str=0.02	,     _lowercase      :    Union[str, Any]=0	,     _lowercase      :    Dict=1	,     _lowercase      :    List[Any]=2	,     _lowercase      :    Any=3	,     _lowercase      :    List[Any]=5	,     _lowercase      :    Optional[Any]=True	,     _lowercase      :    List[Any]="first"	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    int=None	,     _lowercase      :    int=True	,     _lowercase      :    Tuple=0.1	,     _lowercase      :    List[Any]=5	,     _lowercase      :    Any=5	,     _lowercase      :    str=0	,     _lowercase      :    List[Any]=0	,     _lowercase      :    str=2	,     _lowercase      :    List[str]=0	,     **_lowercase      :    Tuple	,     ):



     """simple docstring"""






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

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

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







class __snake_case    (						lowerCamelCase_		):




   @property
   def      __a						(   self      :    str				):



     """simple docstring"""






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

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
