
"""simple docstring"""




import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      Adafactor,
      AdamW,
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_inverse_sqrt_schedule,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=10						):






  """simple docstring"""



  _lowercase:			int       =     []
  for _ in range(_UpperCamelCase						):
    lrs.append(scheduler.get_lr()[0]						)
    scheduler.step()
  return lrs




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=10						):






  """simple docstring"""



  _lowercase:			Optional[Any]       =     []
  for step in range(_UpperCamelCase						):
    lrs.append(scheduler.get_lr()[0]						)
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        _lowercase:			Dict       =     os.path.join(_UpperCamelCase ,				'''schedule.bin'''						)
        torch.save(scheduler.state_dict() ,				_UpperCamelCase						)

        _lowercase:			Tuple       =     torch.load(_UpperCamelCase						)
        scheduler.load_state_dict(_UpperCamelCase						)
  return lrs


@require_torch
class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Optional[Any]:





         """simple docstring"""




         self.assertEqual(len(_lowercase       ) ,				len(_lowercase       )       )
         for a, b in zip(_lowercase ,				_lowercase       ):
           self.assertAlmostEqual(_lowercase ,				_lowercase ,				delta=_lowercase       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			int       =     torch.tensor([0.1, -0.2, -0.1] ,				requires_grad=_lowercase       )
         _lowercase:			List[Any]       =     torch.tensor([0.4, 0.2, -0.5]       )
         _lowercase:			List[Any]       =     nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         _lowercase:			int       =     AdamW(params=[w] ,				lr=2E-1 ,				weight_decay=0.0       )
         for _ in range(100       ):
           _lowercase:			Optional[Any]       =     criterion(_lowercase ,				_lowercase       )
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
         self.assertListAlmostEqual(w.tolist() ,				[0.4, 0.2, -0.5] ,				tol=1E-2       )



       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			List[Any]       =     torch.tensor([0.1, -0.2, -0.1] ,				requires_grad=_lowercase       )
         _lowercase:			Optional[Any]       =     torch.tensor([0.4, 0.2, -0.5]       )
         _lowercase:			Dict       =     nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         _lowercase:			Optional[Any]       =     Adafactor(
             params=[w] ,				lr=1E-2 ,				eps=(1E-30, 1E-3) ,				clip_threshold=1.0 ,				decay_rate=-0.8 ,				betaa=_lowercase ,				weight_decay=0.0 ,				relative_step=_lowercase ,				scale_parameter=_lowercase ,				warmup_init=_lowercase ,				)
         for _ in range(1000       ):
           _lowercase:			str       =     criterion(_lowercase ,				_lowercase       )
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
         self.assertListAlmostEqual(w.tolist() ,				[0.4, 0.2, -0.5] ,				tol=1E-2       )


@require_torch
class 			__magic_name__       (       unittest.TestCase ):
       UpperCamelCase_   =		nn.Linear(50   ,					50 ) if is_torch_available() else None
       UpperCamelCase_   =		AdamW(m.parameters()   ,					lr=1_0.0 ) if is_torch_available() else None
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_=None       )     ->	Union[str, Any]:





         """simple docstring"""




         self.assertEqual(len(_lowercase       ) ,				len(_lowercase       )       )
         for a, b in zip(_lowercase ,				_lowercase       ):
           self.assertAlmostEqual(_lowercase ,				_lowercase ,				delta=_lowercase ,				msg=_lowercase       )



       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			int       =     {'num_warmup_steps': 2, 'num_training_steps': 10}
         # schedulers doct format
         # function: (sched_args_dict, expected_learning_rates)
         _lowercase:			Dict       =     {
             get_constant_schedule: ({}, [10.0] * self.num_steps),
             get_constant_schedule_with_warmup: (
                 {'num_warmup_steps': 4},
                 [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
             ),
             get_linear_schedule_with_warmup: (
                 {**common_kwargs},
                 [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
             ),
             get_cosine_schedule_with_warmup: (
                 {**common_kwargs},
                 [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
             ),
             get_cosine_with_hard_restarts_schedule_with_warmup: (
                 {**common_kwargs, 'num_cycles': 2},
                 [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
             ),
             get_polynomial_decay_schedule_with_warmup: (
                 {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
                 [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
             ),
             get_inverse_sqrt_schedule: (
                 {'num_warmup_steps': 2},
                 [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
             ),
         }

         for scheduler_func, data in scheds.items():
           _lowercase:			Union[str, Any]       =     data

           _lowercase:			Tuple       =     scheduler_func(self.optimizer ,				**_lowercase       )
           self.assertEqual(len([scheduler.get_lr()[0]]       ) ,				1       )
           _lowercase:			Union[str, Any]       =     unwrap_schedule(_lowercase ,				self.num_steps       )
           self.assertListAlmostEqual(
               _lowercase ,				_lowercase ,				tol=1E-2 ,				msg=f'''failed for {scheduler_func} in normal scheduler''' ,				)

           _lowercase:			int       =     scheduler_func(self.optimizer ,				**_lowercase       )
           if scheduler_func.__name__ != "get_constant_schedule":
             LambdaScheduleWrapper.wrap_scheduler(_lowercase       )  # wrap to test picklability of the schedule
           _lowercase:			List[Any]       =     unwrap_and_save_reload_schedule(_lowercase ,				self.num_steps       )
           self.assertListEqual(_lowercase ,				_lowercase ,				msg=f'''failed for {scheduler_func} in save and reload'''       )


class 			__magic_name__       :
       def __init__(     self ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			Dict       =     fn
       def __call__(     self ,				*A_ ,				**A_       )     ->	List[str]:





         """simple docstring"""




         return self.fn(*_lowercase ,				**_lowercase       )



       @classmethod
       def 							lowercase_							(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     list(map(self ,				scheduler.lr_lambdas       )       )







"""simple docstring"""




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)
A__   :					Any							=   logging.getLogger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     '''\n'''.join(_UpperCamelCase						)
  Path(_UpperCamelCase						).open('''w'''						).writelines(_UpperCamelCase						)


A__   :					int							=   'patrickvonplaten/t5-tiny-random'
A__   :					Tuple							=   'sshleifer/bart-tiny-random'
A__   :					Optional[Any]							=   'sshleifer/tiny-mbart'

A__   :					Optional[int]							=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			str       =     Path(self.get_auto_remove_tmp_dir()       ) / '''utest_input.source'''
         _lowercase:			Tuple       =     input_file_name.parent / '''utest_output.txt'''
         assert not output_file_name.exists()
         _lowercase:			List[str]       =     [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
         _dump_articles(A_ ,				A_       )

         _lowercase:			int       =     str(Path(self.get_auto_remove_tmp_dir()       ) / '''scores.json'''       )
         _lowercase:			Optional[Any]       =     '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
         _lowercase:			str       =     f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

         with patch.object(A_ ,				'''argv''' ,				A_       ):
           run_generate()
           assert Path(A_       ).exists()
           # os.remove(Path(output_file_name))
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         self.run_eval_tester(A_       )
       @parameterized.expand([BART_TINY, MBART_TINY]       )
       @slow
       def 							lowercase_							(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         self.run_eval_tester(A_       )



       @parameterized.expand([T5_TINY, MBART_TINY]       )
       @slow
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     Path(self.get_auto_remove_tmp_dir()       ) / '''utest_input.source'''
         _lowercase:			Dict       =     input_file_name.parent / '''utest_output.txt'''
         assert not output_file_name.exists()

         _lowercase:			int       =     {
             '''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!''',
             ],
         }

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

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