

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__	(  lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    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(lowerCamelCase__     ,			lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			emb.weight.shape
	__lowerCamelCase    :    Any 	=			nn.Linear(lowerCamelCase__     ,			lowerCamelCase__     ,			bias=lowerCamelCase__      )
	__lowerCamelCase    :    Optional[Any] 	=			emb.weight.data
	return lin_layer






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None      )  ->					List[str]:
	__lowerCamelCase    :    Tuple 	=			{}
	for old_key in state_dict.keys():
		__lowerCamelCase    :    Optional[Any] 	=			old_key
		if "moe_layer.experts." in key:
			if expert_idx is not None:
				__lowerCamelCase    :    Optional[int] 	=			key.replace('moe_layer.experts.0'     ,			F"ffn.experts.expert_{expert_idx}"      )
			else:
				__lowerCamelCase    :    Union[str, Any] 	=			key.replace('moe_layer.experts.'     ,			'ffn.experts.expert_'      )
		if "gate" in key:
			__lowerCamelCase    :    int 	=			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[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    :    Optional[int] 	=			key.replace('final_layer_norm'     ,			'ff_layer_norm'      )
		__lowerCamelCase    :    Union[str, Any] 	=			state_dict[old_key]
	return new_dict






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = WEIGHTS_NAME      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Union[str, Any] 	=			[]
	__lowerCamelCase    :    List[str] 	=			0
	os.makedirs(lowerCamelCase__     ,			exist_ok=lowerCamelCase__      )

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

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

	with open(os.path.join(lowerCamelCase__     ,			lowerCamelCase__      )     ,			'w'     ,			encoding='utf-8'      ) as f:
		__lowerCamelCase    :    List[Any] 	=			json.dumps(lowerCamelCase__     ,			indent=2     ,			sort_keys=lowerCamelCase__      ) + '\n'
		f.write(lowerCamelCase__      )

	return metadata, index


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

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







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


a		=[
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	__lowerCamelCase    :    int 	=			True
	while ask_again:
		__lowerCamelCase    :    Dict 	=			input(lowerCamelCase__      )
		try:
			if default is not None and len(lowerCamelCase__      ) == 0:
				return default
			return convert_value(lowerCamelCase__      ) if convert_value is not None else result
		except Exception:
			if error_message is not None:
				print(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=[]     ,			lowerCamelCase__=None     ,			lowerCamelCase__=0      )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			BulletMenu(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			menu.run(default_choice=lowerCamelCase__      )
	return convert_value(lowerCamelCase__      ) if convert_value is not None else result






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			int(lowerCamelCase__      )
	return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return DynamoBackend(DYNAMO_BACKENDS[value]      ).value






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	return {"yes": True, "no": False}[value.lower()]






class 	A_		(	argparse.RawDescriptionHelpFormatter    ):
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    int 	=			super()._format_usage(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			usage.replace('<command> [<args>] '       ,'')
								return usage




