



"""simple docstring"""





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


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''gpt_neox'''






							def __init__(  self	,       snake_case=50432	,       snake_case=6144	,       snake_case=44	,       snake_case=64	,       snake_case=24576	,       snake_case="gelu"	,       snake_case=0.25	,       snake_case=10000	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.1	,       snake_case=2048	,       snake_case=0.02	,       snake_case=1E-5	,       snake_case=True	,       snake_case=0	,       snake_case=2	,       snake_case=False	,       snake_case=True	,       snake_case=None	,       **snake_case	,       )			->							Union[str, Any]:
									super().__init__(bos_token_id=snake_case	,       eos_token_id=snake_case	,       **snake_case						)
									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		max_position_embeddings
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		rotary_pct
									_UpperCAmelCase							 =		rotary_emb_base
									_UpperCAmelCase							 =		attention_dropout
									_UpperCAmelCase							 =		hidden_dropout
									_UpperCAmelCase							 =		classifier_dropout
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps
									_UpperCAmelCase							 =		use_cache
									_UpperCAmelCase							 =		tie_word_embeddings
									_UpperCAmelCase							 =		use_parallel_residual
									_UpperCAmelCase							 =		rope_scaling
									self._rope_scaling_validation()

									if self.hidden_size % self.num_attention_heads != 0:
											raise ValueError(
											    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'						)






							def        lowerCamelCase_   (  self						)			->							Dict:
									if self.rope_scaling is None:
											return

									if not isinstance(self.rope_scaling	,       snake_case						) 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							 =		self.rope_scaling.get('type'	,       snake_case						)
									_UpperCAmelCase							 =		self.rope_scaling.get('factor'	,       snake_case						)
									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(snake_case	,       snake_case						) 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"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  							=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''MvpTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MvpForCausalLM''',
					    '''MvpForConditionalGeneration''',
					    '''MvpForQuestionAnswering''',
					    '''MvpForSequenceClassification''',
					    '''MvpModel''',
					    '''MvpPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


