
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		):
   SCREAMING_SNAKE_CASE_     =	[]
   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       A__      (    __lowerCamelCase,   __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	[]
   for d in reversed(__lowerCamelCase		):
      idx.append(flat_idx % d		)
      SCREAMING_SNAKE_CASE_     =	flat_idx // d

   return tuple(reversed(__lowerCamelCase		)		)


@torch.jit.ignore
def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase = None,   __lowerCamelCase = 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		) -> None:
      SCREAMING_SNAKE_CASE_     =	True
      for i in range(len(__lowerCamelCase		)		):
         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(__lowerCamelCase		)
   if end_edges is None:
      SCREAMING_SNAKE_CASE_     =	[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		),)]

   SCREAMING_SNAKE_CASE_     =	[]
   SCREAMING_SNAKE_CASE_     =	[]

   # 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

   SCREAMING_SNAKE_CASE_     =	tuple(__lowerCamelCase		)
   SCREAMING_SNAKE_CASE_     =	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

      SCREAMING_SNAKE_CASE_     =	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

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

   # Get an ordered list of slices to perform
   SCREAMING_SNAKE_CASE_     =	_get_minimal_slice_set(
       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   )

   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       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase = False,   __lowerCamelCase = None,   __lowerCamelCase = False,   ):
   if not (len(__lowerCamelCase		) > 0):
      raise ValueError('''Must provide at least one input'''		)

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

   def _prep_inputs(__lowerCamelCase		) -> 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,   __lowerCamelCase		)
   SCREAMING_SNAKE_CASE_     =	None
   if _out is not None:
      SCREAMING_SNAKE_CASE_     =	tensor_tree_map(lambda __lowerCamelCase		: 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(__lowerCamelCase		) -> 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(__lowerCamelCase		):
      # Chunk the input
      if not low_mem:
         SCREAMING_SNAKE_CASE_     =	_select_chunk
      else:
         SCREAMING_SNAKE_CASE_     =	partial(
             _chunk_slice,   flat_start=__lowerCamelCase,   flat_end=min(__lowerCamelCase,   i + chunk_size		),   no_batch_dims=len(__lowerCamelCase		),   )

      SCREAMING_SNAKE_CASE_     =	tensor_tree_map(__lowerCamelCase,   __lowerCamelCase		)

      # Run the layer on the chunk
      SCREAMING_SNAKE_CASE_     =	layer(**__lowerCamelCase		)

      # Allocate space for the output
      if out is None:
         SCREAMING_SNAKE_CASE_     =	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,   __lowerCamelCase		) -> 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:
                     SCREAMING_SNAKE_CASE_     =	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:
               SCREAMING_SNAKE_CASE_     =	xa
      elif isinstance(__lowerCamelCase,   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 __lowerCamelCase		: t.view(orig_batch_dims + t.shape[1:]		),   __lowerCamelCase		)

   return out





class 							UpperCamelCase__						:






       """simple docstring"""


       def __init__(				self      ,			_A = 512      ,			)							->							Any:
          SCREAMING_SNAKE_CASE_     =	max_chunk_size
          SCREAMING_SNAKE_CASE_     =	None
          SCREAMING_SNAKE_CASE_     =	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

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

          SCREAMING_SNAKE_CASE_     =	0
          SCREAMING_SNAKE_CASE_     =	len(_SCREAMING_SNAKE_CASE     ) - 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(_SCREAMING_SNAKE_CASE     ) - 1) // 2

          return candidates[min_viable_chunk_size_index]



       def   _UpperCamelCase     (				self      ,			_A      ,			_A     )							->							bool:
          SCREAMING_SNAKE_CASE_     =	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     ):
                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(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE     )
             else:
                consistent &= aa == aa

          return consistent




       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A      ,			)							->							int:
          SCREAMING_SNAKE_CASE_     =	True
          SCREAMING_SNAKE_CASE_     =	tree_map(lambda _A     : 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     )
             SCREAMING_SNAKE_CASE_     =	self._compare_arg_caches(self.cached_arg_data      ,			_SCREAMING_SNAKE_CASE     )
          else:
             # Otherwise, we can reuse the precomputed value
             SCREAMING_SNAKE_CASE_     =	False

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

          assert self.cached_chunk_size is not None

          return self.cached_chunk_size






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




