




import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text



def     __lowerCAmelCase						(							snake_case	:    Optional[Any] )       ->							Optional[int]:
   __lowerCamelCase:				List[Any]     =	[]
   for line in lines:
      __lowerCamelCase:				Tuple     =	re.sub(r"""#.*"""						,      """"""						,      lowerCAmelCase_ )  # remove comments
      if line:
         filtered_lines.append(lowerCAmelCase_ )
   __lowerCamelCase:				Dict     =	'''\n'''.join(lowerCAmelCase_ )

   # Make a hash from all this code
   __lowerCamelCase:				Union[str, Any]     =	full_str.encode("""utf-8""" )
   return shaaaa(lowerCAmelCase_ ).hexdigest()


# get importable module names and hash for caching
_A		:				List[Any]									=       {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_A		:				List[str]									=       {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_A		:				str									=       {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
_A		:				Dict[str, List[str]]									=       {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')







_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')






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						(							snake_case	:    Union[dict, list, tuple, torch.Tensor] )       ->							List[Tuple[int, ...]]:
   __lowerCamelCase:				Dict     =	[]
   if isinstance(snake_case						,      snake_case ):
      for v in tree.values():
         shapes.extend(_fetch_dims(snake_case ) )
   elif isinstance(snake_case						,      (list, tuple) ):
      for t in tree:
         shapes.extend(_fetch_dims(snake_case ) )
   elif isinstance(snake_case						,      torch.Tensor ):
      shapes.append(tree.shape )
   else:
      raise ValueError("""Not supported""" )

   return shapes



@torch.jit.ignore
def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    Tuple[int, ...] )       ->							Tuple[int, ...]:
   __lowerCamelCase:				Union[str, Any]     =	[]
   for d in reversed(snake_case ):
      idx.append(flat_idx % d )
      __lowerCamelCase:				Optional[int]     =	flat_idx // d

   return tuple(reversed(snake_case ) )



@torch.jit.ignore
def     __lowerCAmelCase						(							snake_case	:    Sequence[int]						,      snake_case	:    Sequence[int]						,      snake_case	:    Sequence[int]						,      snake_case	:    Optional[Sequence[bool]] = None						,      snake_case	:    Optional[Sequence[bool]] = None						,      )       ->							List[Tuple[slice, ...]]:

   # 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(snake_case	:    List[bool] ) -> None:
      __lowerCamelCase:				List[Any]     =	True
      for i in range(len(snake_case ) ):
         __lowerCamelCase:				str     =	-1 * (i + 1)
         l[reversed_idx] &= tally
         __lowerCamelCase:				List[Any]     =	l[reversed_idx]

   if start_edges is None:
      __lowerCamelCase:				str     =	[s == 0 for s in start]
      reduce_edge_list(snake_case )
   if end_edges is None:
      __lowerCamelCase:				Union[str, Any]     =	[e == (d - 1) for e, d in zip(snake_case						,      snake_case )]
      reduce_edge_list(snake_case )

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

   __lowerCamelCase:				List[Tuple[slice, ...]]     =	[]
   __lowerCamelCase:				List[slice]     =	[]

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

   __lowerCamelCase:				Tuple[slice, ...]     =	tuple(snake_case )
   __lowerCamelCase:				Dict     =	len(snake_case )

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

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

      __lowerCamelCase:				Dict     =	start[divergence_idx]
      return tuple(
          path + (slice(snake_case						,      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

      __lowerCamelCase:				List[Any]     =	end[divergence_idx]
      return tuple(
          path + (slice(snake_case						,      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() )
      __lowerCamelCase:				List[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     __lowerCAmelCase						(							snake_case	:    torch.Tensor						,      snake_case	:    int						,      snake_case	:    int						,      snake_case	:    int )       ->							torch.Tensor:
   __lowerCamelCase:				Any     =	t.shape[:no_batch_dims]
   __lowerCamelCase:				Dict     =	list(_flat_idx_to_idx(snake_case						,      snake_case ) )
   # _get_minimal_slice_set is inclusive
   __lowerCamelCase:				Optional[int]     =	list(_flat_idx_to_idx(flat_end - 1						,      snake_case ) )

   # Get an ordered list of slices to perform
   __lowerCamelCase:				str     =	_get_minimal_slice_set(
       snake_case						,      snake_case						,      snake_case						,      )

   __lowerCamelCase:				Tuple     =	[t[s] for s in slices]

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



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

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

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

   __lowerCamelCase:				Dict[str, Any]     =	tensor_tree_map(_prep_inputs						,      snake_case )
   __lowerCamelCase:				Optional[Any]     =	None
   if _out is not None:
      __lowerCamelCase:				List[Any]     =	tensor_tree_map(lambda snake_case : t.view([-1] + list(t.shape[no_batch_dims:] ) )						,      _out )

   __lowerCamelCase:				List[Any]     =	1
   for d in orig_batch_dims:
      flat_batch_dim *= d

   __lowerCamelCase:				int     =	flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

      __lowerCamelCase:				Dict[str, Any]     =	tensor_tree_map(snake_case						,      snake_case )

      # Run the layer on the chunk
      __lowerCamelCase:				Union[str, Any]     =	layer(**snake_case )

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

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

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

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

      i += chunk_size

   __lowerCamelCase:				Union[str, Any]     =	tensor_tree_map(lambda snake_case : t.view(orig_batch_dims + t.shape[1:] )						,      snake_case )

   return out







class        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int = 512    ,	):
        __lowerCamelCase:				Any     =	max_chunk_size
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[tuple]     =	None





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Callable    ,	SCREAMING_SNAKE_CASE_					:				tuple    ,	SCREAMING_SNAKE_CASE_					:				int							):
        logging.info("""Tuning chunk size..."""							)

        if min_chunk_size >= self.max_chunk_size:
           return min_chunk_size

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

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

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

        return candidates[min_viable_chunk_size_index]





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Iterable    ,	SCREAMING_SNAKE_CASE_					:				Iterable							):
        __lowerCamelCase:				int     =	True
        for aa, aa in zip(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
           assert type(SCREAMING_SNAKE_CASE_							) == type(SCREAMING_SNAKE_CASE_							)
           if isinstance(SCREAMING_SNAKE_CASE_    ,	(list, tuple)							):
              consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           elif isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
              __lowerCamelCase:				int     =	[v for _, v in sorted(aa.items()    ,	key=lambda SCREAMING_SNAKE_CASE_							: x[0]							)]
              __lowerCamelCase:				Dict     =	[v for _, v in sorted(aa.items()    ,	key=lambda SCREAMING_SNAKE_CASE_							: x[0]							)]
              consistent &= self._compare_arg_caches(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           else:
              consistent &= aa == aa

        return consistent







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Callable    ,	SCREAMING_SNAKE_CASE_					:				tuple    ,	SCREAMING_SNAKE_CASE_					:				int    ,	):
        __lowerCamelCase:				int     =	True
        __lowerCamelCase:				tuple     =	tree_map(lambda SCREAMING_SNAKE_CASE_							: a.shape if isinstance(SCREAMING_SNAKE_CASE_    ,	torch.Tensor							) else a    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        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(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				List[str]     =	self._compare_arg_caches(self.cached_arg_data    ,	SCREAMING_SNAKE_CASE_							)
        else:
           # Otherwise, we can reuse the precomputed value
           __lowerCamelCase:				Optional[int]     =	False

        if not consistent:
           __lowerCamelCase:				str     =	self._determine_favorable_chunk_size(
               SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	)
           __lowerCamelCase:				Optional[int]     =	arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size


