


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case							: str      =   logging.getLogger()






def 							__lowercase     (				__lowerCAmelCase							:       Path      ,					__lowerCAmelCase							:       list     ):
							a__    =  '\n'.join(__lowerCAmelCase     )
							Path(__lowerCAmelCase     ).open('w'     ).writelines(__lowerCAmelCase     )


snake_case							: Dict      =   '''patrickvonplaten/t5-tiny-random'''
snake_case							: Union[str, Any]      =   '''sshleifer/bart-tiny-random'''
snake_case							: Tuple      =   '''sshleifer/tiny-mbart'''

snake_case							: str      =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class  snake_case_     (lowerCamelCase_   ):
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :List[Any]   )      ->					Optional[int]:
												a__    =  Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
												a__    =  input_file_name.parent / 'utest_output.txt'
												assert not output_file_name.exists()
												a__    =  [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
												_dump_articles(__snake_case    ,__snake_case   )

												a__    =  str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
												a__    =  'translation_en_to_de' if model == T5_TINY else 'summarization'
												a__    =  F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

												with patch.object(__snake_case    ,'argv'    ,__snake_case   ):
																			run_generate()
																			assert Path(__snake_case   ).exists()
																			# os.remove(Path(output_file_name))
					def 				lowerCamelCase__(   self     :Any   )      ->					int:
												self.run_eval_tester(__snake_case   )
					@parameterized.expand([BART_TINY, MBART_TINY]   )
					@slow
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :str   )      ->					Optional[int]:
												self.run_eval_tester(__snake_case   )



					@parameterized.expand([T5_TINY, MBART_TINY]   )
					@slow
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Union[str, Any]   )      ->					Any:
												a__    =  Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
												a__    =  input_file_name.parent / 'utest_output.txt'
												assert not output_file_name.exists()

												a__    =  {
												    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
												    'de': [
												        'Maschinelles Lernen ist großartig, oder?',
												        'Ich esse gerne Bananen',
												        'Morgen ist wieder ein toller Tag!',
												    ],
												}

												a__    =  Path(self.get_auto_remove_tmp_dir()   )
												a__    =  str(tmp_dir / 'scores.json'   )
												a__    =  str(tmp_dir / 'val.target'   )
												_dump_articles(__snake_case    ,text['en']   )
												_dump_articles(__snake_case    ,text['de']   )
												a__    =  'translation_en_to_de' if model == T5_TINY else 'summarization'
												a__    =  F'\n            run_eval_search.py\n            {model}\n            {str(__snake_case   )}\n            {str(__snake_case   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
												testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

												with patch.object(__snake_case    ,'argv'    ,__snake_case   ):
																			with CaptureStdout() as cs:
																										run_search()
																			a__    =  [' num_beams | length_penalty', model, 'Best score args']
																			a__    =  ['Info']
																			if "translation" in task:
																										expected_strings.append('bleu'   )
																			else:
																										expected_strings.extend(__snake_case   )
																			for w in expected_strings:
																										assert w in cs.out
																			for w in un_expected_strings:
																										assert w not in cs.out
																			assert Path(__snake_case   ).exists()
																			os.remove(Path(__snake_case   )   )








import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																			a__    =  torch.tensor(self.config.trained_betas    ,dtype=torch.floataa   )
												else:
																			a__    =  torch.sin(steps * math.pi / 2   ) ** 2

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = True    ,)      ->					Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																			raise ValueError(
																			    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



					def __len__(   self     :Any   )      ->					Union[str, Any]:
												return self.config.num_train_timesteps




