
from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin




class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_		):



	@register_to_config
	def __init__(       self		,    lowercase = 768		,    )							->			str:
					super().__init__()

					lowerCamelCase_         =     nn.Parameter(torch.zeros(1		,    lowercase					)					)
					lowerCamelCase_         =     nn.Parameter(torch.ones(1		,    lowercase					)					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase = None		,    lowercase = None		,    )							->			Optional[int]:
					lowerCamelCase_         =     nn.Parameter(self.mean.to(lowercase					).to(lowercase					)					)
					lowerCamelCase_         =     nn.Parameter(self.std.to(lowercase					).to(lowercase					)					)
					return self



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			List[str]:
					lowerCamelCase_         =     (embeds - self.mean) * 1.0 / self.std
					return embeds



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Union[str, Any]:
					lowerCamelCase_         =     (embeds * self.std) + self.mean
					return embeds




from typing import TYPE_CHECKING

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


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

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


