



'''simple docstring'''

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


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		"open-llama"


						def __init__( self     ,	A_=10_0000     ,	A_=4096     ,	A_=1_1008     ,	A_=32     ,	A_=32     ,	A_="silu"     ,	A_=2048     ,	A_=0.0_2     ,	A_=1e-6     ,	A_=True     ,	A_=0     ,	A_=1     ,	A_=2     ,	A_=False     ,	A_=True     ,	A_=0.1     ,	A_=0.1     ,	A_=True     ,	A_=True     ,	A_=None     ,	**A_     ,	)     ->     Any:
							lowerCAmelCase   =     vocab_size
							lowerCAmelCase   =     max_position_embeddings
							lowerCAmelCase   =     hidden_size
							lowerCAmelCase   =     intermediate_size
							lowerCAmelCase   =     num_hidden_layers
							lowerCAmelCase   =     num_attention_heads
							lowerCAmelCase   =     hidden_act
							lowerCAmelCase   =     initializer_range
							lowerCAmelCase   =     rms_norm_eps
							lowerCAmelCase   =     use_cache
							lowerCAmelCase   =     kwargs.pop(
							    """use_memorry_efficient_attention"""     ,	A_				)
							lowerCAmelCase   =     hidden_dropout_prob
							lowerCAmelCase   =     attention_dropout_prob
							lowerCAmelCase   =     use_stable_embedding
							lowerCAmelCase   =     shared_input_output_embedding
							lowerCAmelCase   =     rope_scaling
							self._rope_scaling_validation()

							super().__init__(
							    pad_token_id=A_     ,	bos_token_id=A_     ,	eos_token_id=A_     ,	tie_word_embeddings=A_     ,	**A_     ,	)




						def     __snake_case   ( self				)     ->     Tuple:
							if self.rope_scaling is None:
								return

							if not isinstance(self.rope_scaling     ,	A_				) or len(self.rope_scaling				) != 2:
								raise ValueError(
								    """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
								    f'got {self.rope_scaling}'				)
							lowerCAmelCase   =     self.rope_scaling.get("""type"""     ,	A_				)
							lowerCAmelCase   =     self.rope_scaling.get("""factor"""     ,	A_				)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
								raise ValueError(
								    f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'				)
							if rope_scaling_factor is None or not isinstance(A_     ,	A_				) or rope_scaling_factor <= 1.0:
								raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'				)




'''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 ):


						'''simple docstring'''



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

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


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

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

							self.assertFalse(criteria(A_     ,	A_				)				)

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

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


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     MaxLengthCriteria(max_length=10				)

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

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

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


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     MaxNewTokensCriteria(start_length=5     ,	max_new_tokens=5				)

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

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

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

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


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

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

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




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

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

							lowerCAmelCase   =     validate_stopping_criteria(StoppingCriteriaList()     ,	11				)

							self.assertEqual(len(A_				)     ,	1				)