
"""simple docstring"""




import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  if isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    _lowercase:			str       =     np.full((len(_UpperCamelCase						), sequence_length, 2) ,				_UpperCamelCase						)
  else:
    _lowercase:			List[Any]       =     np.full((len(_UpperCamelCase						), sequence_length) ,				_UpperCamelCase						)

  for i, tensor in enumerate(_UpperCamelCase						):
    if padding_side == "right":
      if isinstance(_UpperCamelCase ,				_UpperCamelCase						):
        _lowercase:			int       =     tensor[:sequence_length]
      else:
        _lowercase:			Tuple       =     tensor[:sequence_length]
    else:
      if isinstance(_UpperCamelCase ,				_UpperCamelCase						):
        _lowercase:			Any       =     tensor[:sequence_length]
      else:
        _lowercase:			Any       =     tensor[:sequence_length]

  return out_tensor.tolist()




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			int       =     ord(_UpperCamelCase						)
  if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
    return True
  _lowercase:			str       =     unicodedata.category(_UpperCamelCase						)
  if cat.startswith('''P'''						):
    return True
  return False







@dataclass
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		42
       UpperCamelCase_   =		True
       UpperCamelCase_   =		None
       UpperCamelCase_   =		None
       UpperCamelCase_   =		-100
       UpperCamelCase_   =		"pt"
       def 							lowercase_							(     self ,				A_       )     ->	List[str]:





         """simple docstring"""




         import torch

         _lowercase:			Any       =     '''label''' if '''label''' in features[0].keys() else '''labels'''
         _lowercase:			Optional[int]       =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
         _lowercase:			Dict       =     self.tokenizer.pad(
             A_ ,				padding=self.padding ,				max_length=self.max_length ,				pad_to_multiple_of=self.pad_to_multiple_of ,				return_tensors='''pt''' if labels is None else None ,				)

         if labels is None:
           return batch

         _lowercase:			Tuple       =     torch.tensor(batch['''entity_ids''']       ).shape[1]
         _lowercase:			List[str]       =     self.tokenizer.padding_side
         if padding_side == "right":
           _lowercase:			Union[str, Any]       =     [
               list(A_       ) + [self.label_pad_token_id] * (sequence_length - len(A_       )) for label in labels
           ]
         else:
           _lowercase:			Tuple       =     [
               [self.label_pad_token_id] * (sequence_length - len(A_       )) + list(A_       ) for label in labels
           ]

         _lowercase:			List[Any]       =     [feature['''ner_tags'''] for feature in features]
         _lowercase:			List[Any]       =     padding_tensor(A_ ,				-1 ,				A_ ,				A_       )
         _lowercase:			int       =     [feature['''original_entity_spans'''] for feature in features]
         _lowercase:			Optional[int]       =     padding_tensor(A_ ,				(-1, -1) ,				A_ ,				A_       )
         _lowercase:			Dict       =     {k: torch.tensor(A_ ,				dtype=torch.intaa       ) for k, v in batch.items()}

         return batch


"""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_       )       )
