



"""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      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]			=[
			    '''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      snake_case__   (		__lowerCamelCase				:    Dict	):
			"""simple docstring"""
			lowerCamelCase__	,   lowerCamelCase__		:		Dict			=emb.weight.shape
			lowerCamelCase__		:		Union[str, Any]			=nn.Linear(__lowerCamelCase							,		__lowerCamelCase							,		bias=__lowerCamelCase	)
			lowerCamelCase__		:		Any			=emb.weight.data
			return lin_layer






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Union[str, Any]=None	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			={}
			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__		:		List[str]			=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__		:		int			=key.replace('''.moe_layer.gate.wg'''							,		'''.ffn.router.classifier'''	)
						if "fc2" and "experts" not in key:
									lowerCamelCase__		:		Tuple			=key.replace('''.fc2.'''							,		'''.ffn.fc2.'''	)
						if "fc1" and "experts" not in key:
									lowerCamelCase__		:		Tuple			=key.replace('''.fc1.'''							,		'''.ffn.fc1.'''	)
						if ".encoder_attn." in key:
									lowerCamelCase__		:		Optional[Any]			=key.replace('''.encoder_attn.'''							,		'''.cross_attention.'''	)
						if "encoder_attn_layer_norm" in key:
									lowerCamelCase__		:		Tuple			=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__		:		Optional[int]			=state_dict[old_key]
			return new_dict






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    str = WEIGHTS_NAME	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]			=[]
			lowerCamelCase__		:		Dict			=0
			os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase	)

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

    # Add the metadata
			lowerCamelCase__		:		Optional[int]			={'''total_size''': total_size}
			lowerCamelCase__		:		int			={'''metadata''': metadata, '''weight_map''': weight_map}

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

			return metadata, index


if __name__ == "__main__":
		_lowercase : List[str]      						=						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.",
		)
		_lowercase : int      						=						parser.parse_args()
		_lowercase						,	_lowercase : List[str]      						=						shard_on_the_fly(
		    args.nllb_moe_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    1_2_8,
		    args.dtype,
		)

		_lowercase : str      						=						NllbMoeConfig.from_pretrained(
		    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
		)
		config.save_pretrained(args.pytorch_dump_folder_path)
		_lowercase : Optional[Any]      						=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
		print("Done")
		model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : int      						=						{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		str			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Optional[Any]			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		int			=hidden_act
								lowerCamelCase__		:		Tuple			=hidden_dropout_prob
								lowerCamelCase__		:		Union[str, Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		int			=type_vocab_size
								lowerCamelCase__		:		Tuple			=initializer_range
								lowerCamelCase__		:		Tuple			=relative_attention
								lowerCamelCase__		:		Optional[Any]			=max_relative_positions
								lowerCamelCase__		:		List[Any]			=pad_token_id
								lowerCamelCase__		:		int			=position_biased_input

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Any			={0: '''batch''', 1: '''sequence'''}
								if self._config.type_vocab_size > 0:
											return OrderedDict(
											    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]     )
								else:
											return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]     )



					@property
					def 		snake_case       (       self							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional["TensorType"] = None,  lowerCamelCase							:  int = 3,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  "PreTrainedTokenizerBase" = None,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[Any]			=super().generate_dummy_inputs(preprocessor=lowerCamelCase,  framework=lowerCamelCase     )
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
								return dummy_inputs




