from __future__ import annotations

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

from matplotlib import pyplot as plt
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if not arr:
												return None, None, 0
						if low == high:
												return low, high, arr[low]

						SCREAMING_SNAKE_CASE							:    Tuple							=							(low + high) // 2
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							max_subarray(_lowercase		,  _lowercase		,  _lowercase						)
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							max_subarray(_lowercase		,  mid + 1		,  _lowercase						)
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							max_cross_sum(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase						)
						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       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							float('''-inf'''						), -1
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							float('''-inf'''						), -1

						SCREAMING_SNAKE_CASE							:    int | float							=							0
						for i in range(_lowercase		,  low - 1		,  -1						):
												summ += arr[i]
												if summ > left_sum:
																		SCREAMING_SNAKE_CASE							:    str							=							summ
																		SCREAMING_SNAKE_CASE							:    Dict							=							i

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

						return max_left, max_right, (left_sum + right_sum)
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[randint(1		,  _lowercase						) for _ in range(_lowercase						)]
						SCREAMING_SNAKE_CASE							:    str							=							time.time()
						max_subarray(_lowercase		,  0		,  input_size - 1						)
						SCREAMING_SNAKE_CASE							:    Dict							=							time.time()
						return end - start
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
						SCREAMING_SNAKE_CASE							:    List[str]							=							[time_max_subarray(_lowercase						) for input_size in input_sizes]
						print('''No of Inputs\t\tTime Taken'''						)
						for input_size, runtime in zip(_lowercase		,  _lowercase						):
												print(_lowercase		,  '''\t\t'''		,  _lowercase						)
						plt.plot(_lowercase		,  _lowercase						)
						plt.xlabel('''Number of Inputs'''						)
						plt.ylabel('''Time taken in seconds'''						)
						plt.show()


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

					testmod()






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





