





'''simple docstring'''



import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[dict, list, tuple, torch.Tensor]				):
			__lowercase										=							[]
			if isinstance(lowerCamelCase_		,       lowerCamelCase_				):
						for v in tree.values():
									shapes.extend(_fetch_dims(lowerCamelCase_				)				)
			elif isinstance(lowerCamelCase_		,       (list, tuple)				):
						for t in tree:
									shapes.extend(_fetch_dims(lowerCamelCase_				)				)
			elif isinstance(lowerCamelCase_		,       torch.Tensor				):
						shapes.append(tree.shape				)
			else:
						raise ValueError('''Not supported'''				)

			return shapes




@torch.jit.ignore
def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  Tuple[int, ...]				):
			__lowercase										=							[]
			for d in reversed(lowerCamelCase_				):
						idx.append(flat_idx % d				)
						__lowercase										=							flat_idx // d

			return tuple(reversed(lowerCamelCase_				)				)




@torch.jit.ignore
def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[int]		,       lowerCamelCase_			:  Sequence[int]		,       lowerCamelCase_			:  Sequence[int]		,       lowerCamelCase_			:  Optional[Sequence[bool]] = None		,       lowerCamelCase_			:  Optional[Sequence[bool]] = None		,       ):

			# start_edges and end_edges both indicate whether, starting from any given
			# dimension, the start/end index is at the top/bottom edge of the
			# corresponding tensor, modeled as a tree
			def reduce_edge_list(lowerCamelCase_			:  List[bool]				) -> None:
						__lowercase										=							True
						for i in range(len(lowerCamelCase_				)				):
									__lowercase										=							-1 * (i + 1)
									l[reversed_idx] &= tally
									__lowercase										=							l[reversed_idx]

			if start_edges is None:
						__lowercase										=							[s == 0 for s in start]
						reduce_edge_list(lowerCamelCase_				)
			if end_edges is None:
						__lowercase										=							[e == (d - 1) for e, d in zip(lowerCamelCase_		,       lowerCamelCase_				)]
						reduce_edge_list(lowerCamelCase_				)

			# Base cases. Either start/end are empty and we're done, or the final,
			# one-dimensional tensor can be simply sliced
			if len(lowerCamelCase_				) == 0:
						return [()]
			elif len(lowerCamelCase_				) == 1:
						return [(slice(start[0]		,       end[0] + 1				),)]

			__lowercase										=							[]
			__lowercase										=							[]

			# Dimensions common to start and end can be selected directly
			for s, e in zip(lowerCamelCase_		,       lowerCamelCase_				):
						if s == e:
									path_list.append(slice(lowerCamelCase_		,       s + 1				)				)
						else:
									break

			__lowercase										=							tuple(lowerCamelCase_				)
			__lowercase										=							len(lowerCamelCase_				)

			# start == end, and we're done
			if divergence_idx == len(lowerCamelCase_				):
						return [path]

			def upper() -> Tuple[Tuple[slice, ...], ...]:
						assert start_edges is not None
						assert end_edges is not None

						__lowercase										=							start[divergence_idx]
						return tuple(
						    path + (slice(lowerCamelCase_		,       sdi + 1				),) + s
						    for s in _get_minimal_slice_set(
						        start[divergence_idx + 1 :]		,       [d - 1 for d in dims[divergence_idx + 1 :]]		,       dims[divergence_idx + 1 :]		,       start_edges=start_edges[divergence_idx + 1 :]		,       end_edges=[True for _ in end_edges[divergence_idx + 1 :]]		,       )				)

			def lower() -> Tuple[Tuple[slice, ...], ...]:
						assert start_edges is not None
						assert end_edges is not None

						__lowercase										=							end[divergence_idx]
						return tuple(
						    path + (slice(lowerCamelCase_		,       edi + 1				),) + s
						    for s in _get_minimal_slice_set(
						        [0 for _ in start[divergence_idx + 1 :]]		,       end[divergence_idx + 1 :]		,       dims[divergence_idx + 1 :]		,       start_edges=[True for _ in start_edges[divergence_idx + 1 :]]		,       end_edges=end_edges[divergence_idx + 1 :]		,       )				)

			# If both start and end are at the edges of the subtree rooted at
			# divergence_idx, we can just select the whole subtree at once
			if start_edges[divergence_idx] and end_edges[divergence_idx]:
						slices.append(path + (slice(start[divergence_idx]		,       end[divergence_idx] + 1				),)				)
			# If just start is at the edge, we can grab almost all of the subtree,
			# treating only the ragged bottom edge as an edge case
			elif start_edges[divergence_idx]:
						slices.append(path + (slice(start[divergence_idx]		,       end[divergence_idx]				),)				)
						slices.extend(lower()				)
			# Analogous to the previous case, but the top is ragged this time
			elif end_edges[divergence_idx]:
						slices.extend(upper()				)
						slices.append(path + (slice(start[divergence_idx] + 1		,       end[divergence_idx] + 1				),)				)
			# If both sides of the range are ragged, we need to handle both sides
			# separately. If there's contiguous meat in between them, we can index it
			# in one big chunk
			else:
						slices.extend(upper()				)
						__lowercase										=							end[divergence_idx] - start[divergence_idx]
						if middle_ground > 1:
									slices.append(path + (slice(start[divergence_idx] + 1		,       end[divergence_idx]				),)				)
						slices.extend(lower()				)

			return slices




@torch.jit.ignore
def    _lowerCAmelCase						(				lowerCamelCase_			:  torch.Tensor		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			__lowercase										=							t.shape[:no_batch_dims]
			__lowercase										=							list(_flat_idx_to_idx(lowerCamelCase_		,       lowerCamelCase_				)				)
			# _get_minimal_slice_set is inclusive
			__lowercase										=							list(_flat_idx_to_idx(flat_end - 1		,       lowerCamelCase_				)				)

			# Get an ordered list of slices to perform
			__lowercase										=							_get_minimal_slice_set(
			    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       )

			__lowercase										=							[t[s] for s in slices]

			return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]				) for s in sliced_tensors]				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  Callable		,       lowerCamelCase_			:  Dict[str, Any]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  bool = False		,       lowerCamelCase_			:  Any = None		,       lowerCamelCase_			:  bool = False		,       ):
			if not (len(lowerCamelCase_				) > 0):
						raise ValueError('''Must provide at least one input'''				)

			__lowercase										=							[shape[:no_batch_dims] for shape in _fetch_dims(lowerCamelCase_				)]
			__lowercase										=							tuple([max(lowerCamelCase_				) for s in zip(*lowerCamelCase_				)]				)

			def _prep_inputs(lowerCamelCase_			:  torch.Tensor				) -> torch.Tensor:
						if not low_mem:
									if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
												__lowercase										=							t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
									__lowercase										=							t.reshape(-1		,       *t.shape[no_batch_dims:]				)
						else:
									__lowercase										=							t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
						return t

			__lowercase										=							tensor_tree_map(_prep_inputs		,       lowerCamelCase_				)
			__lowercase										=							None
			if _out is not None:
						__lowercase										=							tensor_tree_map(lambda lowerCamelCase_				: t.view([-1] + list(t.shape[no_batch_dims:]				)				)		,       _out				)

			__lowercase										=							1
			for d in orig_batch_dims:
						flat_batch_dim *= d

			__lowercase										=							flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

			def _select_chunk(lowerCamelCase_			:  torch.Tensor				) -> torch.Tensor:
						return t[i : i + chunk_size] if t.shape[0] != 1 else t

			__lowercase										=							0
			__lowercase										=							prepped_outputs
			for _ in range(lowerCamelCase_				):
						# Chunk the input
						if not low_mem:
									__lowercase										=							_select_chunk
						else:
									__lowercase										=							partial(
									    _chunk_slice		,       flat_start=lowerCamelCase_		,       flat_end=min(lowerCamelCase_		,       i + chunk_size				)		,       no_batch_dims=len(lowerCamelCase_				)		,       )

						__lowercase										=							tensor_tree_map(lowerCamelCase_		,       lowerCamelCase_				)

						# Run the layer on the chunk
						__lowercase										=							layer(**lowerCamelCase_				)

						# Allocate space for the output
						if out is None:
									__lowercase										=							tensor_tree_map(lambda lowerCamelCase_				: t.new_zeros((flat_batch_dim,) + t.shape[1:]				)		,       lowerCamelCase_				)

						# Put the chunk in its pre-allocated space
						if isinstance(lowerCamelCase_		,       lowerCamelCase_				):

									def assign(lowerCamelCase_			:  dict		,       lowerCamelCase_			:  dict				) -> None:
												for k, v in da.items():
															if isinstance(lowerCamelCase_		,       lowerCamelCase_				):
																		assign(lowerCamelCase_		,       da[k]				)
															else:
																		if _add_into_out:
																					v[i : i + chunk_size] += da[k]
																		else:
																					__lowercase										=							da[k]

									assign(lowerCamelCase_		,       lowerCamelCase_				)
						elif isinstance(lowerCamelCase_		,       lowerCamelCase_				):
									for xa, xa in zip(lowerCamelCase_		,       lowerCamelCase_				):
												if _add_into_out:
															xa[i : i + chunk_size] += xa
												else:
															__lowercase										=							xa
						elif isinstance(lowerCamelCase_		,       torch.Tensor				):
									if _add_into_out:
												out[i : i + chunk_size] += output_chunk
									else:
												__lowercase										=							output_chunk
						else:
									raise ValueError('''Not supported'''				)

						i += chunk_size

			__lowercase										=							tensor_tree_map(lambda lowerCamelCase_				: t.view(orig_batch_dims + t.shape[1:]				)		,       lowerCamelCase_				)

			return out






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = 512     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							max_chunk_size
				__lowercase										=							None
				__lowercase										=							None


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				logging.info('''Tuning chunk size...''' )

				if min_chunk_size >= self.max_chunk_size:
							return min_chunk_size

				__lowercase										=							[2**l for l in range(int(math.log(self.max_chunk_size     ,2 ) ) + 1 )]
				__lowercase										=							[c for c in candidates if c > min_chunk_size]
				__lowercase										=							[min_chunk_size] + candidates
				candidates[-1] += 4

				def test_chunk_size(_lowerCamelCase ) -> bool:
							try:
										with torch.no_grad():
													fn(*_lowerCamelCase     ,chunk_size=_lowerCamelCase )
										return True
							except RuntimeError:
										return False

				__lowercase										=							0
				__lowercase										=							len(_lowerCamelCase ) - 1
				while i > min_viable_chunk_size_index:
							__lowercase										=							test_chunk_size(candidates[i] )
							if not viable:
										__lowercase										=							(min_viable_chunk_size_index + i) // 2
							else:
										__lowercase										=							i
										__lowercase										=							(i + len(_lowerCamelCase ) - 1) // 2

				return candidates[min_viable_chunk_size_index]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							bool:







				'''simple docstring'''





				__lowercase										=							True
				for aa, aa in zip(_lowerCamelCase     ,_lowerCamelCase ):
							assert type(_lowerCamelCase ) == type(_lowerCamelCase )
							if isinstance(_lowerCamelCase     ,(list, tuple) ):
										consistent &= self._compare_arg_caches(_lowerCamelCase     ,_lowerCamelCase )
							elif isinstance(_lowerCamelCase     ,_lowerCamelCase ):
										__lowercase										=							[v for _, v in sorted(aa.items()     ,key=lambda _lowerCamelCase : x[0] )]
										__lowercase										=							[v for _, v in sorted(aa.items()     ,key=lambda _lowerCamelCase : x[0] )]
										consistent &= self._compare_arg_caches(_lowerCamelCase     ,_lowerCamelCase )
							else:
										consistent &= aa == aa

				return consistent


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							int:







				'''simple docstring'''





				__lowercase										=							True
				__lowercase										=							tree_map(lambda _lowerCamelCase : a.shape if isinstance(_lowerCamelCase     ,torch.Tensor ) else a     ,_lowerCamelCase     ,_lowerCamelCase )
				if self.cached_arg_data is not None:
							# If args have changed shape/value, we need to re-tune
							assert len(self.cached_arg_data ) == len(_lowerCamelCase )
							__lowercase										=							self._compare_arg_caches(self.cached_arg_data     ,_lowerCamelCase )
				else:
							# Otherwise, we can reuse the precomputed value
							__lowercase										=							False

				if not consistent:
							__lowercase										=							self._determine_favorable_chunk_size(
							    _lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)
							__lowercase										=							arg_data

				assert self.cached_chunk_size is not None

				return self.cached_chunk_size












'''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)





