


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_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= [
		    '''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(_a     ,       _a	)






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= emb.weight.shape
		lowerCAmelCase__       :      List[str]   					= nn.Linear(_a     ,       _a     ,       bias=_a	)
		lowerCAmelCase__       :      str   					= emb.weight.data
		return lin_layer






def 					lowerCamelCase_   (	_a     ,       _a=None	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a = WEIGHTS_NAME	):




		"""simple docstring"""






		lowerCAmelCase__       :      Tuple   					= []
		lowerCAmelCase__       :      int   					= 0
		os.makedirs(_a     ,       exist_ok=_a	)

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

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

    # Add the metadata
		lowerCAmelCase__       :      List[str]   					= {'''total_size''': total_size}
		lowerCAmelCase__       :      Tuple   					= {'''metadata''': metadata, '''weight_map''': weight_map}

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

		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,
		    128,
		    args.dtype,
		)

		lowerCamelCase						=		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)
		lowerCamelCase						=		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_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase						=		{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		['''BeitFeatureExtractor''']
		lowerCamelCase						=		['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase						=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




