



import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


lowerCamelCase		       =      datasets.utils.logging.get_logger(__name__)

lowerCamelCase		       =      ["""names""", """prefix"""]
lowerCamelCase		       =      ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
lowerCamelCase		       =      ["""encoding_errors""", """on_bad_lines"""]
lowerCamelCase		       =      ["""date_format"""]


@dataclass
class 				_a						(       datasets.BuilderConfig			):





							'''simple docstring'''

							A				:str =       ","
							A				:Optional[str] =       None
							A				:Optional[Union[int, List[int], str]] =       "infer"
							A				:Optional[List[str]] =       None
							A				:Optional[List[str]] =       None
							A				:Optional[Union[int, str, List[int], List[str]]] =       None
							A				:Optional[Union[List[int], List[str]]] =       None
							A				:Optional[str] =       None
							A				:bool =       True
							A				:Optional[Literal["c", "python", "pyarrow"]] =       None
							A				:Dict[Union[int, str], Callable[[Any], Any]] =       None
							A				:Optional[list] =       None
							A				:Optional[list] =       None
							A				:bool =       False
							A				:Optional[Union[int, List[int]]] =       None
							A				:Optional[int] =       None
							A				:Optional[Union[str, List[str]]] =       None
							A				:bool =       True
							A				:bool =       True
							A				:bool =       False
							A				:bool =       True
							A				:Optional[str] =       None
							A				:str =       "."
							A				:Optional[str] =       None
							A				:str =       '"'
							A				:int =       0
							A				:Optional[str] =       None
							A				:Optional[str] =       None
							A				:Optional[str] =       None
							A				:Optional[str] =       None
							A				:bool =       True
							A				:bool =       True
							A				:int =       0
							A				:bool =       True
							A				:bool =       False
							A				:Optional[str] =       None
							A				:int =       1_00_00
							A				:Optional[datasets.Features] =       None
							A				:Optional[str] =       "strict"
							A				:Literal["error", "warn", "skip"] =       "error"
							A				:Optional[str] =       None



							def 		_A	(							self				):







											"""simple docstring"""



											if self.delimiter is not None:
															a__	:							str      						= self.delimiter
											if self.column_names is not None:
															a__	:							Optional[Any]      						= self.column_names




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= {
											    "sep": self.sep,
											    "header": self.header,
											    "names": self.names,
											    "index_col": self.index_col,
											    "usecols": self.usecols,
											    "prefix": self.prefix,
											    "mangle_dupe_cols": self.mangle_dupe_cols,
											    "engine": self.engine,
											    "converters": self.converters,
											    "true_values": self.true_values,
											    "false_values": self.false_values,
											    "skipinitialspace": self.skipinitialspace,
											    "skiprows": self.skiprows,
											    "nrows": self.nrows,
											    "na_values": self.na_values,
											    "keep_default_na": self.keep_default_na,
											    "na_filter": self.na_filter,
											    "verbose": self.verbose,
											    "skip_blank_lines": self.skip_blank_lines,
											    "thousands": self.thousands,
											    "decimal": self.decimal,
											    "lineterminator": self.lineterminator,
											    "quotechar": self.quotechar,
											    "quoting": self.quoting,
											    "escapechar": self.escapechar,
											    "comment": self.comment,
											    "encoding": self.encoding,
											    "dialect": self.dialect,
											    "error_bad_lines": self.error_bad_lines,
											    "warn_bad_lines": self.warn_bad_lines,
											    "skipfooter": self.skipfooter,
											    "doublequote": self.doublequote,
											    "memory_map": self.memory_map,
											    "float_precision": self.float_precision,
											    "chunksize": self.chunksize,
											    "encoding_errors": self.encoding_errors,
											    "on_bad_lines": self.on_bad_lines,
											    "date_format": self.date_format,
											}

											# some kwargs must not be passed if they don't have a default value
											# some others are deprecated and we can also not pass them if they are the default value
											for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
															if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,					__UpperCAmelCase				):
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 2):
															for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
											if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
															for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																			del pd_read_csv_kwargs[pd_read_csv_parameter]

											return pd_read_csv_kwargs





class 				_a						(       datasets.ArrowBasedBuilder			):





							'''simple docstring'''

							A				:List[str] =       CsvConfig



							def 		_A	(							self				):







											"""simple docstring"""



											return datasets.DatasetInfo(features=self.config.features				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if not self.config.data_files:
															raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'				)
											a__	:							List[str]      						= dl_manager.download_and_extract(self.config.data_files				)
											if isinstance(__UpperCAmelCase ,					(str, list, tuple)				):
															a__	:							List[str]      						= data_files
															if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				):
																			a__	:							Optional[int]      						= [files]
															a__	:							Optional[int]      						= [dl_manager.iter_files(__UpperCAmelCase				) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,					gen_kwargs={"files": files}				)]
											a__	:							Any      						= []
											for split_name, files in data_files.items():
															if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				):
																			a__	:							Union[str, Any]      						= [files]
															a__	:							List[str]      						= [dl_manager.iter_files(__UpperCAmelCase				) for file in files]
															splits.append(datasets.SplitGenerator(name=__UpperCAmelCase ,					gen_kwargs={"files": files}				)				)
											return splits



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if self.config.features is not None:
															a__	:							Optional[Any]      						= self.config.features.arrow_schema
															if all(not require_storage_cast(__UpperCAmelCase				) for feature in self.config.features.values()				):
																			# cheaper cast
																			a__	:							Optional[Any]      						= pa.Table.from_arrays([pa_table[field.name] for field in schema] ,					schema=__UpperCAmelCase				)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			a__	:							List[Any]      						= table_cast(__UpperCAmelCase ,					__UpperCAmelCase				)
											return pa_table




							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[str]      						= self.config.features.arrow_schema if self.config.features else None
											# dtype allows reading an int column as str
											a__	:							Dict      						= (
											    {
											        name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase				) else object
											        for name, dtype, feature in zip(schema.names ,					schema.types ,					self.config.features.values()				)
											    }
											    if schema is not None
											    else None
											)
											for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase				)				):
															a__	:							List[Any]      						= pd.read_csv(__UpperCAmelCase ,					iterator=__UpperCAmelCase ,					dtype=__UpperCAmelCase ,					**self.config.pd_read_csv_kwargs				)
															try:
																			for batch_idx, df in enumerate(__UpperCAmelCase				):
																							a__	:							str      						= pa.Table.from_pandas(__UpperCAmelCase				)
																							# Uncomment for debugging (will print the Arrow table size and elements)
																							# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																							# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																							yield (file_idx, batch_idx), self._cast_table(__UpperCAmelCase				)
															except ValueError as e:
																			logger.error(f'Failed to read file \'{file}\' with error {type(__UpperCAmelCase				)}: {e}'				)
																			raise







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(      __UpperCamelCase )	->						List[Any]:
				a__	:							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(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Optional[Any]:
				a__						,   a__	:							Optional[int]      						= emb.weight.shape
				a__	:							Union[str, Any]      						= nn.Linear(__UpperCamelCase	,				__UpperCamelCase	,				bias=__UpperCamelCase )
				a__	:							str      						= emb.weight.data
				return lin_layer






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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Dict:
				a__	:							Any      						= []
				a__	:							List[str]      						= 0
				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )

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

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

    # Add the metadata
				a__	:							Optional[int]      						= {"total_size": total_size}
				a__	:							Optional[Any]      						= {"metadata": metadata, "weight_map": weight_map}

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

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      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.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						lowerCamelCase       ,   lowerCamelCase		       =      shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_28,
						    args.dtype,
						)

						lowerCamelCase		       =      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)
						lowerCamelCase		       =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("""Done""")
						model.save_pretrained(args.pytorch_dump_folder_path)


