


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     ( SCREAMING_SNAKE_CASE		: str   ):







 """simple docstring"""



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

 return shapes

@torch.jit.ignore
def 	_a     ( SCREAMING_SNAKE_CASE		: Dict			,       SCREAMING_SNAKE_CASE		: Union[str, Any]   ):







 """simple docstring"""



 UpperCamelCase__    :			Union[str, Any]      				=      []
 for d in reversed(SCREAMING_SNAKE_CASE   ):
  idx.append(flat_idx % d   )
  UpperCamelCase__    :			Optional[Any]      				=      flat_idx // d

 return tuple(reversed(SCREAMING_SNAKE_CASE   )   )

@torch.jit.ignore
def 	_a     ( SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: List[Any]			,       SCREAMING_SNAKE_CASE		: Tuple			,       SCREAMING_SNAKE_CASE		: List[str] = None			,       SCREAMING_SNAKE_CASE		: str = None			,       ):







 """simple docstring"""



 def reduce_edge_list(SCREAMING_SNAKE_CASE		: Tuple   ) -> None:
  UpperCamelCase__    :			List[str]      				=      True
  for i in range(len(SCREAMING_SNAKE_CASE   )   ):
   UpperCamelCase__    :			str      				=      -1 * (i + 1)
   l[reversed_idx] &= tally
   UpperCamelCase__    :			List[Any]      				=      l[reversed_idx]

 if start_edges is None:
  UpperCamelCase__    :			Optional[Any]      				=      [s == 0 for s in start]
  reduce_edge_list(SCREAMING_SNAKE_CASE   )
 if end_edges is None:
  UpperCamelCase__    :			List[str]      				=      [e == (d - 1) for e, d in zip(SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE   )]
  reduce_edge_list(SCREAMING_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(SCREAMING_SNAKE_CASE   ) == 0:
  return [()]
 elif len(SCREAMING_SNAKE_CASE   ) == 1:
  return [(slice(start[0]			,       end[0] + 1   ),)]

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

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

 UpperCamelCase__    :			Tuple[slice, ...]      				=      tuple(SCREAMING_SNAKE_CASE   )
 UpperCamelCase__    :			Dict      				=      len(SCREAMING_SNAKE_CASE   )

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

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

  UpperCamelCase__    :			Union[str, Any]      				=      start[divergence_idx]
  return tuple(
      path + (slice(SCREAMING_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

  UpperCamelCase__    :			Optional[int]      				=      end[divergence_idx]
  return tuple(
      path + (slice(SCREAMING_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()   )
  UpperCamelCase__    :			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     ( SCREAMING_SNAKE_CASE		: Tuple			,       SCREAMING_SNAKE_CASE		: Union[str, Any]			,       SCREAMING_SNAKE_CASE		: int			,       SCREAMING_SNAKE_CASE		: Any   ):







 """simple docstring"""



 UpperCamelCase__    :			str      				=      t.shape[:no_batch_dims]
 UpperCamelCase__    :			Optional[int]      				=      list(_flat_idx_to_idx(SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE   )   )
 # _get_minimal_slice_set is inclusive
 UpperCamelCase__    :			Dict      				=      list(_flat_idx_to_idx(flat_end - 1			,       SCREAMING_SNAKE_CASE   )   )

 # Get an ordered list of slices to perform
 UpperCamelCase__    :			Optional[Any]      				=      _get_minimal_slice_set(
     SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE			,       )

 UpperCamelCase__    :			Optional[Any]      				=      [t[s] for s in slices]

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

def 	_a     ( SCREAMING_SNAKE_CASE		: Union[str, Any]			,       SCREAMING_SNAKE_CASE		: int			,       SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: int = False			,       SCREAMING_SNAKE_CASE		: Optional[int] = None			,       SCREAMING_SNAKE_CASE		: Dict = False			,       ):







 """simple docstring"""



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

 UpperCamelCase__    :			str      				=      [shape[:no_batch_dims] for shape in _fetch_dims(SCREAMING_SNAKE_CASE   )]
 UpperCamelCase__    :			str      				=      tuple([max(SCREAMING_SNAKE_CASE   ) for s in zip(*SCREAMING_SNAKE_CASE   )]   )

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

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

 UpperCamelCase__    :			str      				=      1
 for d in orig_batch_dims:
  flat_batch_dim *= d

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

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

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

  UpperCamelCase__    :			Dict[str, Any]      				=      tensor_tree_map(SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE   )

  # Run the layer on the chunk
  UpperCamelCase__    :			Optional[int]      				=      layer(**SCREAMING_SNAKE_CASE   )

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

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

   def assign(SCREAMING_SNAKE_CASE		: Optional[Any]			,       SCREAMING_SNAKE_CASE		: Dict   ) -> None:
    for k, v in da.items():
     if isinstance(SCREAMING_SNAKE_CASE			,       SCREAMING_SNAKE_CASE   ):
      assign(SCREAMING_SNAKE_CASE			,       da[k]   )
     else:
      if _add_into_out:
       v[i : i + chunk_size] += da[k]
      else:
       UpperCamelCase__    :			int      				=      da[k]

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

  i += chunk_size

 UpperCamelCase__    :			List[str]      				=      tensor_tree_map(lambda SCREAMING_SNAKE_CASE   : t.view(orig_batch_dims + t.shape[1:]   )			,       SCREAMING_SNAKE_CASE   )

 return out






class 					__magic_name__		:


     def __init__(					self							:   int							,		lowerCamelCase__							:   Dict = 512							,		)     ->					Optional[int]:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[Any]      				=      max_chunk_size
      UpperCamelCase__    :			Optional[int]      				=      None
      UpperCamelCase__    :			Optional[tuple]      				=      None


     def    UpperCAmelCase__		(					self							:   Tuple							,		lowerCamelCase__							:   List[Any]							,		lowerCamelCase__							:   Union[str, Any]							,		lowerCamelCase__							:   Union[str, Any]	)     ->					int:





      '''simple docstring'''






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

      if min_chunk_size >= self.max_chunk_size:
       return min_chunk_size

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

      def test_chunk_size(lowerCamelCase__							:   Optional[Any]	) -> bool:
       try:
        with torch.no_grad():
         fn(*__lowerCAmelCase							,		chunk_size=__lowerCAmelCase	)
        return True
       except RuntimeError:
        return False

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

      return candidates[min_viable_chunk_size_index]


     def    UpperCAmelCase__		(					self							:   Optional[Any]							,		lowerCamelCase__							:   List[str]							,		lowerCamelCase__							:   Union[str, Any]	)     ->					bool:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[Any]      				=      True
      for aa, aa in zip(__lowerCAmelCase							,		__lowerCAmelCase	):
       assert type(__lowerCAmelCase	) == type(__lowerCAmelCase	)
       if isinstance(__lowerCAmelCase							,		(list, tuple)	):
        consistent &= self._compare_arg_caches(__lowerCAmelCase							,		__lowerCAmelCase	)
       elif isinstance(__lowerCAmelCase							,		__lowerCAmelCase	):
        UpperCamelCase__    :			Dict      				=      [v for _, v in sorted(aa.items()							,		key=lambda lowerCamelCase__	: x[0]	)]
        UpperCamelCase__    :			Dict      				=      [v for _, v in sorted(aa.items()							,		key=lambda lowerCamelCase__	: x[0]	)]
        consistent &= self._compare_arg_caches(__lowerCAmelCase							,		__lowerCAmelCase	)
       else:
        consistent &= aa == aa

      return consistent


     def    UpperCAmelCase__		(					self							:   Optional[int]							,		lowerCamelCase__							:   Any							,		lowerCamelCase__							:   Optional[int]							,		lowerCamelCase__							:   int							,		)     ->					int:





      '''simple docstring'''






      UpperCamelCase__    :			str      				=      True
      UpperCamelCase__    :			tuple      				=      tree_map(lambda lowerCamelCase__	: a.shape if isinstance(__lowerCAmelCase							,		torch.Tensor	) else a							,		__lowerCAmelCase							,		__lowerCAmelCase	)
      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(__lowerCAmelCase	)
       UpperCamelCase__    :			int      				=      self._compare_arg_caches(self.cached_arg_data							,		__lowerCAmelCase	)
      else:
       # Otherwise, we can reuse the precomputed value
       UpperCamelCase__    :			Optional[int]      				=      False

      if not consistent:
       UpperCamelCase__    :			int      				=      self._determine_favorable_chunk_size(
           __lowerCAmelCase							,		__lowerCAmelCase							,		__lowerCAmelCase							,		)
       UpperCamelCase__    :			Union[str, Any]      				=      arg_data

      assert self.cached_chunk_size is not None

      return self.cached_chunk_size



'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					LongformerForQuestionAnswering.from_pretrained(UpperCAmelCase     )

 # transfer weights
 longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()     )
 longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()     )
 longformer_for_qa.eval()

 # save model
 longformer_for_qa.save_pretrained(UpperCAmelCase     )

 print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""     )


if __name__ == "__main__":
  __a: List[Any]          =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

