


"""simple docstring"""




def        lowercase     (SCREAMING_SNAKE_CASE_						:							list[list]    )  ->		List[Any]:
						SCREAMING_SNAKE_CASE						=   current_set.copy()
						for row_index, row in enumerate(__lowerCAmelCase    ):
												SCREAMING_SNAKE_CASE						=   row[0]
												for column_index, column in enumerate(__lowerCAmelCase    ):
																		if magnitude == 0:
																								SCREAMING_SNAKE_CASE						=   column
																								continue
																		SCREAMING_SNAKE_CASE						=   column / magnitude
    # Subtract to cancel term
						SCREAMING_SNAKE_CASE						=   current_set[0]
						SCREAMING_SNAKE_CASE						=   [first_row]
						SCREAMING_SNAKE_CASE						=   current_set[1::]
						for row in current_set:
												SCREAMING_SNAKE_CASE						=   []
												# If first term is 0, it is already in form we want, so we preserve it
												if row[0] == 0:
																		final_set.append(__lowerCAmelCase    )
																		continue
												for column_index in range(len(__lowerCAmelCase    )    ):
																		temp_row.append(first_row[column_index] - row[column_index]    )
												final_set.append(__lowerCAmelCase    )
						# Create next recursion iteration set
						if len(final_set[0]    ) != 3:
												SCREAMING_SNAKE_CASE						=   final_set[0]
												SCREAMING_SNAKE_CASE						=   []
												SCREAMING_SNAKE_CASE						=   []
												for row in final_set[1::]:
																		current_first_column.append(row[0]    )
																		next_iteration.append(row[1::]    )
												SCREAMING_SNAKE_CASE						=   simplify(__lowerCAmelCase    )
												for i in range(len(__lowerCAmelCase    )    ):
																		resultant[i].insert(0					,     current_first_column[i]    )
												resultant.insert(0					,     __lowerCAmelCase    )
												SCREAMING_SNAKE_CASE						=   resultant
						return final_set


def        lowercase     (SCREAMING_SNAKE_CASE_						:							list[list]    )  ->		Optional[int]:


						if len(__lowerCAmelCase    ) == 0:
												raise IndexError('solve_simultaneous() requires n lists of length n+1'    )
						SCREAMING_SNAKE_CASE						=   len(__lowerCAmelCase    ) + 1
						if any(len(__lowerCAmelCase    ) != _length for item in equations    ):
												raise IndexError('solve_simultaneous() requires n lists of length n+1'    )
						for row in equations:
												if any(not isinstance(__lowerCAmelCase					,     (int, float)    ) for column in row    ):
																		raise ValueError('solve_simultaneous() requires lists of integers'    )
						if len(__lowerCAmelCase    ) == 1:
												return [equations[0][-1] / equations[0][0]]
						SCREAMING_SNAKE_CASE						=   equations.copy()
						if any(0 in row for row in data_set    ):
												SCREAMING_SNAKE_CASE						=   data_set.copy()
												SCREAMING_SNAKE_CASE						=   []
												for row_index, row in enumerate(__lowerCAmelCase    ):
																		if 0 not in row:
																								SCREAMING_SNAKE_CASE						=   data_set.pop(__lowerCAmelCase    )
																								break
												if not full_row:
																		raise ValueError('solve_simultaneous() requires at least 1 full equation'    )
												data_set.insert(0					,     __lowerCAmelCase    )
						SCREAMING_SNAKE_CASE						=   data_set.copy()
						SCREAMING_SNAKE_CASE						=   simplify(__lowerCAmelCase    )
						SCREAMING_SNAKE_CASE						=   simplified[::-1]
						SCREAMING_SNAKE_CASE						=   []
						for row in simplified:
												SCREAMING_SNAKE_CASE						=   row[-1]
												if not solutions:
																		if row[-2] == 0:
																								solutions.append(0    )
																								continue
																		solutions.append(current_solution / row[-2]    )
																		continue
												SCREAMING_SNAKE_CASE						=   row.copy()[: len(__lowerCAmelCase    ) - 1 :]
												while temp_row[0] == 0:
																		temp_row.pop(0    )
												if len(__lowerCAmelCase    ) == 0:
																		solutions.append(0    )
																		continue
												SCREAMING_SNAKE_CASE						=   temp_row[1::]
												SCREAMING_SNAKE_CASE						=   temp_row[::-1]
												for column_index, column in enumerate(__lowerCAmelCase    ):
																		current_solution -= column * solutions[column_index]
												solutions.append(__lowerCAmelCase    )
						SCREAMING_SNAKE_CASE						=   []
						for item in solutions:
												final.append(float(round(__lowerCAmelCase					,     5    )    )    )
						return final[::-1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	__UpperCamelCase		= [
	    [2, 1, 1, 1, 1, 4],
	    [1, 2, 1, 1, 1, 5],
	    [1, 1, 2, 1, 1, 6],
	    [1, 1, 1, 2, 1, 7],
	    [1, 1, 1, 1, 2, 8],
	]
	print(solve_simultaneous(eq))
	print(solve_simultaneous([[4, 2]]))




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A  =	logging.get_logger(__name__)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			# pop unnecessary weights
			lowerCAmelCase_         =      [
			    "decoder.version",
			    "decoder.output_projection.weight",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "decoder.project_in_dim.weight": "decoder.project_in.weight",
			    "decoder.project_out_dim.weight": "decoder.project_out.weight",
			    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
			    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCAmelCase_         =      sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      list(sd.keys()							)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCAmelCase_         =      sd[key]
									# We split QKV in separate Q,K,V

									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".q_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".k_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".v_proj."							)

									lowerCAmelCase_         =      value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      torch.split(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

									lowerCAmelCase_         =      q
									lowerCAmelCase_         =      k
									lowerCAmelCase_         =      v
									del sd[key]

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

			if config is not None:
						lowerCAmelCase_         =      OPTConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      OPTConfig()

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

			# Check results
			Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
			model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fairseq_path",
			    type=str,
			    help=(
			        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
			        " https://huggingface.co/models?other=opt_metasq"
			    ),
			)
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
			_A  =	parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



