





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 ):
   a__						:    List[str]   		=	[]
   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 ):
   a__						:    Optional[Any]   		=	[]
   for d in reversed(lowerCamelCase ):
      idx.append(flat_idx % d )
      a__						:    Tuple   		=	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:
      a__						:    List[str]   		=	True
      for i in range(len(lowerCamelCase ) ):
         a__						:    List[Any]   		=	-1 * (i + 1)
         l[reversed_idx] &= tally
         a__						:    List[Any]   		=	l[reversed_idx]

   if start_edges is None:
      a__						:    List[Any]   		=	[s == 0 for s in start]
      reduce_edge_list(lowerCamelCase )
   if end_edges is None:
      a__						:    List[str]   		=	[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 ),)]

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

   # 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

   a__						:    Tuple[slice, ...]   		=	tuple(lowerCamelCase )
   a__						:    List[Any]   		=	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

      a__						:    Any   		=	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

      a__						:    List[str]   		=	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() )
      a__						:    Tuple   		=	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 ):
   a__						:    str   		=	t.shape[:no_batch_dims]
   a__						:    Optional[Any]   		=	list(_flat_idx_to_idx(lowerCamelCase				,			lowerCamelCase ) )
   # _get_minimal_slice_set is inclusive
   a__						:    Tuple   		=	list(_flat_idx_to_idx(flat_end - 1				,			lowerCamelCase ) )

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

   a__						:    Optional[int]   		=	[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" )

   a__						:    List[Any]   		=	[shape[:no_batch_dims] for shape in _fetch_dims(lowerCamelCase )]
   a__						:    int   		=	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:
            a__						:    int   		=	t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
         a__						:    int   		=	t.reshape(-1				,			*t.shape[no_batch_dims:] )
      else:
         a__						:    Optional[Any]   		=	t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
      return t

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

   a__						:    Tuple   		=	1
   for d in orig_batch_dims:
      flat_batch_dim *= d

   a__						:    Any   		=	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

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

      a__						:    Dict[str, Any]   		=	tensor_tree_map(lowerCamelCase				,			lowerCamelCase )

      # Run the layer on the chunk
      a__						:    Optional[int]   		=	layer(**lowerCamelCase )

      # Allocate space for the output
      if out is None:
         a__						:    List[Any]   		=	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:
                     a__						:    int   		=	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:
               a__						:    List[str]   		=	xa
      elif isinstance(lowerCamelCase				,			torch.Tensor ):
         if _add_into_out:
            out[i : i + chunk_size] += output_chunk
         else:
            a__						:    str   		=	output_chunk
      else:
         raise ValueError("Not supported" )

      i += chunk_size

   a__						:    List[Any]   		=	tensor_tree_map(lambda lowerCamelCase : t.view(orig_batch_dims + t.shape[1:] )				,			lowerCamelCase )

   return out







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case = 512       ,      )					->	Optional[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	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__						:    List[str]   		=	[c for c in candidates if c > min_chunk_size]
      a__						:    Optional[int]   		=	[min_chunk_size] + candidates
      candidates[-1] += 4

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

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

      return candidates[min_viable_chunk_size_index]





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	bool:
      """simple docstring"""





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

      return consistent





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      )					->	int:
      """simple docstring"""





      a__						:    Dict   		=	True
      a__						:    tuple   		=	tree_map(lambda snake_case   : a.shape if isinstance(snake_case       ,      torch.Tensor   ) else a       ,      snake_case       ,      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(snake_case   )
         a__						:    Dict   		=	self._compare_arg_caches(self.cached_arg_data       ,      snake_case   )
      else:
         # Otherwise, we can reuse the precomputed value
         a__						:    Dict   		=	False

      if not consistent:
         a__						:    Optional[int]   		=	self._determine_favorable_chunk_size(
             snake_case       ,      snake_case       ,      snake_case       ,      )
         a__						:    Union[str, Any]   		=	arg_data

      assert self.cached_chunk_size is not None

      return self.cached_chunk_size








import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      int         =    """▁"""

SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    Any  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    Any  		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase			:    Tuple  		=					["""input_ids""", """attention_mask"""]





   def __init__(			self       ,      snake_case       ,      snake_case="</s>"       ,      snake_case="<unk>"       ,      snake_case=[]       ,      snake_case = None       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      a__						:    Optional[Any]   		=	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=snake_case       ,      unk_token=snake_case       ,      additional_special_tokens=snake_case       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **snake_case       ,      )

      a__						:    Union[str, Any]   		=	vocab_file
      a__						:    Optional[Any]   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(snake_case   )





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.sp_model.get_piece_size()





   def 						_snake_case			(			self   )					->	Dict[str, int]:
      """simple docstring"""





      a__						:    Dict   		=	{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





   def __getstate__(			self   )					->	Any:
      """simple docstring"""





      a__						:    Tuple   		=	self.__dict__.copy()
      a__						:    Optional[int]   		=	None
      return state





   def __setstate__(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	d

      # for backward compatibility
      if not hasattr(self       ,      "sp_model_kwargs"   ):
         a__						:    List[Any]   		=	{}

      a__						:    int   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      return self.sp_model.encode(snake_case       ,      out_type=snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      return self.sp_model.piece_to_id(snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      if index < self.sp_model.get_piece_size():
         a__						:    Optional[Any]   		=	self.sp_model.IdToPiece(snake_case   )
      return token





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	[]
      a__						:    Dict   		=	""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(snake_case   ) + token
            a__						:    Dict   		=	[]
         else:
            current_sub_tokens.append(snake_case   )
      out_string += self.sp_model.decode(snake_case   )
      return out_string.strip()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





      if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
      a__						:    Tuple   		=	os.path.join(
          snake_case       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file       ,      snake_case   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case       ,      "wb"   ) as fi:
            a__						:    str   		=	self.sp_model.serialized_model_proto()
            fi.write(snake_case   )

      return (out_vocab_file,)

