






"""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       snake_case  (A_				:Union[dict, list, tuple, torch.Tensor]				):







  '''simple docstring'''

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

  return shapes




@torch.jit.ignore
def       snake_case  (A_				:int     ,      A_				:Tuple[int, ...]				):







  '''simple docstring'''

  a					:      Tuple    =       []
  for d in reversed(A_				):
    idx.append(flat_idx % d				)
    a					:      Union[str, Any]    =       flat_idx // d

  return tuple(reversed(A_				)				)




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







  '''simple docstring'''

  def reduce_edge_list(A_				:List[bool]				) -> None:
    a					:      Tuple    =       True
    for i in range(len(A_				)				):
      a					:      Union[str, Any]    =       -1 * (i + 1)
      l[reversed_idx] &= tally
      a					:      Dict    =       l[reversed_idx]

  if start_edges is None:
    a					:      Dict    =       [s == 0 for s in start]
    reduce_edge_list(A_				)
  if end_edges is None:
    a					:      str    =       [e == (d - 1) for e, d in zip(A_     ,      A_				)]
    reduce_edge_list(A_				)

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

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

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

  a					:      Tuple[slice, ...]    =       tuple(A_				)
  a					:      Union[str, Any]    =       len(A_				)

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

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

    a					:      Optional[Any]    =       start[divergence_idx]
    return tuple(
        path + (slice(A_     ,      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

    a					:      Optional[int]    =       end[divergence_idx]
    return tuple(
        path + (slice(A_     ,      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()				)
    a					:      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       snake_case  (A_				:torch.Tensor     ,      A_				:int     ,      A_				:int     ,      A_				:int				):







  '''simple docstring'''

  a					:      List[Any]    =       t.shape[:no_batch_dims]
  a					:      Union[str, Any]    =       list(_flat_idx_to_idx(A_     ,      A_				)				)
  # _get_minimal_slice_set is inclusive
  a					:      List[str]    =       list(_flat_idx_to_idx(flat_end - 1     ,      A_				)				)

  # Get an ordered list of slices to perform
  a					:      Dict    =       _get_minimal_slice_set(
      A_     ,      A_     ,      A_     ,      )

  a					:      Dict    =       [t[s] for s in slices]

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




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







  '''simple docstring'''

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

  a					:      Optional[Any]    =       [shape[:no_batch_dims] for shape in _fetch_dims(A_				)]
  a					:      List[str]    =       tuple([max(A_				) for s in zip(*A_				)]				)

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

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

  a					:      Union[str, Any]    =       1
  for d in orig_batch_dims:
    flat_batch_dim *= d

  a					:      Union[str, Any]    =       flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

    a					:      Dict[str, Any]    =       tensor_tree_map(A_     ,      A_				)

    # Run the layer on the chunk
    a					:      Tuple    =       layer(**A_				)

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

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

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

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

    i += chunk_size

  a					:      str    =       tensor_tree_map(lambda A_				: t.view(orig_batch_dims + t.shape[1:]				)     ,      A_				)

  return out






class 						snake_case  :





    def __init__(							self     : Dict    ,		A     : int = 5_1_2    ,		):


      '''simple docstring'''




      a					:      List[str]    =       max_chunk_size
      a					:      Optional[int]    =       None
      a					:      Optional[tuple]    =       None





    def     lowerCamelCase__				(							self     : Any    ,		A     : Callable    ,		A     : tuple    ,		A     : int      ):


      '''simple docstring'''




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

      if min_chunk_size >= self.max_chunk_size:
        return min_chunk_size

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

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

      a					:      Any    =       0
      a					:      List[Any]    =       len(A      ) - 1
      while i > min_viable_chunk_size_index:
        a					:      Union[str, Any]    =       test_chunk_size(candidates[i]      )
        if not viable:
          a					:      Dict    =       (min_viable_chunk_size_index + i) // 2
        else:
          a					:      Any    =       i
          a					:      Any    =       (i + len(A      ) - 1) // 2

      return candidates[min_viable_chunk_size_index]





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : Iterable    ,		A     : Iterable      ):


      '''simple docstring'''




      a					:      Tuple    =       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      ):
          a					:      str    =       [v for _, v in sorted(aa.items()    ,		key=lambda A      : x[0]      )]
          a					:      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     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Callable    ,		A     : tuple    ,		A     : int    ,		):


      '''simple docstring'''




      a					:      Optional[Any]    =       True
      a					:      tuple    =       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      )
        a					:      Optional[Any]    =       self._compare_arg_caches(self.cached_arg_data    ,		A      )
      else:
        # Otherwise, we can reuse the precomputed value
        a					:      Optional[Any]    =       False

      if not consistent:
        a					:      Optional[int]    =       self._determine_favorable_chunk_size(
            A    ,		A    ,		A    ,		)
        a					:      List[Any]    =       arg_data

      assert self.cached_chunk_size is not None

      return self.cached_chunk_size












"""simple docstring"""


import os
import sys

_UpperCamelCase    :   str   			=						os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_UpperCamelCase    :   int   			=						[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]




@add_start_docstrings(AutoConfig.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:int				):







  '''simple docstring'''

  return AutoConfig.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoTokenizer.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoTokenizer.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModel.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Tuple				):







  '''simple docstring'''

  return AutoModel.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def       snake_case  (*A_				:str     ,      **A_				:Optional[int]				):







  '''simple docstring'''

  return AutoModelForCausalLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Dict				):







  '''simple docstring'''

  return AutoModelForMaskedLM.from_pretrained(*A_     ,      **A_				)




@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:str				):







  '''simple docstring'''

  return AutoModelForSequenceClassification.from_pretrained(*A_     ,      **A_				)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:List[Any]				):







  '''simple docstring'''

  return AutoModelForQuestionAnswering.from_pretrained(*A_     ,      **A_				)




