


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not head:
						return True
			# split the list to two parts
			__lowercase       ,			__lowercase			       = head.next, head
			while fast and fast.next:
						__lowercase			       = fast.next.next
						__lowercase			       = slow.next
			__lowercase			       = slow.next
			__lowercase			       = None  # Don't forget here! But forget still works!
			# reverse the second part
			__lowercase			       = None
			while second:
						__lowercase			       = second.next
						__lowercase			       = node
						__lowercase			       = second
						__lowercase			       = nxt
			# compare two parts
			# second part has the same or one less node
			while node:
						if node.val != head.val:
									return False
						__lowercase			       = node.next
						__lowercase			       = head.next
			return True


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not head or not head.next:
						return True

			# 1. Get the midpoint (slow)
			__lowercase			       = __lowercase			       = __lowercase			       = head
			while fast and fast.next:
						__lowercase       ,			__lowercase			       = fast.next.next, slow.next

			# 2. Push the second half into the stack
			__lowercase			       = [slow.val]
			while slow.next:
						__lowercase			       = slow.next
						stack.append(slow.val	)

			# 3. Comparison
			while stack:
						if stack.pop() != cur.val:
									return False
						__lowercase			       = cur.next

			return True





def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if not head or not head.next:
						return True
			__lowercase			       = {}
			__lowercase			       = 0
			while head:
						if head.val in d:
									d[head.val].append(_UpperCamelCase	)
						else:
									__lowercase			       = [pos]
						__lowercase			       = head.next
						pos += 1
			__lowercase			       = pos - 1
			__lowercase			       = 0
			for v in d.values():
						if len(_UpperCamelCase	) % 2 != 0:
									middle += 1
						else:
									__lowercase			       = 0
									for i in range(0							,      len(_UpperCamelCase	)	):
												if v[i] + v[len(_UpperCamelCase	) - 1 - step] != checksum:
															return False
												step += 1
						if middle > 1:
									return False
			return True






import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      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(_UpperCamelCase	)

			__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(_UpperCamelCase	)

			__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(_UpperCamelCase							,      depth // 3							,      dim=0	)

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

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

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

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

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


if __name__ == "__main__":
	a			:			List[Any]												=					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			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


