

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 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	3
         lowercase__   :       Tuple				=	250

         lowercase__   :       Union[str, Any]				=	ids_tensor((batch_size, length)						,       lowercase_)
         lowercase__   :       str				=	torch.ones((batch_size, length)						,       device=lowercase_						,       dtype=torch.float) / length
         return input_ids, scores





    def 				snake_case_    (				self):
         lowercase__   :       str				=	self._get_tensors(5)

         lowercase__   :       int				=	StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=10),
                 MaxTimeCriteria(max_time=0.1),
             ])

         self.assertFalse(criteria(lowercase_						,       lowercase_))

         lowercase__   :       Any				=	self._get_tensors(9)
         self.assertFalse(criteria(lowercase_						,       lowercase_))

         lowercase__   :       int				=	self._get_tensors(10)
         self.assertTrue(criteria(lowercase_						,       lowercase_))





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	MaxLengthCriteria(max_length=10)

         lowercase__   :       Tuple				=	self._get_tensors(5)
         self.assertFalse(criteria(lowercase_						,       lowercase_))

         lowercase__   :       str				=	self._get_tensors(9)
         self.assertFalse(criteria(lowercase_						,       lowercase_))

         lowercase__   :       Dict				=	self._get_tensors(10)
         self.assertTrue(criteria(lowercase_						,       lowercase_))





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	MaxNewTokensCriteria(start_length=5						,       max_new_tokens=5)

         lowercase__   :       str				=	self._get_tensors(5)
         self.assertFalse(criteria(lowercase_						,       lowercase_))

         lowercase__   :       Dict				=	self._get_tensors(9)
         self.assertFalse(criteria(lowercase_						,       lowercase_))

         lowercase__   :       Optional[int]				=	self._get_tensors(10)
         self.assertTrue(criteria(lowercase_						,       lowercase_))

         lowercase__   :       List[str]				=	StoppingCriteriaList([criteria])
         self.assertEqual(criteria_list.max_length						,       10)





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self._get_tensors(5)

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

         lowercase__   :       str				=	MaxTimeCriteria(max_time=0.1						,       initial_timestamp=time.time() - 0.2)
         self.assertTrue(criteria(lowercase_						,       lowercase_))





    def 				snake_case_    (				self):
         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])						,       10)

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

         lowercase__   :       Dict				=	validate_stopping_criteria(StoppingCriteriaList()						,       11)

         self.assertEqual(len(lowercase_)						,       1)




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a = None						,       a = None						,       a = True						,       a = None						,       a = False						,       a = None						,       a = True						,       a = "arrow"						,       **a						,       ):
         super().__init__(
             split=a						,       features=a						,       cache_dir=a						,       keep_in_memory=a						,       streaming=a						,       **a						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       Dict				=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
         self.builder.download_and_prepare(
             download_mode=a						,       file_format=self._file_format						,       )
         return self.builder.as_dataset(split=self.split)

