





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__       (       UpperCamelCase__     :    str  )     ->	Optional[int]:



					'''simple docstring'''




					_snake_case													=			[
					    '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   lowerCamelCase__       (       UpperCamelCase__     :    Tuple  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			emb.weight.shape
					_snake_case													=			nn.Linear(UpperCamelCase__    ,      UpperCamelCase__    ,      bias=UpperCamelCase__  )
					_snake_case													=			emb.weight.data
					return lin_layer





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Any=None  )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			{}
					for old_key in state_dict.keys():
										_snake_case													=			old_key
										if "moe_layer.experts." in key:
															if expert_idx is not None:
																				_snake_case													=			key.replace('moe_layer.experts.0'    ,      F'''ffn.experts.expert_{expert_idx}'''  )
															else:
																				_snake_case													=			key.replace('moe_layer.experts.'    ,      'ffn.experts.expert_'  )
										if "gate" in key:
															_snake_case													=			key.replace('.moe_layer.gate.wg'    ,      '.ffn.router.classifier'  )
										if "fc2" and "experts" not in key:
															_snake_case													=			key.replace('.fc2.'    ,      '.ffn.fc2.'  )
										if "fc1" and "experts" not in key:
															_snake_case													=			key.replace('.fc1.'    ,      '.ffn.fc1.'  )
										if ".encoder_attn." in key:
															_snake_case													=			key.replace('.encoder_attn.'    ,      '.cross_attention.'  )
										if "encoder_attn_layer_norm" in key:
															_snake_case													=			key.replace('encoder_attn_layer_norm'    ,      'cross_attention_layer_norm'  )
										if "final_layer_norm" in key:
															_snake_case													=			key.replace('final_layer_norm'    ,      'ff_layer_norm'  )
										_snake_case													=			state_dict[old_key]
					return new_dict





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    str = WEIGHTS_NAME  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case													=			[]
					_snake_case													=			0
					os.makedirs(UpperCamelCase__    ,      exist_ok=UpperCamelCase__  )

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

    # Add the metadata
					_snake_case													=			{'total_size': total_size}
					_snake_case													=			{'metadata': metadata, 'weight_map': weight_map}

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

					return metadata, index


if __name__ == "__main__":
					UpperCAmelCase_ 							=       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_ 							=       parser.parse_args()
					UpperCAmelCase_							, UpperCAmelCase_ 							=       shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    128,
					    args.dtype,
					)

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










import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_ 							=       [
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict=True  )     ->	Dict:



					'''simple docstring'''




					if with_config:
										return [
										    {
										        "testcase_name": d["dataset"] + "/" + d["config_name"],
										        "dataset": d["dataset"],
										        "config_name": d["config_name"],
										    }
										    for d in DATASETS_ON_HF_GCP
										]
					else:
										return [
										    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
										]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowerCamelCase   )   )
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		None
					lowerCAmelCase_							=		None




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										with TemporaryDirectory() as tmp_dir:
															_snake_case													=			dataset_module_factory(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)

															_snake_case													=			import_main_class(dataset_module.module_path			,  dataset=lowerCAmelCase_					)

															_snake_case													=			builder_cls(
															    cache_dir=lowerCAmelCase_			,  config_name=lowerCAmelCase_			,  hash=dataset_module.hash			,  )

															_snake_case													=			'/'.join(
															    [
															        HF_GCP_BASE_URL,
															        builder_instance._relative_data_dir(with_hash=lowerCAmelCase_					).replace(os.sep			,  '/'					),
															        config.DATASET_INFO_FILENAME,
															    ]					)
															_snake_case													=			cached_path(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)
															self.assertTrue(os.path.exists(lowerCAmelCase_					)					)





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Any  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      config_name='20220301.frr'    ,      hash=dataset_module.hash    ,      )
					# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
					_snake_case													=			None
					builder_instance.download_and_prepare()
					_snake_case													=			builder_instance.as_dataset()
					assert ds





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path    ,      dataset=UpperCamelCase__  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      config_name='20220301.frr'    ,      hash=dataset_module.hash    ,      )
					_snake_case													=			builder_instance.as_streaming_dataset()
					assert ds
					assert isinstance(UpperCamelCase__    ,      UpperCamelCase__  )
					assert "train" in ds
					assert isinstance(ds['train']    ,      UpperCamelCase__  )
					assert next(iter(ds['train']  )  )



