



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=sys.maxsize		):
							A__							=       "bilinear"
							A__							=       max_size
							A__							=       short_edge_length



	def __call__(       self			,	UpperCAmelCase__		):
							A__							=       []
							for img in imgs:
													A__						,       A__							=       img.shape[:2]
													# later: provide list and randomly choose index for resize
													A__							=       np.random.randint(self.short_edge_length[0]			,	self.short_edge_length[1] + 1		)
													if size == 0:
																			return img
													A__							=       size * 1.0 / min(UpperCAmelCase__			,	UpperCAmelCase__		)
													if h < w:
																			A__						,       A__							=       size, scale * w
													else:
																			A__						,       A__							=       scale * h, size
													if max(UpperCAmelCase__			,	UpperCAmelCase__		) > self.max_size:
																			A__							=       self.max_size * 1.0 / max(UpperCAmelCase__			,	UpperCAmelCase__		)
																			A__							=       newh * scale
																			A__							=       neww * scale
													A__							=       int(neww + 0.5		)
													A__							=       int(newh + 0.5		)

													if img.dtype == np.uinta:
																			A__							=       Image.fromarray(UpperCAmelCase__		)
																			A__							=       pil_image.resize((neww, newh)			,	PILImageResampling.BILINEAR		)
																			A__							=       np.asarray(UpperCAmelCase__		)
													else:
																			A__							=       img.permute(2			,	0			,	1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
																			A__							=       nn.functional.interpolate(
																			    UpperCAmelCase__			,	(newh, neww)			,	mode=self.interp_method			,	align_corners=UpperCAmelCase__		).squeeze(0		)
													img_augs.append(UpperCAmelCase__		)

							return img_augs






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__		):
							A__							=       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,	cfg.INPUT.MAX_SIZE_TEST		)
							A__							=       cfg.INPUT.FORMAT
							A__							=       cfg.SIZE_DIVISIBILITY
							A__							=       cfg.PAD_VALUE
							A__							=       cfg.INPUT.MAX_SIZE_TEST
							A__							=       cfg.MODEL.DEVICE
							A__							=       torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)			,	1			,	1		)
							A__							=       torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)			,	1			,	1		)
							A__							=       lambda UpperCAmelCase__		: (x - self.pixel_mean) / self.pixel_std



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       tuple(max(UpperCAmelCase__		) for s in zip(*[img.shape for img in images]		)		)
							A__							=       [im.shape[-2:] for im in images]
							A__							=       [
							    nn.functional.pad(
							        UpperCAmelCase__			,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,	value=self.pad_value			,	)
							    for size, im in zip(UpperCAmelCase__			,	UpperCAmelCase__		)
							]

							return torch.stack(UpperCAmelCase__		), torch.tensor(UpperCAmelCase__		)



	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=False		):
							with torch.no_grad():
													if not isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
																			A__							=       [images]
													if single_image:
																			assert len(UpperCAmelCase__		) == 1
													for i in range(len(UpperCAmelCase__		)		):
																			if isinstance(images[i]			,	torch.Tensor		):
																									images.insert(UpperCAmelCase__			,	images.pop(UpperCAmelCase__		).to(self.device		).float()		)
																			elif not isinstance(images[i]			,	torch.Tensor		):
																									images.insert(
																									    UpperCAmelCase__			,	torch.as_tensor(img_tensorize(images.pop(UpperCAmelCase__		)			,	input_format=self.input_format		)		)
																									    .to(self.device		)
																									    .float()			,	)
            # resize smallest edge
													A__							=       torch.tensor([im.shape[:2] for im in images]		)
													A__							=       self.aug(UpperCAmelCase__		)
													# transpose images and convert to torch tensors
													# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
													# now normalize before pad to avoid useless arithmetic
													A__							=       [self.normalizer(UpperCAmelCase__		) for x in images]
													# now pad them to do the following operations
													A__						,       A__							=       self.pad(UpperCAmelCase__		)
													# Normalize

													if self.size_divisibility > 0:
																			raise NotImplementedError()
													# pad
													A__							=       torch.true_divide(UpperCAmelCase__			,	UpperCAmelCase__		)
													if single_image:
																			return images[0], sizes[0], scales_yx[0]
													else:
																			return images, sizes, scales_yx





def  UpperCamelCase						(       _A			:				int			,							_A			:				str    )->       List[Any]:





						"""simple docstring"""





						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Tuple[int, int]    )->       Tuple:





						"""simple docstring"""





						assert torch.isfinite(_A    ).all(), "Box tensor contains infinite or NaN!"
						A__						,       A__							=       box_size
						tensor[:, 0].clamp_(min=0			,							max=_A    )
						tensor[:, 1].clamp_(min=0			,							max=_A    )
						tensor[:, 2].clamp_(min=0			,							max=_A    )
						tensor[:, 3].clamp_(min=0			,							max=_A    )










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  UpperCamelCase						(       _A			:				Tuple    )->       Dict:





						"""simple docstring"""





						A__							=       [
						    "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  UpperCamelCase						(       _A			:				int    )->       Optional[Any]:





						"""simple docstring"""





						A__						,       A__							=       emb.weight.shape
						A__							=       nn.Linear(_A			,							_A			,							bias=_A    )
						A__							=       emb.weight.data
						return lin_layer
def  UpperCamelCase						(       _A			:				str			,							_A			:				Optional[Any]=None    )->       str:





						"""simple docstring"""





						A__							=       {}
						for old_key in state_dict.keys():
												A__							=       old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								A__							=       key.replace("moe_layer.experts.0"			,							f"""ffn.experts.expert_{expert_idx}"""    )
																		else:
																								A__							=       key.replace("moe_layer.experts."			,							"ffn.experts.expert_"    )
												if "gate" in key:
																		A__							=       key.replace(".moe_layer.gate.wg"			,							".ffn.router.classifier"    )
												if "fc2" and "experts" not in key:
																		A__							=       key.replace(".fc2."			,							".ffn.fc2."    )
												if "fc1" and "experts" not in key:
																		A__							=       key.replace(".fc1."			,							".ffn.fc1."    )
												if ".encoder_attn." in key:
																		A__							=       key.replace(".encoder_attn."			,							".cross_attention."    )
												if "encoder_attn_layer_norm" in key:
																		A__							=       key.replace("encoder_attn_layer_norm"			,							"cross_attention_layer_norm"    )
												if "final_layer_norm" in key:
																		A__							=       key.replace("final_layer_norm"			,							"ff_layer_norm"    )
												A__							=       state_dict[old_key]
						return new_dict
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Tuple			,							_A			:				int			,							_A			:				str			,							_A			:				str = WEIGHTS_NAME    )->       List[str]:





						"""simple docstring"""





						A__							=       []
						A__							=       0
						os.makedirs(_A			,							exist_ok=_A    )

						for expert in range(_A    ):
												A__							=       switch_checkpoint_path + f"""-rank-{expert}.pt"""
												if os.path.isfile(_A    ):
																		A__							=       torch.load(_A    )["model"]
																		remove_ignore_keys_(_A    )
																		A__							=       rename_fairseq_keys(_A			,							_A    )
																		A__							=       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
						A__							=       os.path.join(_A			,							weights_name.replace(".bin"			,							f"""-{len(_A    )+1:05d}-of-???.bin"""    )    )
						A__							=       torch.load(switch_checkpoint_path + "-shared.pt"    )["model"]
						remove_ignore_keys_(_A    )
						A__							=       rename_fairseq_keys(_A			,							_A    )
						A__							=       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:
												A__							=       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
						A__							=       {}
						for idx, shard in enumerate(_A    ):
												A__							=       weights_name.replace(".bin"			,							f"""-{idx+1:05d}-of-{len(_A    ):05d}.bin"""    )
												A__							=       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:
																		A__							=       shard_file

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

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

						return metadata, index


if __name__ == "__main__":
						UpperCAmelCase_							:      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.",
						)
						UpperCAmelCase_							:      Union[str, Any]      =  parser.parse_args()
						UpperCAmelCase_   ,	UpperCAmelCase_							:      Optional[int]      =  shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCAmelCase_							:      Any      =  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)
						UpperCAmelCase_							:      Tuple      =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)





