






'''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  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[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      )      ->       List[Any]:
							lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       emb.weight.shape
							lowerCamelCase__					:	Optional[int]         =       nn.Linear(UpperCamelCase				,			UpperCamelCase				,			bias=UpperCamelCase      )
							lowerCamelCase__					:	List[Any]         =       emb.weight.data
							return lin_layer





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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase = WEIGHTS_NAME      )      ->       List[str]:
							lowerCamelCase__					:	Optional[Any]         =       []
							lowerCamelCase__					:	Optional[int]         =       0
							os.makedirs(UpperCamelCase				,			exist_ok=UpperCamelCase      )

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

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

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

							return metadata, index


if __name__ == "__main__":
			_A	:			Any         =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	:			Dict         =parser.parse_args()
			_A     ,      _A	:			int         =shard_on_the_fly(
			    args.nllb_moe_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    128,
			    args.dtype,
			)

			_A	:			Union[str, 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)
			_A	:			Dict         =NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print('''Done''')
			model.save_pretrained(args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__       :      List[Any]  =   {'processing_layoutxlm': ['LayoutXLMProcessor']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   ['LayoutXLMTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      Dict  =   ['LayoutXLMTokenizerFast']

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       SCREAMING_SNAKE_CASE__       :      str  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






