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  UpperCamelCase_						(   self      :     Any ,			_A      :     str		):
												_UpperCamelCase			     =     3
												_UpperCamelCase			     =     250

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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)

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

												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(9		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(10		)
												self.assertTrue(criteria(_A ,			_A		)		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     MaxLengthCriteria(max_length=10		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(9		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(10		)
												self.assertTrue(criteria(_A ,			_A		)		)







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     MaxNewTokensCriteria(start_length=5 ,			max_new_tokens=5		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(9		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(10		)
												self.assertTrue(criteria(_A ,			_A		)		)

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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self._get_tensors(5		)

												_UpperCamelCase			     =     MaxTimeCriteria(max_time=0.1		)
												self.assertFalse(criteria(_A ,			_A		)		)

												_UpperCamelCase			     =     MaxTimeCriteria(max_time=0.1 ,			initial_timestamp=time.time() - 0.2		)
												self.assertTrue(criteria(_A ,			_A		)		)







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

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

												_UpperCamelCase			     =     validate_stopping_criteria(StoppingCriteriaList() ,			11		)

												self.assertEqual(len(_A		) ,			1		)


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     '''gpt_neox_japanese'''

					def __init__(						self       :  Union[str, Any] ,       __UpperCamelCase       :  str=3_2_0_0_0 ,       __UpperCamelCase       :  List[Any]=2_5_6_0 ,       __UpperCamelCase       :  Any=3_2 ,       __UpperCamelCase       :  List[str]=3_2 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  Dict="gelu" ,       __UpperCamelCase       :  List[Any]=1.00 ,       __UpperCamelCase       :  Any=1_0_0_0_0 ,       __UpperCamelCase       :  Optional[Any]=2_0_4_8 ,       __UpperCamelCase       :  Tuple=0.02 ,       __UpperCamelCase       :  List[str]=1E-5 ,       __UpperCamelCase       :  str=True ,       __UpperCamelCase       :  str=3_1_9_9_6 ,       __UpperCamelCase       :  int=3_1_9_9_9 ,       __UpperCamelCase       :  Optional[Any]=0.1 ,       __UpperCamelCase       :  Tuple=0.0 ,       **__UpperCamelCase       :  List[str] ,       ):
												super().__init__(bos_token_id=__UpperCamelCase ,       eos_token_id=__UpperCamelCase ,       **__UpperCamelCase )
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_multiple_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       rotary_pct
												lowerCamelCase_					=       rotary_emb_base
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       layer_norm_eps
												lowerCamelCase_					=       use_cache
												lowerCamelCase_					=       attention_dropout
												lowerCamelCase_					=       hidden_dropout





