


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     ):
							__snake_case		:      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(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_     )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Optional[Any]										=       emb.weight.shape
							__snake_case		:      Dict										=       nn.Linear(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     bias=SCREAMING_SNAKE_CASE_     )
							__snake_case		:      Dict										=       emb.weight.data
							return lin_layer







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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = WEIGHTS_NAME     ):
							__snake_case		:      str										=       []
							__snake_case		:      int										=       0
							os.makedirs(SCREAMING_SNAKE_CASE_							,     exist_ok=SCREAMING_SNAKE_CASE_     )

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

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

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

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

							return metadata, index


if __name__ == "__main__":
			_snake_case					:		Dict      =    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					:		Union[str, Any]      =    parser.parse_args()
			_snake_case ,				_snake_case					:		List[Any]      =    shard_on_the_fly(
			    args.nllb_moe_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    128,
			    args.dtype,
			)

			_snake_case					:		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)
			_snake_case					:		str      =    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print("Done")
			model.save_pretrained(args.pytorch_dump_folder_path)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case					:		int      =    logging.get_logger(__name__)

_snake_case					:		int      =    {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "beit"

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Any=8192   ,     lowerCamelCase	:  Dict=768   ,     lowerCamelCase	:  int=12   ,     lowerCamelCase	:  Optional[Any]=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  Tuple="gelu"   ,     lowerCamelCase	:  Union[str, Any]=0.0   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Dict=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Optional[Any]=224   ,     lowerCamelCase	:  Optional[int]=16   ,     lowerCamelCase	:  Any=3   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Any=False   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  int=False   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Tuple=0.1   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  int=[3, 5, 7, 11]   ,     lowerCamelCase	:  str=[1, 2, 3, 6]   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  List[Any]=0.4   ,     lowerCamelCase	:  int=256   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  List[str]=255   ,     **lowerCamelCase	:  Dict   ,     )     ->   int:
														super().__init__(**lowerCamelCase    )

														__snake_case		:      Any										=       vocab_size
														__snake_case		:      List[str]										=       hidden_size
														__snake_case		:      List[Any]										=       num_hidden_layers
														__snake_case		:      Tuple										=       num_attention_heads
														__snake_case		:      Dict										=       intermediate_size
														__snake_case		:      Union[str, Any]										=       hidden_act
														__snake_case		:      Optional[Any]										=       hidden_dropout_prob
														__snake_case		:      Optional[int]										=       attention_probs_dropout_prob
														__snake_case		:      Union[str, Any]										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps

														__snake_case		:      Optional[Any]										=       image_size
														__snake_case		:      List[str]										=       patch_size
														__snake_case		:      Optional[Any]										=       num_channels
														__snake_case		:      Any										=       use_mask_token
														__snake_case		:      List[str]										=       use_absolute_position_embeddings
														__snake_case		:      List[Any]										=       use_relative_position_bias
														__snake_case		:      str										=       use_shared_relative_position_bias
														__snake_case		:      str										=       layer_scale_init_value
														__snake_case		:      Any										=       drop_path_rate
														__snake_case		:      int										=       use_mean_pooling
														# decode head attributes (semantic segmentation)
														__snake_case		:      Optional[Any]										=       out_indices
														__snake_case		:      List[str]										=       pool_scales
														# auxiliary head attributes (semantic segmentation)
														__snake_case		:      int										=       use_auxiliary_head
														__snake_case		:      int										=       auxiliary_loss_weight
														__snake_case		:      Optional[int]										=       auxiliary_channels
														__snake_case		:      int										=       auxiliary_num_convs
														__snake_case		:      str										=       auxiliary_concat_input
														__snake_case		:      List[str]										=       semantic_loss_ignore_index

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  version.parse("1.11"				)

							@property
							def    __snake_case (							self	:  Dict    )     ->   Mapping[str, Mapping[int, str]]:
														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ]    )






							@property
							def    __snake_case (							self	:  str    )     ->   float:
														return 1E-4

