


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 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Dict   ,     __A						:   Tuple						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Any						  =    3
									a__ :str						  =    250

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




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Any:






									"""simple docstring"""






									a__  ,  a__ :Union[str, Any]						  =    self._get_tensors(5						)

									a__ :List[Any]						  =    StoppingCriteriaList(
									    [
									        MaxLengthCriteria(max_length=10						),
									        MaxTimeCriteria(max_time=0.1						),
									    ]						)

									self.assertFalse(criteria(__A   ,     __A						)						)

									a__  ,  a__ :Optional[Any]						  =    self._get_tensors(9						)
									self.assertFalse(criteria(__A   ,     __A						)						)

									a__  ,  a__ :List[Any]						  =    self._get_tensors(10						)
									self.assertTrue(criteria(__A   ,     __A						)						)




						def  _snake_case					(	self						:   List[str]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :str						  =    MaxLengthCriteria(max_length=10						)

									a__  ,  a__ :List[Any]						  =    self._get_tensors(5						)
									self.assertFalse(criteria(__A   ,     __A						)						)

									a__  ,  a__ :List[str]						  =    self._get_tensors(9						)
									self.assertFalse(criteria(__A   ,     __A						)						)

									a__  ,  a__ :str						  =    self._get_tensors(10						)
									self.assertTrue(criteria(__A   ,     __A						)						)




						def  _snake_case					(	self						:   Any						)      ->Optional[int]:






									"""simple docstring"""






									a__ :int						  =    MaxNewTokensCriteria(start_length=5   ,     max_new_tokens=5						)

									a__  ,  a__ :List[Any]						  =    self._get_tensors(5						)
									self.assertFalse(criteria(__A   ,     __A						)						)

									a__  ,  a__ :List[str]						  =    self._get_tensors(9						)
									self.assertFalse(criteria(__A   ,     __A						)						)

									a__  ,  a__ :int						  =    self._get_tensors(10						)
									self.assertTrue(criteria(__A   ,     __A						)						)

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




						def  _snake_case					(	self						:   Any						)      ->List[str]:






									"""simple docstring"""






									a__  ,  a__ :Any						  =    self._get_tensors(5						)

									a__ :Any						  =    MaxTimeCriteria(max_time=0.1						)
									self.assertFalse(criteria(__A   ,     __A						)						)

									a__ :int						  =    MaxTimeCriteria(max_time=0.1   ,     initial_timestamp=time.time() - 0.2						)
									self.assertTrue(criteria(__A   ,     __A						)						)






						def  _snake_case					(	self						:   Dict						)      ->List[str]:






									"""simple docstring"""






									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)   ,     10						)

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

									a__ :Any						  =    validate_stopping_criteria(StoppingCriteriaList()   ,     11						)

									self.assertEqual(len(__A						)   ,     1						)






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case__			 =						'''scheduler_config.json'''


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     1
						lowerCamelCase_         =     2
						lowerCamelCase_         =     3
						lowerCamelCase_         =     4
						lowerCamelCase_         =     5
						lowerCamelCase_         =     6
						lowerCamelCase_         =     7
						lowerCamelCase_         =     8
						lowerCamelCase_         =     9
						lowerCamelCase_         =     10
						lowerCamelCase_         =     11
						lowerCamelCase_         =     12
						lowerCamelCase_         =     13
						lowerCamelCase_         =     14






@dataclass
class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     42






class 					lowerCAmelCase_ :
						lowerCamelCase_         =     SCHEDULER_CONFIG_NAME
						lowerCamelCase_         =     []
						lowerCamelCase_         =     True




						@classmethod
						def  _snake_case					(	cls						:   Union[str, Any]   ,     __A						:   Dict[str, Any] = None   ,     __A						:   Optional[str] = None   ,     __A						:   Optional[int]=False   ,     **__A						:   Union[str, Any]   ,     )      ->List[Any]:






									"""simple docstring"""






									a__  ,  a__  ,  a__ :List[Any]						  =    cls.load_config(
									    pretrained_model_name_or_path=__A   ,     subfolder=__A   ,     return_unused_kwargs=__A   ,     return_commit_hash=__A   ,     **__A   ,     )
									return cls.from_config(__A   ,     return_unused_kwargs=__A   ,     **__A						)




						def  _snake_case					(	self						:   str   ,     __A						:   Union[str, os.PathLike]   ,     __A						:   bool = False   ,     **__A						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									self.save_config(save_directory=__A   ,     push_to_hub=__A   ,     **__A						)




						@property
						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






									return self._get_compatibles()






						@classmethod
						def  _snake_case					(	cls						:   Dict						)      ->int:






									"""simple docstring"""






									a__ :Optional[int]						  =    list(set([cls.__name__] + cls._compatibles						)						)
									a__ :Union[str, Any]						  =    importlib.import_module(__name__.split("."						)[0]						)
									a__ :Optional[int]						  =    [
									    getattr(__A   ,     __A						) for c in compatible_classes_str if hasattr(__A   ,     __A						)
									]
									return compatible_classes


