


'''simple docstring'''


from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if not arr:
				return None, None, 0
		if low == high:
				return low, high, arr[low]

		_UpperCAmelCase						:				Optional[Any]	    =					(low + high) // 2
		_UpperCAmelCase						:				Dict	    =					max_subarray(_lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[Any]	    =					max_subarray(_lowerCAmelCase					,       mid + 1					,       _lowerCAmelCase      )
		_UpperCAmelCase						:				List[str]	    =					max_cross_sum(_lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase      )
		if left_sum >= right_sum and left_sum >= cross_sum:
				return left_low, left_high, left_sum
		elif right_sum >= left_sum and right_sum >= cross_sum:
				return right_low, right_high, right_sum
		return cross_left, cross_right, cross_sum






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				str	    =					float("-inf"      ), -1
		_UpperCAmelCase						:				Any	    =					float("-inf"      ), -1

		_UpperCAmelCase						:				int | float	    =					0
		for i in range(_lowerCAmelCase					,       low - 1					,       -1      ):
				summ += arr[i]
				if summ > left_sum:
						_UpperCAmelCase						:				List[str]	    =					summ
						_UpperCAmelCase						:				Optional[int]	    =					i

		_UpperCAmelCase						:				List[str]	    =					0
		for i in range(mid + 1					,       high + 1      ):
				summ += arr[i]
				if summ > right_sum:
						_UpperCAmelCase						:				Dict	    =					summ
						_UpperCAmelCase						:				List[str]	    =					i

		return max_left, max_right, (left_sum + right_sum)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Tuple	    =					[randint(1					,       _lowerCAmelCase      ) for _ in range(_lowerCAmelCase      )]
		_UpperCAmelCase						:				List[Any]	    =					time.time()
		max_subarray(_lowerCAmelCase					,       0					,       input_size - 1      )
		_UpperCAmelCase						:				Tuple	    =					time.time()
		return end - start






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
		_UpperCAmelCase						:				str	    =					[time_max_subarray(_lowerCAmelCase      ) for input_size in input_sizes]
		print("No of Inputs\t\tTime Taken"      )
		for input_size, runtime in zip(_lowerCAmelCase					,       _lowerCAmelCase      ):
				print(_lowerCAmelCase					,       "\t\t"					,       _lowerCAmelCase      )
		plt.plot(_lowerCAmelCase					,       _lowerCAmelCase      )
		plt.xlabel("Number of Inputs"      )
		plt.ylabel("Time taken in seconds"      )
		plt.show()


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

						testmod()








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase											=						logging.get_logger(__name__)

_UpperCamelCase											=						{
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""xlm-roberta"""




  def __init__( self      :	Any			,	_a      :	Optional[Any]=3_0522			,	_a      :	Optional[Any]=768			,	_a      :	int=12			,	_a      :	Tuple=12			,	_a      :	str=3072			,	_a      :	List[Any]="gelu"			,	_a      :	int=0.1			,	_a      :	Optional[int]=0.1			,	_a      :	Optional[int]=512			,	_a      :	List[Any]=2			,	_a      :	Optional[Any]=0.02			,	_a      :	str=1e-12			,	_a      :	str=1			,	_a      :	str=0			,	_a      :	Optional[Any]=2			,	_a      :	Optional[int]="absolute"			,	_a      :	int=True			,	_a      :	Tuple=None			,	**_a      :	Any			,	)					->					Dict:
    super().__init__(pad_token_id=_a			,	bos_token_id=_a			,	eos_token_id=_a			,	**_a   )

    __lowerCamelCase       :       int           = vocab_size
    __lowerCamelCase       :       Union[str, Any]           = hidden_size
    __lowerCamelCase       :       int           = num_hidden_layers
    __lowerCamelCase       :       Optional[int]           = num_attention_heads
    __lowerCamelCase       :       List[str]           = hidden_act
    __lowerCamelCase       :       Any           = intermediate_size
    __lowerCamelCase       :       int           = hidden_dropout_prob
    __lowerCamelCase       :       List[Any]           = attention_probs_dropout_prob
    __lowerCamelCase       :       Optional[int]           = max_position_embeddings
    __lowerCamelCase       :       Tuple           = type_vocab_size
    __lowerCamelCase       :       Optional[int]           = initializer_range
    __lowerCamelCase       :       Optional[int]           = layer_norm_eps
    __lowerCamelCase       :       str           = position_embedding_type
    __lowerCamelCase       :       List[Any]           = use_cache
    __lowerCamelCase       :       int           = classifier_dropout






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  @property
  def 						_lowercase     ( self      :	Optional[Any]   )					->					Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
      __lowerCamelCase       :       int           = {0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      __lowerCamelCase       :       Any           = {0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]   )






