





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__A									=				transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def      lowerCAmelCase_ (       __a		)     ->		int:







  """simple docstring"""





  if isinstance(__a	,    torch.Tensor		):
    return image
  elif isinstance(__a	,    PIL.Image.Image		):
    lowerCamelCase__:      str			  =[image]

  lowerCamelCase__:      str			  =[trans(img.convert("RGB"		)		) for img in image]
  lowerCamelCase__:      List[Any]			  =torch.stack(__a		)
  return image






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

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



   '''simple docstring'''



   super().__init__()

   # make sure scheduler can always be converted to DDIM
   lowerCamelCase__:      int			  =DDIMScheduler.from_config(scheduler.config)

   self.register_modules(unet=UpperCAmelCase_       , scheduler=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   str       , UpperCAmelCase_				:   Tuple)       ->Any:



   '''simple docstring'''



   if strength < 0 or strength > 1:
     raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   Optional[Any]       , UpperCAmelCase_				:   Dict)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      int			  =min(int(num_inference_steps * strength)       , UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =max(num_inference_steps - init_timestep       , 0)
   lowerCamelCase__:      Tuple			  =self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   int=None)       ->str:



   '''simple docstring'''



   if not isinstance(UpperCAmelCase_       , (torch.Tensor, PIL.Image.Image, list)):
     raise ValueError(
         F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_)}""")

   lowerCamelCase__:      str			  =image.to(device=UpperCAmelCase_       , dtype=UpperCAmelCase_)

   if isinstance(UpperCAmelCase_       , UpperCAmelCase_) and len(UpperCAmelCase_) != batch_size:
     raise ValueError(
         F"""You have passed a list of generators of length {len(UpperCAmelCase_)}, but requested an effective batch"""
         F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

   lowerCamelCase__:      Union[str, Any]			  =init_latents.shape
   lowerCamelCase__:      Any			  =randn_tensor(UpperCAmelCase_       , generator=UpperCAmelCase_       , device=UpperCAmelCase_       , dtype=UpperCAmelCase_)

   # get latents
   print("add noise to latents at timestep"       , UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =self.scheduler.add_noise(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =init_latents

   return latents







 @torch.no_grad()
 def __call__(self				:   str       , UpperCAmelCase_				:   Union[torch.FloatTensor, PIL.Image.Image] = None       , UpperCAmelCase_				:   float = 0.8       , UpperCAmelCase_				:   int = 1       , UpperCAmelCase_				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None       , UpperCAmelCase_				:   float = 0.0       , UpperCAmelCase_				:   int = 50       , UpperCAmelCase_				:   Optional[bool] = None       , UpperCAmelCase_				:   Optional[str] = "pil"       , UpperCAmelCase_				:   bool = True       , )       ->Union[ImagePipelineOutput, Tuple]:



   '''simple docstring'''



   self.check_inputs(UpperCAmelCase_)

   # 2. Preprocess image
   lowerCamelCase__:      int			  =preprocess(UpperCAmelCase_)

   # 3. set timesteps
   self.scheduler.set_timesteps(UpperCAmelCase_       , device=self.device)
   lowerCamelCase__  ,					lowerCamelCase__:      List[Any]			  =self.get_timesteps(UpperCAmelCase_       , UpperCAmelCase_       , self.device)
   lowerCamelCase__:      Union[str, Any]			  =timesteps[:1].repeat(UpperCAmelCase_)

   # 4. Prepare latent variables
   lowerCamelCase__:      str			  =self.prepare_latents(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , self.unet.dtype       , self.device       , UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =latents

   # 5. Denoising loop
   for t in self.progress_bar(UpperCAmelCase_):
     # 1. predict noise model_output
     lowerCamelCase__:      Optional[Any]			  =self.unet(UpperCAmelCase_       , UpperCAmelCase_).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     lowerCamelCase__:      Optional[int]			  =self.scheduler.step(
         UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , eta=UpperCAmelCase_       , use_clipped_model_output=UpperCAmelCase_       , generator=UpperCAmelCase_       , ).prev_sample

   lowerCamelCase__:      str			  =(image / 2 + 0.5).clamp(0       , 1)
   lowerCamelCase__:      Union[str, Any]			  =image.cpu().permute(0       , 2       , 3       , 1).numpy()
   if output_type == "pil":
     lowerCamelCase__:      Optional[int]			  =self.numpy_to_pil(UpperCAmelCase_)

   if not return_dict:
     return (image, latent_timestep.item())

   return ImagePipelineOutput(images=UpperCAmelCase_)





'''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        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[str]	    =					[
		    "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(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[Any]	    =					emb.weight.shape
		_UpperCAmelCase						:				str	    =					nn.Linear(__lowerCAmelCase					,       __lowerCAmelCase					,       bias=__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[int]	    =					emb.weight.data
		return lin_layer






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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = WEIGHTS_NAME      ):
		_UpperCAmelCase						:				Optional[int]	    =					[]
		_UpperCAmelCase						:				Optional[Any]	    =					0
		os.makedirs(__lowerCAmelCase					,       exist_ok=__lowerCAmelCase      )

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

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

    # Add the metadata
		_UpperCAmelCase						:				Any	    =					{"total_size": total_size}
		_UpperCAmelCase						:				List[str]	    =					{"metadata": metadata, "weight_map": weight_map}

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

		return metadata, index


if __name__ == "__main__":
						lowerCamelCase__					=   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.',
						)
						lowerCamelCase__					=   parser.parse_args()
						lowerCamelCase__		,lowerCamelCase__					=   shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						lowerCamelCase__					=   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)
						lowerCamelCase__					=   NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)



