
'''simple docstring'''

import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]       ):
				SCREAMING_SNAKE_CASE  :						int      					= [
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''encoder.embed_positions._float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(_A    ,	_A       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Tuple      					= emb.weight.shape
				SCREAMING_SNAKE_CASE  :						Any      					= nn.Linear(_A    ,	_A    ,	bias=_A       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= emb.weight.data
				return lin_layer






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=None       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
				for old_key in state_dict.keys():
								SCREAMING_SNAKE_CASE  :						Any      					= old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																SCREAMING_SNAKE_CASE  :						Dict      					= key.replace('''moe_layer.experts.0'''    ,	F'''ffn.experts.expert_{expert_idx}'''       )
												else:
																SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= key.replace('''moe_layer.experts.'''    ,	'''ffn.experts.expert_'''       )
								if "gate" in key:
												SCREAMING_SNAKE_CASE  :						int      					= key.replace('''.moe_layer.gate.wg'''    ,	'''.ffn.router.classifier'''       )
								if "fc2" and "experts" not in key:
												SCREAMING_SNAKE_CASE  :						Tuple      					= key.replace('''.fc2.'''    ,	'''.ffn.fc2.'''       )
								if "fc1" and "experts" not in key:
												SCREAMING_SNAKE_CASE  :						Dict      					= key.replace('''.fc1.'''    ,	'''.ffn.fc1.'''       )
								if ".encoder_attn." in key:
												SCREAMING_SNAKE_CASE  :						Dict      					= key.replace('''.encoder_attn.'''    ,	'''.cross_attention.'''       )
								if "encoder_attn_layer_norm" in key:
												SCREAMING_SNAKE_CASE  :						List[Any]      					= key.replace('''encoder_attn_layer_norm'''    ,	'''cross_attention_layer_norm'''       )
								if "final_layer_norm" in key:
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= key.replace('''final_layer_norm'''    ,	'''ff_layer_norm'''       )
								SCREAMING_SNAKE_CASE  :						Any      					= state_dict[old_key]
				return new_dict






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :str = WEIGHTS_NAME       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= []
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 0
				os.makedirs(_A    ,	exist_ok=_A       )

				for expert in range(_A       ):
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= switch_checkpoint_path + F'''-rank-{expert}.pt'''
								if os.path.isfile(_A       ):
												SCREAMING_SNAKE_CASE  :						List[str]      					= torch.load(_A       )['''model''']
												remove_ignore_keys_(_A       )
												SCREAMING_SNAKE_CASE  :						List[Any]      					= rename_fairseq_keys(_A    ,	_A       )
												SCREAMING_SNAKE_CASE  :						str      					= os.path.join(
												    _A    ,	weights_name.replace('''.bin'''    ,	F'''-{len(_A       )+1:05d}-of-???.bin'''       )       )
												torch.save(_A    ,	_A       )
												sharded_state_dicts.append(expert_state.keys()       )
												total_size += sum([value.numel() for key, value in expert_state.items()]       ) * dtype_byte_size(
												    expert_state[list(_A       )[0]].dtype       )

    # Add the last block
				SCREAMING_SNAKE_CASE  :						Any      					= os.path.join(_A    ,	weights_name.replace('''.bin'''    ,	F'''-{len(_A       )+1:05d}-of-???.bin'''       )       )
				SCREAMING_SNAKE_CASE  :						str      					= torch.load(switch_checkpoint_path + '''-shared.pt'''       )['''model''']
				remove_ignore_keys_(_A       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= rename_fairseq_keys(_A    ,	_A       )
				SCREAMING_SNAKE_CASE  :						int      					= shared_weights['''decoder.embed_tokens.weight''']
				sharded_state_dicts.append(shared_weights.keys()       )

				# If we only have the shared weights (dummy model/experts saved on the same file)
				if len(_A       ) == 1:
								SCREAMING_SNAKE_CASE  :						int      					= os.path.join(_A    ,	_A       )
								torch.save(_A    ,	_A       )
								return {weights_name: sharded_state_dicts[0]}, None
				else:
								torch.save(_A    ,	_A       )
				# Otherwise, let's build the index
				SCREAMING_SNAKE_CASE  :						Dict      					= {}
				for idx, shard in enumerate(_A       ):
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= weights_name.replace('''.bin'''    ,	F'''-{idx+1:05d}-of-{len(_A       ):05d}.bin'''       )
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= os.path.join(_A    ,	weights_name.replace('''.bin'''    ,	F'''-{idx+1:05d}-of-???.bin'''       )       )
								os.rename(_A    ,	os.path.join(_A    ,	_A       )       )
								for key in shard:
												SCREAMING_SNAKE_CASE  :						Dict      					= shard_file

    # Add the metadata
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= {'''total_size''': total_size}
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {'''metadata''': metadata, '''weight_map''': weight_map}

				with open(os.path.join(_A    ,	_A       )    ,	'''w'''    ,	encoding='''utf-8'''       ) as f:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= json.dumps(_A    ,	indent=2    ,	sort_keys=_A       ) + '''\n'''
								f.write(_A       )

				return metadata, index


if __name__ == "__main__":
	snake_case_			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--nllb_moe_checkpoint_path""",
	    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
	    type=str,
	    required=False,
	    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
	)
	parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
	    type=str,
	    required=False,
	    help="""Path to the output pytorch model.""",
	)
	snake_case_			=     parser.parse_args()
	snake_case_			=     shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	snake_case_			=     NllbMoeConfig.from_pretrained(
	    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
	)
	config.save_pretrained(args.pytorch_dump_folder_path)
	snake_case_			=     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print("""Done""")
	model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin


class 	a__     (				_lowercase,			_lowercase	):


	@register_to_config
	def __init__(self : List[str],					__UpperCAmelCase : int = 128,					__UpperCAmelCase : int = 256,					__UpperCAmelCase : float = 2000.0,					__UpperCAmelCase : int = 768,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 64,					__UpperCAmelCase : int = 2048,					__UpperCAmelCase : float = 0.1,					)     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()

					SCREAMING_SNAKE_CASE  :						int      					= nn.Sequential(
					    nn.Linear(__UpperCAmelCase,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					nn.Linear(d_model * 4,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Embedding(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= False

					SCREAMING_SNAKE_CASE  :						str      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Dropout(p=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= nn.ModuleList()
					for lyr_num in range(__UpperCAmelCase      ):
									# FiLM conditional T5 decoder
									SCREAMING_SNAKE_CASE  :						List[Any]      					= DecoderLayer(d_model=__UpperCAmelCase,					d_kv=__UpperCAmelCase,					num_heads=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )
									self.decoders.append(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Dropout(p=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.mul(query_input.unsqueeze(-1      ),					key_input.unsqueeze(-2      )      )
					return mask.unsqueeze(-3      )






	def 				lowercase__  (self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= get_timestep_embedding(
					    decoder_noise_time * self.config.max_decoder_noise_time,					embedding_dim=self.config.d_model,					max_period=self.config.max_decoder_noise_time,					).to(dtype=self.dtype      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.conditioning_emb(__UpperCAmelCase      ).unsqueeze(1      )

					assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

					SCREAMING_SNAKE_CASE  :						Tuple      					= decoder_input_tokens.shape[1]

					# If we want to use relative positions for audio context, we can just offset
					# this sequence by the length of encodings_and_masks.
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.broadcast_to(
					    torch.arange(__UpperCAmelCase,					device=decoder_input_tokens.device      ),					(batch, seq_length),					)

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.position_encoding(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.continuous_inputs_projection(__UpperCAmelCase      )
					inputs += position_encodings
					SCREAMING_SNAKE_CASE  :						Dict      					= self.dropout(__UpperCAmelCase      )

					# decoder: No padding present.
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.ones(
					    decoder_input_tokens.shape[:2],					device=decoder_input_tokens.device,					dtype=inputs.dtype      )

					# Translate encoding masks to encoder-decoder masks.
					SCREAMING_SNAKE_CASE  :						Tuple      					= [(x, self.encoder_decoder_mask(__UpperCAmelCase,					__UpperCAmelCase      )) for x, y in encodings_and_masks]

					# cross attend style: concat encodings
					SCREAMING_SNAKE_CASE  :						Dict      					= torch.cat([x[0] for x in encodings_and_encdec_masks],					dim=1      )
					SCREAMING_SNAKE_CASE  :						Dict      					= torch.cat([x[1] for x in encodings_and_encdec_masks],					dim=-1      )

					for lyr in self.decoders:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= lyr(
									    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						str      					= self.decoder_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= self.post_dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.spec_out(__UpperCAmelCase      )
					return spec_out



class 	a__     (				nn.Module	):


	def __init__(self : Union[str, Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any=1e-6      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.ModuleList()

					# cond self attention: layer 0
					self.layer.append(
					    TaLayerSelfAttentionCond(d_model=__UpperCAmelCase,					d_kv=__UpperCAmelCase,					num_heads=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )      )

					# cross attention: layer 1
					self.layer.append(
					    TaLayerCrossAttention(
					        d_model=__UpperCAmelCase,					d_kv=__UpperCAmelCase,					num_heads=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase,					layer_norm_epsilon=__UpperCAmelCase,					)      )

					# Film Cond MLP + dropout: last layer
					self.layer.append(
					    TaLayerFFCond(d_model=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase,					layer_norm_epsilon=__UpperCAmelCase      )      )






	def 				lowercase__  (self : int,					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Optional[Any]=None,					__UpperCAmelCase : Dict=None,					)     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.layer[0](
					    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					if encoder_hidden_states is not None:
									SCREAMING_SNAKE_CASE  :						Any      					= torch.where(encoder_attention_mask > 0,					0,					-1e10      ).to(
									    encoder_hidden_states.dtype      )

									SCREAMING_SNAKE_CASE  :						Any      					= self.layer[1](
									    __UpperCAmelCase,					key_value_states=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					# Apply Film Conditional Feed Forward layer
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.layer[-1](__UpperCAmelCase,					__UpperCAmelCase      )

					return (hidden_states,)



class 	a__     (				nn.Module	):


	def __init__(self : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Dict      					= TaLayerNorm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= TaFiLMLayer(in_features=d_model * 4,					out_features=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Attention(query_dim=__UpperCAmelCase,					heads=__UpperCAmelCase,					dim_head=__UpperCAmelCase,					out_bias=__UpperCAmelCase,					scale_qk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : List[str]=None,					)     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.layer_norm(__UpperCAmelCase      )

					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.FiLMLayer(__UpperCAmelCase,					__UpperCAmelCase      )

					# Self-attention block
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.attention(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= hidden_states + self.dropout(__UpperCAmelCase      )

					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int]      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Attention(query_dim=__UpperCAmelCase,					heads=__UpperCAmelCase,					dim_head=__UpperCAmelCase,					out_bias=__UpperCAmelCase,					scale_qk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=None,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.layer_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.attention(
					    __UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					attention_mask=attention_mask.squeeze(1      ),					)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return layer_output



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Dict      					= TaDenseGatedActDense(d_model=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= TaFiLMLayer(in_features=d_model * 4,					out_features=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any,					__UpperCAmelCase : List[Any]=None      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.layer_norm(__UpperCAmelCase      )
					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= self.film(__UpperCAmelCase,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.DenseReluDense(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : str,					__UpperCAmelCase : str      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= NewGELUActivation()






	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.act(self.wi_a(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.wi_a(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_gelu * hidden_linear
					SCREAMING_SNAKE_CASE  :						int      					= self.dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.wo(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Dict,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any=1e-6      )     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Parameter(torch.ones(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= eps






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= hidden_states.to(torch.floataa      ).pow(2      ).mean(-1,					keepdim=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

					# convert into half-precision if necessary
					if self.weight.dtype in [torch.floataa, torch.bfloataa]:
									SCREAMING_SNAKE_CASE  :						Dict      					= hidden_states.to(self.weight.dtype      )

					return self.weight * hidden_states



class 	a__     (				nn.Module	):


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : torch.Tensor      )     ->			torch.Tensor:

					"""simple docstring"""

					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.04_4715 * torch.pow(__UpperCAmelCase,					3.0      ))      ))



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[str]      )     ->			List[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					out_features * 2,					bias=__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Dict,					__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.scale_bias(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= torch.chunk(__UpperCAmelCase,					2,					-1      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= x * (1 + scale) + shift
					return x




