import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
    import torch

    from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer
@require_torch
class 				lowerCamelCase			(							unittest.TestCase					):






      @slow
      def 			lowerCAmelCase_   (      self						)     ->				Union[str, Any]:


          '''simple docstring'''







          snake_case:						List[Any]         =   XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
          model.to(__lowerCamelCase						)

          # encoder-decoder outputs
          snake_case:						Tuple         =   torch.tensor([[17, 9_62_08, 10_34_71, 2]]						).to(__lowerCamelCase						)
          snake_case:						Any         =   torch.tensor(
              [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]						).to(__lowerCamelCase						)
          snake_case:						Tuple         =   model(
              input_ids=__lowerCamelCase  ,		attention_mask=__lowerCamelCase  ,		encoder_outputs=__lowerCamelCase  ,		decoder_input_ids=__lowerCamelCase						)
          snake_case:						Optional[int]         =   output[0]
          snake_case:						List[str]         =   torch.Size((1, 14, 25_00_12)						)
          self.assertEqual(output_predited_logis.shape  ,		__lowerCamelCase						)
          snake_case:						Dict         =   torch.tensor(
              [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]						).to(__lowerCamelCase						)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,		__lowerCamelCase  ,		atol=1e-4						)						)

          # encoder outputs
          snake_case:						Tuple         =   model.prophetnet.encoder(__lowerCamelCase						)[0]
          snake_case:						Tuple         =   torch.tensor(
              [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]						).to(__lowerCamelCase						)
          snake_case:						List[Any]         =   torch.Size((1, 4, 10_24)						)
          self.assertEqual(encoder_outputs.shape  ,		__lowerCamelCase						)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]  ,		__lowerCamelCase  ,		atol=1e-4						)						)

          # decoder outputs
          snake_case:						Dict         =   model.prophetnet.decoder(
              __lowerCamelCase  ,		encoder_hidden_states=__lowerCamelCase  ,		)
          snake_case:						List[Any]         =   decoder_outputs[1].view(1  ,		model.config.ngram  ,		14  ,		-1						)
          snake_case:						Optional[int]         =   model.lm_head(__lowerCamelCase						)
          snake_case:						str         =   predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]  ,		__lowerCamelCase  ,		atol=1e-4						)						)






      @slow
      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						Optional[int]         =   XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(__lowerCamelCase						)

          snake_case:						str         =   torch.tensor([[17, 9_62_08, 10_34_71, 2]]						).to(__lowerCamelCase						)
          snake_case:						List[Any]         =   torch.tensor(
              [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]						).to(__lowerCamelCase						)
          snake_case:						Optional[Any]         =   model(
              input_ids=__lowerCamelCase  ,		attention_mask=__lowerCamelCase  ,		encoder_outputs=__lowerCamelCase  ,		decoder_input_ids=__lowerCamelCase						)
          snake_case:						Union[str, Any]         =   output[0]
          snake_case:						int         =   torch.Size((1, 14, 25_00_12)						)
          self.assertEqual(output_predited_logis.shape  ,		__lowerCamelCase						)
          # compare the actual values for a slice.
          snake_case:						Any         =   torch.tensor(
              [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]						).to(__lowerCamelCase						)

          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]  ,		__lowerCamelCase  ,		atol=1e-4						)						)






      @slow
      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						Tuple         =   XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(__lowerCamelCase						)
          snake_case:						Any         =   5_12

          snake_case:						Union[str, Any]         =   XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)

          snake_case:						str         =   (
              """Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"""
              """ January 14, 2020, according to the official portal of the organization. From that day, users of this"""
              """ system will not be able to receive security updates, which could make their computers vulnerable to"""
              """ cyber attacks."""
          )
          snake_case:						Tuple         =   (
              """орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"""
              """ после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"""
              """ этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"""
              """ к кибератакам."""
          )
          snake_case:						Any         =   (
              """根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"""
          )

          snake_case:						str         =   tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]  ,		padding=__lowerCamelCase  ,		max_length=2_55  ,		return_tensors="""pt"""						).input_ids
          snake_case:						Dict         =   input_ids.to(__lowerCamelCase						)

          snake_case:						Optional[int]         =   model.generate(
              __lowerCamelCase  ,		num_beams=10  ,		length_penalty=1.0  ,		no_repeat_ngram_size=3  ,		early_stopping=__lowerCamelCase						)
          snake_case:						Optional[Any]         =   [tokenizer.decode(__lowerCamelCase  ,		skip_special_tokens=__lowerCamelCase						) for g in summary_ids]
          snake_case:						List[Any]         =   """Microsoft to end Windows 7 free support after January 14, 2020"""
          snake_case:						Optional[Any]         =   """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          snake_case:						Any         =   """微软打算终止对Windows 7操作系统的免费支持"""
          self.assertListEqual(
              [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]  ,		__lowerCamelCase  ,		)

          snake_case:						List[Any]         =   model.generate(
              __lowerCamelCase  ,		num_beams=1  ,		length_penalty=1.0  ,		no_repeat_ngram_size=3  ,		early_stopping=__lowerCamelCase						)
          snake_case:						str         =   [
              tokenizer.convert_ids_to_tokens(__lowerCamelCase  ,		skip_special_tokens=__lowerCamelCase						) for g in summary_ids_beama
          ]
          snake_case:						Union[str, Any]         =   """▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """						)
          snake_case:						Optional[Any]         =   """▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
              """ """						)
          snake_case:						List[Any]         =   """微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持""".split(""" """						)
          self.assertListEqual(
              [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]  ,		__lowerCamelCase  ,		)



from __future__ import annotations

from statistics import mean

def   a_					(_lowerCAmelCase  :   list[int]       )->				list[int]:
    snake_case:						Tuple         =   2
    snake_case:						str         =   list(_lowerCAmelCase       )
    snake_case:						Union[str, Any]         =   [0] * len(_lowerCAmelCase       )
    snake_case:						List[Any]         =   0
    while True:
        snake_case:						Optional[int]         =   True
        for i, burst_time in enumerate(_lowerCAmelCase       ):
            if rem_burst_times[i] > 0:
                snake_case:						Dict         =   False
                if rem_burst_times[i] > quantum:
                    t += quantum
                    rem_burst_times[i] -= quantum
                else:
                    t += rem_burst_times[i]
                    snake_case:						Optional[Any]         =   t - burst_time
                    snake_case:						Tuple         =   0
        if done is True:
            return waiting_times

def   a_					(_lowerCAmelCase  :   list[int]			,	_lowerCAmelCase  :   list[int]       )->				list[int]:
    return [burst + waiting for burst, waiting in zip(_lowerCAmelCase			,	_lowerCAmelCase       )]


if __name__ == "__main__":
    __lowerCAmelCase    :  List[Any]										= [3, 5, 7]
    __lowerCAmelCase    :  List[Any]										= calculate_waiting_times(burst_times)
    __lowerCAmelCase    :  str										= calculate_turn_around_times(burst_times, waiting_times)
    print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
    for i, burst_time in enumerate(burst_times):
        print(
            F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
            F"""{turn_around_times[i]}"""
        )
    print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
    print(F"""Average turn around time = {mean(turn_around_times):.5f}""")


