





'''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 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Union[str, 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(lowerCamelCase_			,			lowerCamelCase_	)



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



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



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_=None	):


		"""simple docstring"""



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



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


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[str]		=			[]
		SCREAMING_SNAKE_CASE :				List[Any]		=			0
		os.makedirs(lowerCamelCase_			,			exist_ok=lowerCamelCase_	)

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

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

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

		return metadata, index


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

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










'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class      UpperCamelCase__					:




					"""simple docstring"""


					@property
					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							return self.get_dummy_input()


					@property
					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							if self.block_type == "down":
									return (4, 32, 16, 16)
							elif self.block_type == "mid":
									return (4, 32, 32, 32)
							elif self.block_type == "up":
									return (4, 32, 64, 64)

							raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''						)


					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			Dict=True      ,						lowerCamelCase_				:			List[Any]=False      ,						lowerCamelCase_				:			str=False      ,						lowerCamelCase_				:			Optional[int]=False      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			4
							SCREAMING_SNAKE_CASE :				Any		=			32
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			(32, 32)

							SCREAMING_SNAKE_CASE :				int		=			torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.device(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			(batch_size, num_channels) + sizes
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			randn_tensor(lowerCamelCase_      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{"""hidden_states""": hidden_states}

							if include_temb:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1_28
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			randn_tensor((batch_size, temb_channels)      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)

							if include_res_hidden_states_tuple:
									SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(1						)
									SCREAMING_SNAKE_CASE :				int		=			(randn_tensor(lowerCamelCase_      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						),)

							if include_encoder_hidden_states:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			floats_tensor((batch_size, 32, 32)						).to(lowerCamelCase_						)

							if include_skip_sample:
									SCREAMING_SNAKE_CASE :				str		=			randn_tensor(((batch_size, 3) + sizes)      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)

							return dummy_input


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			{
							    """in_channels""": 32,
							    """out_channels""": 32,
							    """temb_channels""": 1_28,
							}
							if self.block_type == "up":
									SCREAMING_SNAKE_CASE :				List[str]		=			32

							if self.block_type == "mid":
									init_dict.pop("""out_channels"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_input
							return init_dict, inputs_dict


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.prepare_init_args_and_inputs_for_common()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.block_class(**lowerCamelCase_						)
							unet_block.to(lowerCamelCase_						)
							unet_block.eval()

							with torch.no_grad():
									SCREAMING_SNAKE_CASE :				List[str]		=			unet_block(**lowerCamelCase_						)

							if isinstance(lowerCamelCase_      ,						lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Dict		=			output[0]

							self.assertEqual(output.shape      ,						self.output_shape						)

							SCREAMING_SNAKE_CASE :				Dict		=			output[0, -1, -3:, -3:]
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.tensor(lowerCamelCase_						).to(lowerCamelCase_						)
							assert torch_all_close(output_slice.flatten()      ,						lowerCamelCase_      ,						atol=5e-3						)



					@unittest.skipIf(torch_device == """mps"""      ,						"""Training is not supported in mps"""						)
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.prepare_init_args_and_inputs_for_common()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.block_class(**lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.train()
							SCREAMING_SNAKE_CASE :				Tuple		=			model(**lowerCamelCase_						)

							if isinstance(lowerCamelCase_      ,						lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			output[0]

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.device(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			randn_tensor(output.shape      ,						device=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.nn.functional.mse_loss(lowerCamelCase_      ,						lowerCamelCase_						)
							loss.backward()



