





'''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_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	str:
						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(_A,			_A   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	str:
						UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  emb.weight.shape
						UpperCAmelCase_			:      Any     						=  nn.Linear(_A,			_A,			bias=_A   )
						UpperCAmelCase_			:      str     						=  emb.weight.data
						return lin_layer





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			List[Any]=None   ) ->	Any:
						UpperCAmelCase_			:      List[str]     						=  {}
						for old_key in state_dict.keys():
												UpperCAmelCase_			:      Tuple     						=  old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								UpperCAmelCase_			:      Union[str, Any]     						=  key.replace('''moe_layer.experts.0''',			F"""ffn.experts.expert_{expert_idx}"""   )
																		else:
																								UpperCAmelCase_			:      Optional[int]     						=  key.replace('''moe_layer.experts.''',			'''ffn.experts.expert_'''   )
												if "gate" in key:
																		UpperCAmelCase_			:      Optional[int]     						=  key.replace('''.moe_layer.gate.wg''',			'''.ffn.router.classifier'''   )
												if "fc2" and "experts" not in key:
																		UpperCAmelCase_			:      Dict     						=  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_			:      Tuple     						=  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_			:      Dict     						=  key.replace('''final_layer_norm''',			'''ff_layer_norm'''   )
												UpperCAmelCase_			:      str     						=  state_dict[old_key]
						return new_dict





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			str = WEIGHTS_NAME   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      int     						=  []
						UpperCAmelCase_			:      Union[str, Any]     						=  0
						os.makedirs(_A,			exist_ok=_A   )

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

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

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

						return metadata, index


if __name__ == "__main__":
					snake_case_     :       str						=  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_     :       Tuple						=  parser.parse_args()
					snake_case_     :       List[Any]						=  shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    1_28,
					    args.dtype,
					)

					snake_case_     :       str						=  NllbMoeConfig.from_pretrained(
					    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
					)
					config.save_pretrained(args.pytorch_dump_folder_path)
					snake_case_     :       Optional[int]						=  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print("Done")
					model.save_pretrained(args.pytorch_dump_folder_path)












'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_     :       Union[str, Any]						=  logging.get_logger(__name__)


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Tuple   )					->       np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  get_image_size(__magic_name__   )
													# Rounds the height and width down to the closest multiple of size_divisor
													UpperCAmelCase_			:      Dict     						=  height // size_divisor * size_divisor
													UpperCAmelCase_			:      Dict     						=  width // size_divisor * size_divisor
													UpperCAmelCase_			:      Any     						=  resize(__magic_name__       ,     (new_h, new_w)       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )
													return image

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Optional[Any]   )					->       np.ndarray:







													"""simple docstring"""
													return rescale(image=__magic_name__       ,     scale=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[TensorType, str]] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Tuple       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  resample if resample is not None else self.resample

													if do_resize and size_divisor is None:
																			raise ValueError('''size_divisor is required for resizing'''   )

													UpperCAmelCase_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

													if not valid_images(__magic_name__   ):
																			raise ValueError('''Invalid image(s)'''   )

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

													if do_resize:
																			UpperCAmelCase_			:      str     						=  [self.resize(__magic_name__       ,     size_divisor=__magic_name__       ,     resample=__magic_name__   ) for image in images]

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





