





import argparse
from pathlib import Path

import torch

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


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





def   lowerCamelCase__       (       UpperCamelCase__     :    List[str]  )     ->	Any:



					'''simple docstring'''




					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )
					if "model" in sd.keys():
										_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']

					# pop unnecessary weights
					_snake_case													=			[
					    'decoder.version',
					    'decoder.output_projection.weight',
					]
					for key in keys_to_delete:
										if key in sd:
															sd.pop(UpperCamelCase__  )

					_snake_case													=			{
					    '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:
															_snake_case													=			sd.pop(UpperCamelCase__  )

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

															_snake_case													=			key.replace('.qkv_proj.'    ,      '.q_proj.'  )
															_snake_case													=			key.replace('.qkv_proj.'    ,      '.k_proj.'  )
															_snake_case													=			key.replace('.qkv_proj.'    ,      '.v_proj.'  )

															_snake_case													=			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
															_snake_case							,       _snake_case							,       _snake_case													=			torch.split(UpperCamelCase__    ,      depth // 3    ,      dim=0  )

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

					return sd





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



					'''simple docstring'''




					_snake_case													=			load_checkpoint(UpperCamelCase__  )

					if config is not None:
										_snake_case													=			OPTConfig.from_pretrained(UpperCamelCase__  )
					else:
										_snake_case													=			OPTConfig()

					_snake_case													=			OPTModel(UpperCamelCase__  ).half().eval()
					model.load_state_dict(UpperCamelCase__  )

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


if __name__ == "__main__":
					UpperCAmelCase_ 							=       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.""")
					UpperCAmelCase_ 							=       parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








import random





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    float    ,      UpperCamelCase__     :    bool = False  )     ->	dict:



					'''simple docstring'''




					_snake_case													=			{i: [] for i in range(UpperCamelCase__  )}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(UpperCamelCase__  )
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(UpperCamelCase__  ):
										for j in range(i + 1    ,      UpperCamelCase__  ):
															if random.random() < probability:
																				graph[i].append(UpperCamelCase__  )
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(UpperCamelCase__  )
					return graph





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	dict:



					'''simple docstring'''




					return {
					    i: [j for j in range(UpperCamelCase__  ) if i != j] for i in range(UpperCamelCase__  )
					}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



