"""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 				a_     (      _lowerCAmelCase  : Union[dict, list, tuple, torch.Tensor]     ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	[]
       if isinstance(_UpperCamelCase   , _UpperCamelCase     ):
              for v in tree.values():
                     shapes.extend(_fetch_dims(_UpperCamelCase     )     )
       elif isinstance(_UpperCamelCase   , (list, tuple)     ):
              for t in tree:
                     shapes.extend(_fetch_dims(_UpperCamelCase     )     )
       elif isinstance(_UpperCamelCase   , torch.Tensor     ):
              shapes.append(tree.shape     )
       else:
              raise ValueError('Not supported'     )

       return shapes





@torch.jit.ignore
def 				a_     (      _lowerCAmelCase  : int   , _lowerCAmelCase  : Tuple[int, ...]     ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	[]
       for d in reversed(_UpperCamelCase     ):
              idx.append(flat_idx % d     )
              lowercase__			:		Union[str, Any]       =	flat_idx // d

       return tuple(reversed(_UpperCamelCase     )     )





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





       '''simple docstring'''

       def reduce_edge_list(_lowerCAmelCase  : List[bool]     ) -> None:
              lowercase__			:		Tuple       =	True
              for i in range(len(_UpperCamelCase     )     ):
                     lowercase__			:		Optional[int]       =	-1 * (i + 1)
                     l[reversed_idx] &= tally
                     lowercase__			:		Any       =	l[reversed_idx]

       if start_edges is None:
              lowercase__			:		Dict       =	[s == 0 for s in start]
              reduce_edge_list(_UpperCamelCase     )
       if end_edges is None:
              lowercase__			:		Optional[int]       =	[e == (d - 1) for e, d in zip(_UpperCamelCase   , _UpperCamelCase     )]
              reduce_edge_list(_UpperCamelCase     )

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

       lowercase__			:		Optional[int]       =	[]
       lowercase__			:		Dict       =	[]

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

       lowercase__			:		Optional[Any]       =	tuple(_UpperCamelCase     )
       lowercase__			:		Optional[Any]       =	len(_UpperCamelCase     )

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

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

              lowercase__			:		Any       =	start[divergence_idx]
              return tuple(
                  path + (slice(_UpperCamelCase   , 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__			:		List[str]       =	end[divergence_idx]
              return tuple(
                  path + (slice(_UpperCamelCase   , 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__			:		str       =	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 				a_     (      _lowerCAmelCase  : torch.Tensor   , _lowerCAmelCase  : int   , _lowerCAmelCase  : int   , _lowerCAmelCase  : int     ):





       '''simple docstring'''

       lowercase__			:		str       =	t.shape[:no_batch_dims]
       lowercase__			:		Tuple       =	list(_flat_idx_to_idx(_UpperCamelCase   , _UpperCamelCase     )     )
       # _get_minimal_slice_set is inclusive
       lowercase__			:		Union[str, Any]       =	list(_flat_idx_to_idx(flat_end - 1   , _UpperCamelCase     )     )

       # Get an ordered list of slices to perform
       lowercase__			:		Union[str, Any]       =	_get_minimal_slice_set(
           _UpperCamelCase   , _UpperCamelCase   , _UpperCamelCase   , )

       lowercase__			:		str       =	[t[s] for s in slices]

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





def 				a_     (      _lowerCAmelCase  : Callable   , _lowerCAmelCase  : Dict[str, Any]   , _lowerCAmelCase  : int   , _lowerCAmelCase  : int   , _lowerCAmelCase  : bool = False   , _lowerCAmelCase  : Any = None   , _lowerCAmelCase  : bool = False   , ):





       '''simple docstring'''

       if not (len(_UpperCamelCase     ) > 0):
              raise ValueError('Must provide at least one input'     )

       lowercase__			:		Optional[int]       =	[shape[:no_batch_dims] for shape in _fetch_dims(_UpperCamelCase     )]
       lowercase__			:		int       =	tuple([max(_UpperCamelCase     ) for s in zip(*_UpperCamelCase     )]     )

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

       lowercase__			:		int       =	tensor_tree_map(_prep_inputs   , _UpperCamelCase     )
       lowercase__			:		List[Any]       =	None
       if _out is not None:
              lowercase__			:		Optional[int]       =	tensor_tree_map(lambda _lowerCAmelCase     : t.view([-1] + list(t.shape[no_batch_dims:]     )     )   , _out     )

       lowercase__			:		Optional[Any]       =	1
       for d in orig_batch_dims:
              flat_batch_dim *= d

       lowercase__			:		Union[str, Any]       =	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__			:		List[str]       =	0
       lowercase__			:		str       =	prepped_outputs
       for _ in range(_UpperCamelCase     ):
              # Chunk the input
              if not low_mem:
                     lowercase__			:		Union[str, Any]       =	_select_chunk
              else:
                     lowercase__			:		Optional[int]       =	partial(
                         _chunk_slice   , flat_start=_UpperCamelCase   , flat_end=min(_UpperCamelCase   , i + chunk_size     )   , no_batch_dims=len(_UpperCamelCase     )   , )

              lowercase__			:		List[str]       =	tensor_tree_map(_UpperCamelCase   , _UpperCamelCase     )

              # Run the layer on the chunk
              lowercase__			:		Optional[int]       =	layer(**_UpperCamelCase     )

              # Allocate space for the output
              if out is None:
                     lowercase__			:		Optional[Any]       =	tensor_tree_map(lambda _lowerCAmelCase     : t.new_zeros((flat_batch_dim,) + t.shape[1:]     )   , _UpperCamelCase     )

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

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

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

              i += chunk_size

       lowercase__			:		Optional[Any]       =	tensor_tree_map(lambda _lowerCAmelCase     : t.view(orig_batch_dims + t.shape[1:]     )   , _UpperCamelCase     )

       return out






class      UpperCAmelCase_	:


      def __init__(   self		,			a = 5_1_2		,			)       ->					int:
             lowercase__			:		Dict       =	max_chunk_size
             lowercase__			:		Optional[Any]       =	None
             lowercase__			:		Optional[int]       =	None


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					int:
             logging.info('Tuning chunk size...'							)

             if min_chunk_size >= self.max_chunk_size:
                    return min_chunk_size

             lowercase__			:		Any       =	[2**l for l in range(int(math.log(self.max_chunk_size		,			2							)							) + 1							)]
             lowercase__			:		Optional[Any]       =	[c for c in candidates if c > min_chunk_size]
             lowercase__			:		Union[str, Any]       =	[min_chunk_size] + candidates
             candidates[-1] += 4

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

             lowercase__			:		List[str]       =	0
             lowercase__			:		int       =	len(_a							) - 1
             while i > min_viable_chunk_size_index:
                    lowercase__			:		List[Any]       =	test_chunk_size(candidates[i]							)
                    if not viable:
                           lowercase__			:		List[str]       =	(min_viable_chunk_size_index + i) // 2
                    else:
                           lowercase__			:		List[str]       =	i
                           lowercase__			:		str       =	(i + len(_a							) - 1) // 2

             return candidates[min_viable_chunk_size_index]


      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					bool:
             lowercase__			:		List[Any]       =	True
             for aa, aa in zip(_a		,			_a							):
                    assert type(_a							) == type(_a							)
                    if isinstance(_a		,			(list, tuple)							):
                           consistent &= self._compare_arg_caches(_a		,			_a							)
                    elif isinstance(_a		,			_a							):
                           lowercase__			:		List[Any]       =	[v for _, v in sorted(aa.items()		,			key=lambda a							: x[0]							)]
                           lowercase__			:		Optional[Any]       =	[v for _, v in sorted(aa.items()		,			key=lambda a							: x[0]							)]
                           consistent &= self._compare_arg_caches(_a		,			_a							)
                    else:
                           consistent &= aa == aa

             return consistent


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			)       ->					int:
             lowercase__			:		List[Any]       =	True
             lowercase__			:		int       =	tree_map(lambda a							: a.shape if isinstance(_a		,			torch.Tensor							) else a		,			_a		,			_a							)
             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(_a							)
                    lowercase__			:		List[Any]       =	self._compare_arg_caches(self.cached_arg_data		,			_a							)
             else:
                    # Otherwise, we can reuse the precomputed value
                    lowercase__			:		str       =	False

             if not consistent:
                    lowercase__			:		Optional[int]       =	self._determine_favorable_chunk_size(
                        _a		,			_a		,			_a		,			)
                    lowercase__			:		Any       =	arg_data

             assert self.cached_chunk_size is not None

             return self.cached_chunk_size




'''simple docstring'''
import copy
import re







class 							A__    :
  A__		 =				'hp'
  A__		 =				{}
  A__		 =				None





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	Optional[Any]							,					_a      :	Any )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prefix
       _SCREAMING_SNAKE_CASE							    =defaults
       cls.build_naming_info()





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	List[Any] )	->    Any:





       '''simple docstring'''






       if len(_a ) == 0:
            return ""
       _SCREAMING_SNAKE_CASE							    =None
       if any(char.isdigit() for char in word ):
            raise Exception(f"Parameters should not contain numbers: '{word}' contains a number" )
       if word in info["short_word"]:
            return info["short_word"][word]
       for prefix_len in range(1							,					len(_a ) + 1 ):
            _SCREAMING_SNAKE_CASE							    =word[:prefix_len]
            if prefix in info["reverse_short_word"]:
                 continue
            else:
                 _SCREAMING_SNAKE_CASE							    =prefix
                 break

       if short_word is None:
            # Paranoid fallback
            def int_to_alphabetic(_a      :	str ):
                 _SCREAMING_SNAKE_CASE							    =''
                 while integer != 0:
                      _SCREAMING_SNAKE_CASE							    =chr(ord('A' ) + integer % 10 ) + s
                      integer //= 10
                 return s

            _SCREAMING_SNAKE_CASE							    =0
            while True:
                 _SCREAMING_SNAKE_CASE							    =word + '#' + int_to_alphabetic(_a )
                 if sword in info["reverse_short_word"]:
                      continue
                 else:
                      _SCREAMING_SNAKE_CASE							    =sword
                      break

       _SCREAMING_SNAKE_CASE							    =short_word
       _SCREAMING_SNAKE_CASE							    =word
       return short_word





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =param_name.split('_' )

       _SCREAMING_SNAKE_CASE							    =[TrialShortNamer.shortname_for_word(_a							,					_a ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       _SCREAMING_SNAKE_CASE							    =['', '_']

       for separator in separators:
            _SCREAMING_SNAKE_CASE							    =separator.join(_a )
            if shortname not in info["reverse_short_param"]:
                 _SCREAMING_SNAKE_CASE							    =shortname
                 _SCREAMING_SNAKE_CASE							    =param_name
                 return shortname

       return param_name





  @staticmethod
  def 						A  (		_a      :	Dict							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =TrialShortNamer.shortname_for_key(_a							,					_a )
       _SCREAMING_SNAKE_CASE							    =short_name
       _SCREAMING_SNAKE_CASE							    =param_name





  @classmethod
  def 						A  (		cls      :	Optional[int] )	->    Tuple:





       '''simple docstring'''






       if cls.NAMING_INFO is not None:
            return

       _SCREAMING_SNAKE_CASE							    ={
           'short_word': {},
           'reverse_short_word': {},
           'short_param': {},
           'reverse_short_param': {},
       }

       _SCREAMING_SNAKE_CASE							    =list(cls.DEFAULTS.keys() )

       for k in field_keys:
            cls.add_new_param_name(_a							,					_a )

       _SCREAMING_SNAKE_CASE							    =info





  @classmethod
  def 						A  (		cls      :	List[Any]							,					_a      :	int )	->    int:





       '''simple docstring'''






       cls.build_naming_info()
       assert cls.PREFIX is not None
       _SCREAMING_SNAKE_CASE							    =[copy.copy(cls.PREFIX )]

       for k, v in params.items():
            if k not in cls.DEFAULTS:
                 raise Exception(f"You should provide a default value for the param name {k} with value {v}" )
            if v == cls.DEFAULTS[k]:
                 # The default value is not added to the name
                 continue

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['short_param'][k]

            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =1 if v else 0

            _SCREAMING_SNAKE_CASE							    ='' if isinstance(_a							,					(int, float) ) else '-'
            _SCREAMING_SNAKE_CASE							    =f"{key}{sep}{v}"
            name.append(_a )

       return "_".join(_a )





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =repr[len(cls.PREFIX ) + 1 :]
       if repr == "":
            _SCREAMING_SNAKE_CASE							    =[]
       else:
            _SCREAMING_SNAKE_CASE							    =repr.split('_' )

       _SCREAMING_SNAKE_CASE							    ={}

       for value in values:
            if "-" in value:
                 _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =value.split('-' )
            else:
                 _SCREAMING_SNAKE_CASE							    =re.sub('[0-9.]'							,					''							,					_a )
                 _SCREAMING_SNAKE_CASE							    =float(re.sub('[^0-9.]'							,					''							,					_a ) )

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['reverse_short_param'][p_k]

            _SCREAMING_SNAKE_CASE							    =p_v

       for k in cls.DEFAULTS:
            if k not in parameters:
                 _SCREAMING_SNAKE_CASE							    =cls.DEFAULTS[k]

       return parameters
