


'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Optional[Any] = None  ,_UpperCamelCase		:Tuple = None  ,_UpperCamelCase		:Any = None  ,_UpperCamelCase		:Optional[int] = False  ,_UpperCamelCase		:int = False  ,_UpperCamelCase		:str = None  ,_UpperCamelCase		:Optional[Any] = None  ,**_UpperCamelCase		:int  ,):
									super().__init__(
									    A__  ,split=A__  ,features=A__  ,cache_dir=A__  ,keep_in_memory=A__  ,streaming=A__  ,num_proc=A__  ,**A__  ,)
									snake_case_		:							Tuple			       =						field
									snake_case_		:							Optional[int]			       =						path_or_paths if isinstance(A__  ,A__			) else {self.split: path_or_paths}
									snake_case_		:							List[Any]			       =						Json(
									    cache_dir=A__  ,data_files=A__  ,features=A__  ,field=A__  ,**A__  ,)





							def 		a__   (  self		:Dict			):
									# Build iterable dataset
									if self.streaming:
											snake_case_		:							Dict			       =						self.builder.as_streaming_dataset(split=self.split			)
									# Build regular (map-style) dataset
									else:
											snake_case_		:							str			       =						None
											snake_case_		:							str			       =						None
											snake_case_		:							Union[str, Any]			       =						None
											snake_case_		:							Any			       =						None

											self.builder.download_and_prepare(
											    download_config=A__  ,download_mode=A__  ,verification_mode=A__  ,base_path=A__  ,num_proc=self.num_proc  ,)
											snake_case_		:							Union[str, Any]			       =						self.builder.as_dataset(
											    split=self.split  ,verification_mode=A__  ,in_memory=self.keep_in_memory			)
									return dataset





class 						__UpperCamelCase	:





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Dict = None  ,_UpperCamelCase		:int = None  ,**_UpperCamelCase		:Any  ,):
									if num_proc is not None and num_proc <= 0:
											raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''			)

									snake_case_		:							int			       =						dataset
									snake_case_		:							Any			       =						path_or_buf
									snake_case_		:							Union[str, Any]			       =						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		:							str			       =						num_proc
									snake_case_		:							Union[str, Any]			       =						"""utf-8"""
									snake_case_		:							List[Any]			       =						to_json_kwargs





							def 		a__   (  self		:List[str]			):
									snake_case_		:							List[Any]			       =						self.to_json_kwargs.pop("""path_or_buf"""  ,A__			)
									snake_case_		:							List[Any]			       =						self.to_json_kwargs.pop("""orient"""  ,"""records"""			)
									snake_case_		:							Optional[Any]			       =						self.to_json_kwargs.pop("""lines"""  ,True if orient == """records""" else False			)
									snake_case_		:							Optional[int]			       =						self.to_json_kwargs.pop("""index"""  ,False if orient in ["""split""", """table"""] else True			)
									snake_case_		:							int			       =						self.to_json_kwargs.pop("""compression"""  ,A__			)

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
											raise NotImplementedError(F'''`datasets` currently does not support {compression} compression'''			)

									if isinstance(self.path_or_buf  ,(str, bytes, os.PathLike)			):
											with fsspec.open(self.path_or_buf  ,"""wb"""  ,compression=A__			) as buffer:
													snake_case_		:							Optional[Any]			       =						self._write(file_obj=A__  ,orient=A__  ,lines=A__  ,index=A__  ,**self.to_json_kwargs			)
									else:
											if compression:
													raise NotImplementedError(
													    F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
													    """ was passed. Please provide a local path instead."""			)
											snake_case_		:							int			       =						self._write(
											    file_obj=self.path_or_buf  ,orient=A__  ,lines=A__  ,index=A__  ,**self.to_json_kwargs			)
									return written





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							int			       =						args

									snake_case_		:							Union[str, Any]			       =						query_table(
									    table=self.dataset.data  ,key=slice(A__  ,offset + self.batch_size			)  ,indices=self.dataset._indices  ,)
									snake_case_		:							List[Any]			       =						batch.to_pandas().to_json(
									    path_or_buf=A__  ,orient=A__  ,lines=A__  ,index=A__  ,**A__			)
									if not json_str.endswith("""\n"""			):
											json_str += "\n"
									return json_str.encode(self.encoding			)





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Union[str, Any]  ,**_UpperCamelCase		:Any  ,):
									snake_case_		:							List[str]			       =						0

									if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0  ,len(self.dataset			)  ,self.batch_size			)  ,unit="""ba"""  ,disable=not logging.is_progress_bar_enabled()  ,desc="""Creating json from Arrow format"""  ,):
													snake_case_		:							Optional[int]			       =						self._batch_json((offset, orient, lines, index, to_json_kwargs)			)
													written += file_obj.write(A__			)
									else:
											snake_case_		:							Optional[int]			       =						len(self.dataset			), self.batch_size
											with multiprocessing.Pool(self.num_proc			) as pool:
													for json_str in logging.tqdm(
													    pool.imap(
													        self._batch_json  ,[(offset, orient, lines, index, to_json_kwargs) for offset in range(0  ,A__  ,A__			)]  ,)  ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,unit="""ba"""  ,disable=not logging.is_progress_bar_enabled()  ,desc="""Creating json from Arrow format"""  ,):
															written += file_obj.write(A__			)

									return written



'''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      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[
		    """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      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_       ,      snake_case_		:							Dict			       =						emb.weight.shape
		snake_case_		:							str			       =						nn.Linear(lowerCamelCase_				,      lowerCamelCase_				,      bias=lowerCamelCase_ )
		snake_case_		:							str			       =						emb.weight.data
		return lin_layer





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str]=None ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						{}
		for old_key in state_dict.keys():
				snake_case_		:							Optional[int]			       =						old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								snake_case_		:							Dict			       =						key.replace("""moe_layer.experts.0"""				,      F'''ffn.experts.expert_{expert_idx}''' )
						else:
								snake_case_		:							str			       =						key.replace("""moe_layer.experts."""				,      """ffn.experts.expert_""" )
				if "gate" in key:
						snake_case_		:							List[str]			       =						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_		:							str			       =						key.replace(""".fc1."""				,      """.ffn.fc1.""" )
				if ".encoder_attn." in key:
						snake_case_		:							str			       =						key.replace(""".encoder_attn."""				,      """.cross_attention.""" )
				if "encoder_attn_layer_norm" in key:
						snake_case_		:							List[str]			       =						key.replace("""encoder_attn_layer_norm"""				,      """cross_attention_layer_norm""" )
				if "final_layer_norm" in key:
						snake_case_		:							Dict			       =						key.replace("""final_layer_norm"""				,      """ff_layer_norm""" )
				snake_case_		:							Dict			       =						state_dict[old_key]
		return new_dict





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :str = WEIGHTS_NAME ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[]
		snake_case_		:							Dict			       =						0
		os.makedirs(lowerCamelCase_				,      exist_ok=lowerCamelCase_ )

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

    # Add the metadata
		snake_case_		:							Any			       =						{"""total_size""": total_size}
		snake_case_		:							int			       =						{"""metadata""": metadata, """weight_map""": weight_map}

		with open(os.path.join(lowerCamelCase_				,      lowerCamelCase_ )				,      """w"""				,      encoding="""utf-8""" ) as f:
				snake_case_		:							List[str]			       =						json.dumps(lowerCamelCase_				,      indent=2				,      sort_keys=lowerCamelCase_ ) + """\n"""
				f.write(lowerCamelCase_ )

		return metadata, index


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

							__A	:	Tuple     =     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)
							__A	:	List[str]     =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)