

'''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					(						_UpperCamelCase						:     Union[dict, list, tuple, torch.Tensor]				)      ->			List[Tuple[int, ...]]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     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        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     Tuple[int, ...]				)      ->			Tuple[int, ...]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     for d in reversed(_UpperCamelCase				):
          idx.append(flat_idx % d				)
          _SCREAMING_SNAKE_CASE							    =flat_idx // d

     return tuple(reversed(_UpperCamelCase				)				)


@torch.jit.ignore
def        _lowerCAmelCase					(						_UpperCamelCase						:     Sequence[int]							, _UpperCamelCase						:     Sequence[int]							, _UpperCamelCase						:     Sequence[int]							, _UpperCamelCase						:     Optional[Sequence[bool]] = None							, _UpperCamelCase						:     Optional[Sequence[bool]] = None							, )      ->			List[Tuple[slice, ...]]:
     """simple docstring"""




     def reduce_edge_list(_UpperCamelCase						:     List[bool]				) -> None:
          _SCREAMING_SNAKE_CASE							    =True
          for i in range(len(_UpperCamelCase				)				):
               _SCREAMING_SNAKE_CASE							    =-1 * (i + 1)
               l[reversed_idx] &= tally
               _SCREAMING_SNAKE_CASE							    =l[reversed_idx]

     if start_edges is None:
          _SCREAMING_SNAKE_CASE							    =[s == 0 for s in start]
          reduce_edge_list(_UpperCamelCase				)
     if end_edges is None:
          _SCREAMING_SNAKE_CASE							    =[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				),)]

     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =[]

     # 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

     _SCREAMING_SNAKE_CASE							    =tuple(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =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

          _SCREAMING_SNAKE_CASE							    =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

          _SCREAMING_SNAKE_CASE							    =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()				)
          _SCREAMING_SNAKE_CASE							    =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					(						_UpperCamelCase						:     torch.Tensor							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			torch.Tensor:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =t.shape[:no_batch_dims]
     _SCREAMING_SNAKE_CASE							    =list(_flat_idx_to_idx(_UpperCamelCase							, _UpperCamelCase				)				)
     # _get_minimal_slice_set is inclusive
     _SCREAMING_SNAKE_CASE							    =list(_flat_idx_to_idx(flat_end - 1							, _UpperCamelCase				)				)

     # Get an ordered list of slices to perform
     _SCREAMING_SNAKE_CASE							    =_get_minimal_slice_set(
         _UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, )

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

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Callable							, _UpperCamelCase						:     Dict[str, Any]							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int							, _UpperCamelCase						:     bool = False							, _UpperCamelCase						:     Any = None							, _UpperCamelCase						:     bool = False							, )      ->			Any:
     """simple docstring"""




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

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

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

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

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

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

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

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

          _SCREAMING_SNAKE_CASE							    =tensor_tree_map(_UpperCamelCase							, _UpperCamelCase				)

          # Run the layer on the chunk
          _SCREAMING_SNAKE_CASE							    =layer(**_UpperCamelCase				)

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

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

               def assign(_UpperCamelCase						:     dict							, _UpperCamelCase						:     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:
                                   _SCREAMING_SNAKE_CASE							    =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:
                         _SCREAMING_SNAKE_CASE							    =xa
          elif isinstance(_UpperCamelCase							, torch.Tensor				):
               if _add_into_out:
                    out[i : i + chunk_size] += output_chunk
               else:
                    _SCREAMING_SNAKE_CASE							    =output_chunk
          else:
               raise ValueError('Not supported'				)

          i += chunk_size

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

     return out







class 							A__    :





  def __init__(		self      :	str							,					_a      :	int = 512							,					)	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =max_chunk_size
       _SCREAMING_SNAKE_CASE							    =None
       _SCREAMING_SNAKE_CASE							    =None





  def 						A  (		self      :	Optional[Any]							,					_a      :	Callable							,					_a      :	tuple							,					_a      :	int )	->    int:





       '''simple docstring'''






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

       if min_chunk_size >= self.max_chunk_size:
            return min_chunk_size

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

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

       return candidates[min_viable_chunk_size_index]





  def 						A  (		self      :	Dict							,					_a      :	Iterable							,					_a      :	Iterable )	->    bool:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =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 ):
                 _SCREAMING_SNAKE_CASE							    =[v for _, v in sorted(aa.items()							,					key=lambda _a : x[0] )]
                 _SCREAMING_SNAKE_CASE							    =[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 						A  (		self      :	List[str]							,					_a      :	Callable							,					_a      :	tuple							,					_a      :	int							,					)	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =True
       _SCREAMING_SNAKE_CASE							    =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 )
            _SCREAMING_SNAKE_CASE							    =self._compare_arg_caches(self.cached_arg_data							,					_a )
       else:
            # Otherwise, we can reuse the precomputed value
            _SCREAMING_SNAKE_CASE							    =False

       if not consistent:
            _SCREAMING_SNAKE_CASE							    =self._determine_favorable_chunk_size(
                _a							,					_a							,					_a							,					)
            _SCREAMING_SNAKE_CASE							    =arg_data

       assert self.cached_chunk_size is not None

       return self.cached_chunk_size



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      Any					 =     {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     ["FunnelTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[str]					 =     [
         "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FunnelBaseModel",
         "FunnelForMaskedLM",
         "FunnelForMultipleChoice",
         "FunnelForPreTraining",
         "FunnelForQuestionAnswering",
         "FunnelForSequenceClassification",
         "FunnelForTokenClassification",
         "FunnelModel",
         "FunnelPreTrainedModel",
         "load_tf_weights_in_funnel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFunnelBaseModel",
         "TFFunnelForMaskedLM",
         "TFFunnelForMultipleChoice",
         "TFFunnelForPreTraining",
         "TFFunnelForQuestionAnswering",
         "TFFunnelForSequenceClassification",
         "TFFunnelForTokenClassification",
         "TFFunnelModel",
         "TFFunnelPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_funnel_fast import FunnelTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_funnel import (
              FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              FunnelBaseModel,
              FunnelForMaskedLM,
              FunnelForMultipleChoice,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForSequenceClassification,
              FunnelForTokenClassification,
              FunnelModel,
              FunnelPreTrainedModel,
              load_tf_weights_in_funnel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_funnel import (
              TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFunnelBaseModel,
              TFFunnelForMaskedLM,
              TFFunnelForMultipleChoice,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForSequenceClassification,
              TFFunnelForTokenClassification,
              TFFunnelModel,
              TFFunnelPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      Optional[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
