





'''simple docstring'''






def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


			def update_area_of_max_square(_lowerCamelCase    ,	_lowerCamelCase ) -> int:
						# BASE CASE
						if row >= rows or col >= cols:
									return 0

						__snake_case					:      Dict							      =     update_area_of_max_square(_lowerCamelCase    ,	col + 1 )
						__snake_case					:      Optional[int]							      =     update_area_of_max_square(row + 1    ,	col + 1 )
						__snake_case					:      Any							      =     update_area_of_max_square(row + 1    ,	_lowerCamelCase )

						if mat[row][col]:
									__snake_case					:      Optional[Any]							      =     1 + min([right, diagonal, down] )
									__snake_case					:      int							      =     max(largest_square_area[0]    ,	_lowerCamelCase )
									return sub_problem_sol
						else:
									return 0

			__snake_case					:      List[Any]							      =     [0]
			update_area_of_max_square(0    ,	0 )
			return largest_square_area[0]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


			def update_area_of_max_square_using_dp_array(
			    _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) -> int:
						if row >= rows or col >= cols:
									return 0
						if dp_array[row][col] != -1:
									return dp_array[row][col]

						__snake_case					:      Tuple							      =     update_area_of_max_square_using_dp_array(_lowerCamelCase    ,	col + 1    ,	_lowerCamelCase )
						__snake_case					:      Dict							      =     update_area_of_max_square_using_dp_array(row + 1    ,	col + 1    ,	_lowerCamelCase )
						__snake_case					:      Union[str, Any]							      =     update_area_of_max_square_using_dp_array(row + 1    ,	_lowerCamelCase    ,	_lowerCamelCase )

						if mat[row][col]:
									__snake_case					:      List[Any]							      =     1 + min([right, diagonal, down] )
									__snake_case					:      List[str]							      =     max(largest_square_area[0]    ,	_lowerCamelCase )
									__snake_case					:      List[str]							      =     sub_problem_sol
									return sub_problem_sol
						else:
									return 0

			__snake_case					:      Union[str, Any]							      =     [0]
			__snake_case					:      Union[str, Any]							      =     [[-1] * cols for _ in range(_lowerCamelCase )]
			update_area_of_max_square_using_dp_array(0    ,	0    ,	_lowerCamelCase )

			return largest_square_area[0]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     [[0] * (cols + 1) for _ in range(rows + 1 )]
			__snake_case					:      str							      =     0
			for row in range(rows - 1    ,	-1    ,	-1 ):
						for col in range(cols - 1    ,	-1    ,	-1 ):
									__snake_case					:      List[Any]							      =     dp_array[row][col + 1]
									__snake_case					:      str							      =     dp_array[row + 1][col + 1]
									__snake_case					:      List[str]							      =     dp_array[row + 1][col]

									if mat[row][col] == 1:
												__snake_case					:      List[str]							      =     1 + min(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
												__snake_case					:      str							      =     max(dp_array[row][col]    ,	_lowerCamelCase )
									else:
												__snake_case					:      Optional[Any]							      =     0

			return largest_square_area







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


			__snake_case					:      Any							      =     [0] * (cols + 1)
			__snake_case					:      Union[str, Any]							      =     [0] * (cols + 1)
			__snake_case					:      List[Any]							      =     0
			for row in range(rows - 1    ,	-1    ,	-1 ):
						for col in range(cols - 1    ,	-1    ,	-1 ):
									__snake_case					:      int							      =     current_row[col + 1]
									__snake_case					:      Dict							      =     next_row[col + 1]
									__snake_case					:      int							      =     next_row[col]

									if mat[row][col] == 1:
												__snake_case					:      Union[str, Any]							      =     1 + min(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
												__snake_case					:      Union[str, Any]							      =     max(current_row[col]    ,	_lowerCamelCase )
									else:
												__snake_case					:      List[Any]							      =     0
						__snake_case					:      Optional[Any]							      =     current_row

			return largest_square_area


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))









'''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 ) ->				List[Any]:



			"""simple docstring"""


			__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 ) ->				List[str]:



			"""simple docstring"""


			__snake_case      ,     __snake_case					:      Dict							      =     emb.weight.shape
			__snake_case					:      Optional[int]							      =     nn.Linear(_lowerCamelCase    ,	_lowerCamelCase    ,	bias=_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     emb.weight.data
			return lin_layer







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=None ) ->				Union[str, Any]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = WEIGHTS_NAME ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     []
			__snake_case					:      Dict							      =     0
			os.makedirs(_lowerCamelCase    ,	exist_ok=_lowerCamelCase )

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

    # Add the metadata
			__snake_case					:      Optional[Any]							      =     {"""total_size""": total_size}
			__snake_case					:      int							      =     {"""metadata""": metadata, """weight_map""": weight_map}

			with open(os.path.join(_lowerCamelCase    ,	_lowerCamelCase )    ,	"""w"""    ,	encoding="""utf-8""" ) as f:
						__snake_case					:      Union[str, Any]							      =     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)


