"""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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Dict:
				SCREAMING_SNAKE_CASE__		  =				[
				    "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(__UpperCAmelCase  ,					__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				emb.weight.shape
				SCREAMING_SNAKE_CASE__		  =				nn.Linear(__UpperCAmelCase  ,					__UpperCAmelCase  ,					bias=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				emb.weight.data
				return lin_layer




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=None       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				{}
				for old_key in state_dict.keys():
								SCREAMING_SNAKE_CASE__		  =				old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																SCREAMING_SNAKE_CASE__		  =				key.replace("moe_layer.experts.0"  ,					F"""ffn.experts.expert_{expert_idx}"""       )
												else:
																SCREAMING_SNAKE_CASE__		  =				key.replace("moe_layer.experts."  ,					"ffn.experts.expert_"       )
								if "gate" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".moe_layer.gate.wg"  ,					".ffn.router.classifier"       )
								if "fc2" and "experts" not in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".fc2."  ,					".ffn.fc2."       )
								if "fc1" and "experts" not in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".fc1."  ,					".ffn.fc1."       )
								if ".encoder_attn." in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".encoder_attn."  ,					".cross_attention."       )
								if "encoder_attn_layer_norm" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace("encoder_attn_layer_norm"  ,					"cross_attention_layer_norm"       )
								if "final_layer_norm" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace("final_layer_norm"  ,					"ff_layer_norm"       )
								SCREAMING_SNAKE_CASE__		  =				state_dict[old_key]
				return new_dict




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = WEIGHTS_NAME       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				[]
				SCREAMING_SNAKE_CASE__		  =				0
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )

				for expert in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				switch_checkpoint_path + F"""-rank-{expert}.pt"""
								if os.path.isfile(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )["model"]
												remove_ignore_keys_(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__		  =				rename_fairseq_keys(__UpperCAmelCase  ,					__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__		  =				os.path.join(
												    __UpperCAmelCase  ,					weights_name.replace(".bin"  ,					F"""-{len(__UpperCAmelCase       )+1:05d}-of-???.bin"""       )       )
												torch.save(__UpperCAmelCase  ,					__UpperCAmelCase       )
												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(__UpperCAmelCase       )[0]].dtype       )

    # Add the last block
				SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCAmelCase  ,					weights_name.replace(".bin"  ,					F"""-{len(__UpperCAmelCase       )+1:05d}-of-???.bin"""       )       )
				SCREAMING_SNAKE_CASE__		  =				torch.load(switch_checkpoint_path + "-shared.pt"       )["model"]
				remove_ignore_keys_(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				rename_fairseq_keys(__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				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(__UpperCAmelCase       ) == 1:
								SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )
								torch.save(__UpperCAmelCase  ,					__UpperCAmelCase       )
								return {weights_name: sharded_state_dicts[0]}, None
				else:
								torch.save(__UpperCAmelCase  ,					__UpperCAmelCase       )
				# Otherwise, let's build the index
				SCREAMING_SNAKE_CASE__		  =				{}
				for idx, shard in enumerate(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				weights_name.replace(".bin"  ,					F"""-{idx+1:05d}-of-{len(__UpperCAmelCase       ):05d}.bin"""       )
								SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCAmelCase  ,					weights_name.replace(".bin"  ,					F"""-{idx+1:05d}-of-???.bin"""       )       )
								os.rename(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )
								for key in shard:
												SCREAMING_SNAKE_CASE__		  =				shard_file

    # Add the metadata
				SCREAMING_SNAKE_CASE__		  =				{"total_size": total_size}
				SCREAMING_SNAKE_CASE__		  =				{"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )  ,					"w"  ,					encoding="utf-8"       ) as f:
								SCREAMING_SNAKE_CASE__		  =				json.dumps(__UpperCAmelCase  ,					indent=2  ,					sort_keys=__UpperCAmelCase       ) + "\n"
								f.write(__UpperCAmelCase       )

				return metadata, index


if __name__ == "__main__":
						_A   					=    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.',
						)
						_A   					=    parser.parse_args()
						_A					,			_A   					=    shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_2_8,
						    args.dtype,
						)

						_A   					=    NllbMoeConfig.from_pretrained(
						    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						_A   					=    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)





"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42
					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		1



					@register_to_config
					def __init__(						self			:				Tuple   , _snake_case			:				int = 2000   , _snake_case			:				float = 0.15   , _snake_case			:				float = 0.01   , _snake_case			:				float = 1348.0   , _snake_case			:				float = 1e-5   , _snake_case			:				int = 1   , )							->       str:
									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE__		  =				sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__		  =				None

									self.set_sigmas(_snake_case   , _snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[int] = None		)							->       torch.FloatTensor:
									return sample



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				Union[str, torch.device] = None		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

									SCREAMING_SNAKE_CASE__		  =				torch.linspace(1   , _snake_case   , _snake_case   , device=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				float = None   , _snake_case			:				float = None		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				sigma_min if sigma_min is not None else self.config.sigma_min
									SCREAMING_SNAKE_CASE__		  =				sigma_max if sigma_max is not None else self.config.sigma_max
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if self.timesteps is None:
													self.set_timesteps(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
									SCREAMING_SNAKE_CASE__		  =				torch.exp(torch.linspace(math.log(_snake_case		)   , math.log(_snake_case		)   , _snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				List[str]		)							->       List[Any]:
									return torch.where(
									    timesteps == 0   , torch.zeros_like(t.to(timesteps.device		)		)   , self.discrete_sigmas[timesteps - 1].to(timesteps.device		)   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				torch.FloatTensor   , _snake_case			:				int   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SdeVeOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									SCREAMING_SNAKE_CASE__		  =				timestep * torch.ones(
									    sample.shape[0]   , device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
									SCREAMING_SNAKE_CASE__		  =				(timestep * (len(self.timesteps		) - 1)).long()

									# mps requires indices to be in the same device, so we use cpu as is the default with cuda
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(self.discrete_sigmas.device		)

									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas[timesteps].to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				self.get_adjacent_sigma(_snake_case   , _snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				torch.zeros_like(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				(sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									SCREAMING_SNAKE_CASE__		  =				diffusion.flatten()
									while len(diffusion.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				diffusion.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(
									    sample.shape   , layout=sample.layout   , generator=_snake_case   , device=sample.device   , dtype=sample.dtype		)
									SCREAMING_SNAKE_CASE__		  =				sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									SCREAMING_SNAKE_CASE__		  =				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
													return (prev_sample, prev_sample_mean)

									return SdeVeOutput(prev_sample=_snake_case   , prev_sample_mean=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SchedulerOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(sample.shape   , layout=sample.layout   , generator=_snake_case		).to(sample.device		)

									# compute step size from the model_output, the noise, and the snr
									SCREAMING_SNAKE_CASE__		  =				torch.norm(model_output.reshape(model_output.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				torch.norm(noise.reshape(noise.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									SCREAMING_SNAKE_CASE__		  =				step_size * torch.ones(sample.shape[0]		).to(sample.device		)
									# self.repeat_scalar(step_size, sample.shape[0])

									# compute corrected sample: model_output term and noise term
									SCREAMING_SNAKE_CASE__		  =				step_size.flatten()
									while len(step_size.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				step_size.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				sample + step_size * model_output
									SCREAMING_SNAKE_CASE__		  =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , )							->       torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(original_samples.device		)
									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas.to(original_samples.device		)[timesteps]
									SCREAMING_SNAKE_CASE__		  =				(
									    noise * sigmas[:, None, None, None]
									    if noise is not None
									    else torch.randn_like(_snake_case		) * sigmas[:, None, None, None]
									)
									SCREAMING_SNAKE_CASE__		  =				noise + original_samples
									return noisy_samples



					def __len__(						self			:				List[Any]		)							->       int:
									return self.config.num_train_timesteps




