
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					(		_lowerCamelCase :			Dict)					->    Tuple:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					[
							    "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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int])					->    Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					emb.weight.shape
							__UpperCamelCase	:   Optional[Any]			   =					nn.Linear(_lowerCamelCase						,  _lowerCamelCase						,  bias=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					emb.weight.data
							return lin_layer




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			Tuple=None)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   List[str]			   =					{}
							for old_key in state_dict.keys():
														__UpperCamelCase	:   Union[str, Any]			   =					old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												__UpperCamelCase	:   Any			   =					key.replace("moe_layer.experts.0"						,  F'ffn.experts.expert_{expert_idx}')
																					else:
																												__UpperCamelCase	:   Dict			   =					key.replace("moe_layer.experts."						,  "ffn.experts.expert_")
														if "gate" in key:
																					__UpperCamelCase	:   int			   =					key.replace(".moe_layer.gate.wg"						,  ".ffn.router.classifier")
														if "fc2" and "experts" not in key:
																					__UpperCamelCase	:   Union[str, Any]			   =					key.replace(".fc2."						,  ".ffn.fc2.")
														if "fc1" and "experts" not in key:
																					__UpperCamelCase	:   Union[str, Any]			   =					key.replace(".fc1."						,  ".ffn.fc1.")
														if ".encoder_attn." in key:
																					__UpperCamelCase	:   int			   =					key.replace(".encoder_attn."						,  ".cross_attention.")
														if "encoder_attn_layer_norm" in key:
																					__UpperCamelCase	:   List[str]			   =					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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Dict						,  _lowerCamelCase :			List[Any]						,  _lowerCamelCase :			List[Any]						,  _lowerCamelCase :			Dict						,  _lowerCamelCase :			str = WEIGHTS_NAME)					->    List[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					[]
							__UpperCamelCase	:   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	:   Optional[int]			   =					torch.load(_lowerCamelCase)["model"]
																					remove_ignore_keys_(_lowerCamelCase)
																					__UpperCamelCase	:   List[Any]			   =					rename_fairseq_keys(_lowerCamelCase						,  _lowerCamelCase)
																					__UpperCamelCase	:   Optional[Any]			   =					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	:   str			   =					os.path.join(_lowerCamelCase						,  weights_name.replace(".bin"						,  F'-{len(_lowerCamelCase)+1:05d}-of-???.bin'))
							__UpperCamelCase	:   List[str]			   =					torch.load(switch_checkpoint_path + "-shared.pt")["model"]
							remove_ignore_keys_(_lowerCamelCase)
							__UpperCamelCase	:   List[Any]			   =					rename_fairseq_keys(_lowerCamelCase						,  _lowerCamelCase)
							__UpperCamelCase	:   Dict			   =					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	:   int			   =					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	:   Optional[Any]			   =					{}
							for idx, shard in enumerate(_lowerCamelCase):
														__UpperCamelCase	:   int			   =					weights_name.replace(".bin"						,  F'-{idx+1:05d}-of-{len(_lowerCamelCase):05d}.bin')
														__UpperCamelCase	:   Optional[int]			   =					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	:   Any			   =					shard_file

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

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

							return metadata, index


if __name__ == "__main__":
		lowercase  :					int													=       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.',
		)
		lowercase  :					str													=       parser.parse_args()
		lowercase							,			lowercase  :					List[Any]													=       shard_on_the_fly(
		    args.nllb_moe_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    128,
		    args.dtype,
		)

		lowercase  :					Optional[int]													=       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)
		lowercase  :					Optional[Any]													=       NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
		print('Done')
		model.save_pretrained(args.pytorch_dump_folder_path)

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
		lowercase  :					int													=       {
		    'linear': PIL.Image.Resampling.BILINEAR,
		    'bilinear': PIL.Image.Resampling.BILINEAR,
		    'bicubic': PIL.Image.Resampling.BICUBIC,
		    'lanczos': PIL.Image.Resampling.LANCZOS,
		    'nearest': PIL.Image.Resampling.NEAREST,
		}
else:
		lowercase  :					Any													=       {
		    'linear': PIL.Image.LINEAR,
		    'bilinear': PIL.Image.BILINEAR,
		    'bicubic': PIL.Image.BICUBIC,
		    'lanczos': PIL.Image.LANCZOS,
		    'nearest': PIL.Image.NEAREST,
		}




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					(images / 2 + 0.5).clamp(0						,  1)
							__UpperCamelCase	:   int			   =					images.cpu().permute(0						,  2						,  3						,  1).float().numpy()
							__UpperCamelCase	:   Dict			   =					numpy_to_pil(_lowerCamelCase)
							return images




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Optional[Any]:






							'''simple docstring'''





							if images.ndim == 3:
														__UpperCamelCase	:   Optional[int]			   =					images[None, ...]
							__UpperCamelCase	:   Dict			   =					(images * 255).round().astype("uint8")
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														__UpperCamelCase	:   int			   =					[Image.fromarray(image.squeeze()						,  mode="L") for image in images]
							else:
														__UpperCamelCase	:   Union[str, Any]			   =					[Image.fromarray(_lowerCamelCase) for image in images]

							return pil_images