


"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


snake_case__		:     Optional[Any]    				=				namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[str]			):
      return (abs(source - target			) / target) < 0.01






@pytest.mark.integration
def      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   _TestCommandArgs(dataset=_snake_case ,   all_configs=_snake_case ,   save_infos=_snake_case			)
      lowerCAmelCase					:	str       =   TestCommand(*_snake_case			)
      test_command.run()
      lowerCAmelCase					:	str       =   os.path.join(_snake_case ,   '''README.md'''			)
      assert os.path.exists(_snake_case			)
      lowerCAmelCase					:	Tuple       =   DatasetInfosDict.from_directory(_snake_case			)
      lowerCAmelCase					:	List[str]       =   DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''			)			),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']			)			),
                          '''langs''': Sequence(Value('''string'''			)			),
                          '''spans''': Sequence(Value('''string'''			)			),
                      }			) ,   splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ] ,   download_size=3940680 ,   dataset_size=2589981 ,   )
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   getattr(dataset_infos['''default'''] ,   _snake_case			), getattr(expected_dataset_infos['''default'''] ,   _snake_case			)
            if key == "num_bytes":
                  assert is_apercent_close(_snake_case ,   _snake_case			)
            elif key == "splits":
                  assert list(_snake_case			) == list(_snake_case			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes			)
            else:
                  result == expected






"""simple docstring"""





import time
import unittest

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

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )



@require_torch
class 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					int				):
           lowerCAmelCase					:	str       =   3
           lowerCAmelCase					:	Tuple       =   2_5_0

           lowerCAmelCase					:	Optional[Any]       =   ids_tensor((batch_size, length)					,				UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   torch.ones((batch_size, length)					,				device=UpperCamelCase_					,				dtype=torch.float				) / length
           return input_ids, scores






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self._get_tensors(5				)

           lowerCAmelCase					:	Union[str, Any]       =   StoppingCriteriaList(
               [
                   MaxLengthCriteria(max_length=1_0				),
                   MaxTimeCriteria(max_time=0.1				),
               ]				)

           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   MaxLengthCriteria(max_length=1_0				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[Any]       =   self._get_tensors(5				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	str       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   MaxNewTokensCriteria(start_length=5					,				max_new_tokens=5				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self._get_tensors(5				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	str       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase					:	Dict       =   StoppingCriteriaList([criteria]				)
           self.assertEqual(criteria_list.max_length					,				1_0				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   self._get_tensors(5				)

           lowerCAmelCase					:	List[str]       =   MaxTimeCriteria(max_time=0.1				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase					:	List[str]       =   MaxTimeCriteria(max_time=0.1					,				initial_timestamp=time.time() - 0.2				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					str				):
           validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)					,				1_0				)

           with self.assertWarns(UpperCamelCase_				):
                 validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)					,				1_1				)

           lowerCAmelCase					:	str       =   validate_stopping_criteria(StoppingCriteriaList()					,				1_1				)

           self.assertEqual(len(UpperCamelCase_				)					,				1				)


