


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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_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(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       emb.weight.shape
					__SCREAMING_SNAKE_CASE		     =       nn.Linear(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		bias=_SCREAMING_SNAKE_CASE     )
					__SCREAMING_SNAKE_CASE		     =       emb.weight.data
					return lin_layer




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_ = WEIGHTS_NAME     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       []
					__SCREAMING_SNAKE_CASE		     =       0
					os.makedirs(_SCREAMING_SNAKE_CASE						,		exist_ok=_SCREAMING_SNAKE_CASE     )

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

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

										# Add the metadata
					__SCREAMING_SNAKE_CASE		     =       {"total_size": total_size}
					__SCREAMING_SNAKE_CASE		     =       {"metadata": metadata, "weight_map": weight_map}

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

					return metadata, index


if __name__ == "__main__":
		a__       :							int        =  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__       :							Union[str, Any]        =  parser.parse_args()
		a__    ,  a__       :							Tuple        =  shard_on_the_fly(
		    args.nllb_moe_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    1_2_8,
		    args.dtype,
		)

		a__       :							Any        =  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)
		a__       :							Tuple        =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
		print('''Done''')
		model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


a__       :							Dict        =  logging.get_logger(__name__)

a__       :							str        =  {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Optional[Any]					      =    "dpt"




	def __init__(				self    :       Tuple    ,    UpperCAmelCase__    :       int=7_6_8    ,    UpperCAmelCase__    :       List[str]=1_2    ,    UpperCAmelCase__    :       Any=1_2    ,    UpperCAmelCase__    :       int=3_0_7_2    ,    UpperCAmelCase__    :       List[str]="gelu"    ,    UpperCAmelCase__    :       Union[str, Any]=0.0    ,    UpperCAmelCase__    :       Any=0.0    ,    UpperCAmelCase__    :       List[Any]=0.02    ,    UpperCAmelCase__    :       List[Any]=1E-12    ,    UpperCAmelCase__    :       List[str]=3_8_4    ,    UpperCAmelCase__    :       Tuple=1_6    ,    UpperCAmelCase__    :       Optional[Any]=3    ,    UpperCAmelCase__    :       str=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Optional[Any]=[2, 5, 8, 1_1]    ,    UpperCAmelCase__    :       Union[str, Any]="project"    ,    UpperCAmelCase__    :       Dict=[4, 2, 1, 0.5]    ,    UpperCAmelCase__    :       Optional[Any]=[9_6, 1_9_2, 3_8_4, 7_6_8]    ,    UpperCAmelCase__    :       List[str]=2_5_6    ,    UpperCAmelCase__    :       Optional[Any]=-1    ,    UpperCAmelCase__    :       Union[str, Any]=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Dict=0.4    ,    UpperCAmelCase__    :       Union[str, Any]=2_5_5    ,    UpperCAmelCase__    :       List[Any]=0.1    ,    UpperCAmelCase__    :       Optional[int]=[1, 1_0_2_4, 2_4, 2_4]    ,    UpperCAmelCase__    :       List[Any]=[0, 1]    ,    UpperCAmelCase__    :       List[str]=None    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			Dict:
						super().__init__(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       hidden_size
						__SCREAMING_SNAKE_CASE		     =       is_hybrid

						if self.is_hybrid:
											if backbone_config is None:
																logger.info("Initializing the config with a `BiT` backbone."     )
																__SCREAMING_SNAKE_CASE		     =       {
																    "global_padding": "same",
																    "layer_type": "bottleneck",
																    "depths": [3, 4, 9],
																    "out_features": ["stage1", "stage2", "stage3"],
																    "embedding_dynamic_padding": True,
																}
																__SCREAMING_SNAKE_CASE		     =       BitConfig(**UpperCAmelCase__     )
											elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
																logger.info("Initializing the config with a `BiT` backbone."     )
																__SCREAMING_SNAKE_CASE		     =       BitConfig(**UpperCAmelCase__     )
											elif isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       backbone_config
											else:
																raise ValueError(
																    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""     )

											__SCREAMING_SNAKE_CASE		     =       backbone_featmap_shape
											__SCREAMING_SNAKE_CASE		     =       neck_ignore_stages

											if readout_type != "project":
																raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."     )
						else:
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       []

						__SCREAMING_SNAKE_CASE		     =       num_hidden_layers
						__SCREAMING_SNAKE_CASE		     =       num_attention_heads
						__SCREAMING_SNAKE_CASE		     =       intermediate_size
						__SCREAMING_SNAKE_CASE		     =       hidden_act
						__SCREAMING_SNAKE_CASE		     =       hidden_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       initializer_range
						__SCREAMING_SNAKE_CASE		     =       layer_norm_eps
						__SCREAMING_SNAKE_CASE		     =       image_size
						__SCREAMING_SNAKE_CASE		     =       patch_size
						__SCREAMING_SNAKE_CASE		     =       num_channels
						__SCREAMING_SNAKE_CASE		     =       qkv_bias
						__SCREAMING_SNAKE_CASE		     =       backbone_out_indices
						if readout_type not in ["ignore", "add", "project"]:
											raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"     )
						__SCREAMING_SNAKE_CASE		     =       readout_type
						__SCREAMING_SNAKE_CASE		     =       reassemble_factors
						__SCREAMING_SNAKE_CASE		     =       neck_hidden_sizes
						__SCREAMING_SNAKE_CASE		     =       fusion_hidden_size
						__SCREAMING_SNAKE_CASE		     =       head_in_index
						__SCREAMING_SNAKE_CASE		     =       use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						__SCREAMING_SNAKE_CASE		     =       use_auxiliary_head
						__SCREAMING_SNAKE_CASE		     =       auxiliary_loss_weight
						__SCREAMING_SNAKE_CASE		     =       semantic_loss_ignore_index
						__SCREAMING_SNAKE_CASE		     =       semantic_classifier_dropout




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       copy.deepcopy(self.__dict__     )

						if output["backbone_config"] is not None:
											__SCREAMING_SNAKE_CASE		     =       self.backbone_config.to_dict()

						__SCREAMING_SNAKE_CASE		     =       self.__class__.model_type
						return output
