




'''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   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def     __snake_case						(	self     :						str      ,       _lowercase     :						int)    ->							int:
								A_									= 3
								A_									= 250

								A_									= ids_tensor((batch_size, length)      ,       _lowercase)
								A_									= torch.ones((batch_size, length)      ,       device=_lowercase      ,       dtype=torch.float) / length
								return input_ids, scores



					def     __snake_case						(	self     :						List[Any])    ->							Optional[Any]:
								A_		, A_									= self._get_tensors(5)

								A_									= StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=10),
								        MaxTimeCriteria(max_time=0.1),
								    ])

								self.assertFalse(criteria(_lowercase      ,       _lowercase))

								A_		, A_									= self._get_tensors(9)
								self.assertFalse(criteria(_lowercase      ,       _lowercase))

								A_		, A_									= self._get_tensors(10)
								self.assertTrue(criteria(_lowercase      ,       _lowercase))



					def     __snake_case						(	self     :						Dict)    ->							str:
								A_									= MaxLengthCriteria(max_length=10)

								A_		, A_									= self._get_tensors(5)
								self.assertFalse(criteria(_lowercase      ,       _lowercase))

								A_		, A_									= self._get_tensors(9)
								self.assertFalse(criteria(_lowercase      ,       _lowercase))

								A_		, A_									= self._get_tensors(10)
								self.assertTrue(criteria(_lowercase      ,       _lowercase))



					def     __snake_case						(	self     :						Union[str, Any])    ->							Tuple:
								A_									= MaxNewTokensCriteria(start_length=5      ,       max_new_tokens=5)

								A_		, A_									= self._get_tensors(5)
								self.assertFalse(criteria(_lowercase      ,       _lowercase))

								A_		, A_									= self._get_tensors(9)
								self.assertFalse(criteria(_lowercase      ,       _lowercase))

								A_		, A_									= self._get_tensors(10)
								self.assertTrue(criteria(_lowercase      ,       _lowercase))

								A_									= StoppingCriteriaList([criteria])
								self.assertEqual(criteria_list.max_length      ,       10)



					def     __snake_case						(	self     :						Any)    ->							Optional[Any]:
								A_		, A_									= self._get_tensors(5)

								A_									= MaxTimeCriteria(max_time=0.1)
								self.assertFalse(criteria(_lowercase      ,       _lowercase))

								A_									= MaxTimeCriteria(max_time=0.1      ,       initial_timestamp=time.time() - 0.2)
								self.assertTrue(criteria(_lowercase      ,       _lowercase))



					def     __snake_case						(	self     :						Any)    ->							int:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])      ,       10)

								with self.assertWarns(_lowercase):
											validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])      ,       11)

								A_									= validate_stopping_criteria(StoppingCriteriaList()      ,       11)

								self.assertEqual(len(_lowercase)      ,       1)










'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    'split_dict'       ,[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42       ,dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'       ,num_bytes=1337       ,num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]       ,)
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= split_dict._to_yaml_list()
			assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
			A_									= SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE_     )
			for split_name, split_info in split_dict.items():
						# dataset_name field is deprecated, and is therefore not part of the YAML dump
						A_									= None
						# the split name of split_dict takes over the name of the split info object
						A_									= split_name
			assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'       ,[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE_     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[Any]:
			# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
			# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
			A_									= asdict(SplitDict({'train': split_info}     )     )
			assert "dataset_name" in split_dict_asdict["train"]
			assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name




