





'''simple docstring'''



from collections import deque




def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple				):
			__lowercase										=							len(lowerCamelCase_				)
			__lowercase										=							deque()
			__lowercase										=							[False for _ in range(lowerCamelCase_				)]
			__lowercase										=							[-1 for _ in range(lowerCamelCase_				)]
			__lowercase										=							index_of[:]

			def strong_connect(lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Optional[int]				):
						__lowercase										=							index  # the number when this node is seen
						__lowercase										=							index  # lowest rank node reachable from here
						index += 1
						stack.append(lowerCamelCase_				)
						__lowercase										=							True

						for w in g[v]:
									if index_of[w] == -1:
												__lowercase										=							strong_connect(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
												__lowercase										=							(
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)
									elif on_stack[w]:
												__lowercase										=							(
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)

						if lowlink_of[v] == index_of[v]:
									__lowercase										=							[]
									__lowercase										=							stack.pop()
									__lowercase										=							False
									component.append(lowerCamelCase_				)
									while w != v:
												__lowercase										=							stack.pop()
												__lowercase										=							False
												component.append(lowerCamelCase_				)
									components.append(lowerCamelCase_				)
						return index

			__lowercase										=							[]
			for v in range(lowerCamelCase_				):
						if index_of[v] == -1:
									strong_connect(lowerCamelCase_		,       0		,       lowerCamelCase_				)

			return components




def    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Tuple				):
			__lowercase										=							[[] for _ in range(lowerCamelCase_				)]
			for u, v in edges:
						g[u].append(lowerCamelCase_				)
			return g


if __name__ == "__main__":
	# Test
	_SCREAMING_SNAKE_CASE					=    7
	_SCREAMING_SNAKE_CASE					=    [0, 0, 1, 2, 3, 3, 4, 4, 6]
	_SCREAMING_SNAKE_CASE					=    [1, 3, 2, 0, 1, 4, 5, 6, 5]
	_SCREAMING_SNAKE_CASE					=    [(u, v) for u, v in zip(source, target)]
	_SCREAMING_SNAKE_CASE					=    create_graph(n_vertices, edges)

	assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)












'''simple docstring'''



import argparse
from pathlib import Path

import torch

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


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)
			if "model" in sd.keys():
						__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)['''model''']

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

			__lowercase										=							{
			    '''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:
									__lowercase										=							sd.pop(lowerCamelCase_				)

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

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

									__lowercase										=							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
									__lowercase						,		__lowercase						,		__lowercase										=							torch.split(lowerCamelCase_		,       depth // 3		,       dim=0				)

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

			return sd




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]=None				):
			__lowercase										=							load_checkpoint(lowerCamelCase_				)

			if config is not None:
						__lowercase										=							OPTConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							OPTConfig()

			__lowercase										=							OPTModel(lowerCamelCase_				).half().eval()
			model.load_state_dict(lowerCamelCase_				)

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


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





