


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'llama'
		_a									=       ['past_key_values']




		def __init__(	self					,     UpperCamelCase_=32000					,     UpperCamelCase_=4096					,     UpperCamelCase_=11008					,     UpperCamelCase_=32					,     UpperCamelCase_=32					,     UpperCamelCase_=None					,     UpperCamelCase_="silu"					,     UpperCamelCase_=2048					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-6					,     UpperCamelCase_=True					,     UpperCamelCase_=0					,     UpperCamelCase_=1					,     UpperCamelCase_=2					,     UpperCamelCase_=1					,     UpperCamelCase_=False					,     UpperCamelCase_=None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				vocab_size
							UpperCamelCase__     :Any							      =				max_position_embeddings
							UpperCamelCase__     :Tuple							      =				hidden_size
							UpperCamelCase__     :Dict							      =				intermediate_size
							UpperCamelCase__     :List[str]							      =				num_hidden_layers
							UpperCamelCase__     :Any							      =				num_attention_heads

							# for backward compatibility
							if num_key_value_heads is None:
												UpperCamelCase__     :List[str]							      =				num_attention_heads

							UpperCamelCase__     :Optional[Any]							      =				num_key_value_heads
							UpperCamelCase__     :Dict							      =				hidden_act
							UpperCamelCase__     :Union[str, Any]							      =				initializer_range
							UpperCamelCase__     :Tuple							      =				rms_norm_eps
							UpperCamelCase__     :Union[str, Any]							      =				pretraining_tp
							UpperCamelCase__     :Any							      =				use_cache
							UpperCamelCase__     :Union[str, Any]							      =				rope_scaling
							self._rope_scaling_validation()

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if self.rope_scaling is None:
												return

							if not isinstance(self.rope_scaling					,     UpperCamelCase_							) 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}'''							)
							UpperCamelCase__     :Optional[int]							      =				self.rope_scaling.get('''type'''					,     UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				self.rope_scaling.get('''factor'''					,     UpperCamelCase_							)
							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(UpperCamelCase_					,     UpperCamelCase_							) 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 tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)