

'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_snake_case         =  logging.get_logger(__name__)







def  _A		(     snake_case					)      ->     List[int]:
   if isinstance(snake_case		,							np.ndarray					):
      return list(tensor.shape					)

   _lowercase      :				Tuple      					=       tf.shape(snake_case					)

   if tensor.shape == tf.TensorShape(snake_case					):
      return dynamic

   _lowercase      :				Any      					=       tensor.shape.as_list()

   return [dynamic[i] if s is None else s for i, s in enumerate(snake_case					)]







def  _A		(     snake_case		,							snake_case = None		,							snake_case = None					)      ->     tf.Tensor:
   return tf.nn.softmax(logits=logits + 1E-9		,							axis=snake_case		,							name=snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case=1E-5		,							snake_case=-1					)      ->     str:
   # This is a very simplified functional layernorm, designed to duplicate
   # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
   # models in Transformers.

   if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case		,							snake_case					):
      raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."					)

   # Get mean and variance on the axis to be normalized
   _lowercase					,				_lowercase      :				Any      					=       tf.nn.moments(snake_case		,							axes=[axis]		,							keepdims=snake_case					)

   if axis != -1:
      # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
      # on every dimension except axis
      _lowercase      :				Optional[int]      					=       [1] * inputs.shape.rank
      _lowercase      :				Tuple      					=       shape_list(snake_case					)[axis]
      _lowercase      :				Any      					=       tf.reshape(snake_case		,							snake_case					)
      _lowercase      :				str      					=       tf.reshape(snake_case		,							snake_case					)

   # Compute layer normalization using the batch_normalization
   # function.
   _lowercase      :				Any      					=       tf.nn.batch_normalization(
       snake_case		,							snake_case		,							snake_case		,							offset=snake_case		,							scale=snake_case		,							variance_epsilon=snake_case		,							)
   return outputs







def  _A		(     snake_case		,							snake_case=0		,							snake_case=-1					)      ->     Union[str, Any]:
   # Replicates the behavior of torch.flatten in TF

   # If end_dim or start_dim is negative, count them from the end
   if end_dim < 0:
      end_dim += input.shape.rank
   if start_dim < 0:
      start_dim += input.shape.rank

   if start_dim == end_dim:
      return input

   _lowercase      :				Union[str, Any]      					=       tf.shape(snake_case					)
   _lowercase      :				Any      					=       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
   _lowercase      :				Optional[int]      					=       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,							axis=0					)
   return tf.reshape(snake_case		,							snake_case					)







def  _A		(     snake_case					)      ->     tf.Tensor:
   if not isinstance(snake_case		,							tf.Tensor					):
      _lowercase      :				Optional[Any]      					=       tf.convert_to_tensor(snake_case					)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      _lowercase      :				Dict      					=       encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      _lowercase      :				Tuple      					=       encoder_attention_mask[:, None, None, :]
   # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
   # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
   # /transformer/transformer_layers.py#L270
   # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
   # encoder_extended_attention_mask.transpose(-1, -2))
   _lowercase      :				Dict      					=       (
       tf.cast(1		,							encoder_attention_mask.dtype					) - encoder_extended_attention_mask
   ) * encoder_extended_attention_mask.dtype.min

   return encoder_extended_attention_mask







def  _A		(     snake_case		,							snake_case		,							snake_case = "input_ids"					)      ->     None:
   tf.debugging.assert_less(
       snake_case		,							tf.cast(snake_case		,							dtype=tensor.dtype					)		,							message=(
           F'''The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case					)}) must be smaller than the embedding '''
           F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
       )		,							)







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Optional[Any]:
   _lowercase      :				Optional[int]      					=       6_45_12
   # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
   # because in that case even chunking the array would not make the saving
   # possible.
   _lowercase      :				Union[str, Any]      					=       [x for x in data if len(snake_case					) > HDF5_OBJECT_HEADER_LIMIT]

   # Expecting this to never be true.
   if bad_attributes:
      raise RuntimeError(
          "The following attributes cannot be saved to HDF5 file because "
          F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
          F'''bytes: {bad_attributes}'''					)

   _lowercase      :				Dict      					=       np.asarray(snake_case					)

   _lowercase      :				str      					=       1
   _lowercase      :				Union[str, Any]      					=       np.array_split(snake_case		,							snake_case					)

   # This will never loop forever thanks to the test above.
   while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data					):
      num_chunks += 1
      _lowercase      :				int      					=       np.array_split(snake_case		,							snake_case					)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(snake_case					):
         _lowercase      :				Optional[int]      					=       chunk_data
   else:
      _lowercase      :				List[str]      					=       data







def  _A		(     snake_case		,							snake_case					)      ->     int:
   if name in group.attrs:
      _lowercase      :				Any      					=       [n.decode("utf8"					) if hasattr(snake_case		,							"decode"					) else n for n in group.attrs[name]]
   else:
      _lowercase      :				Union[str, Any]      					=       []
      _lowercase      :				List[Any]      					=       0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode("utf8"					) if hasattr(snake_case		,							"decode"					) else n for n in group.attrs["%s%d" % (name, chunk_id)]]					)
         chunk_id += 1
   return data







def  _A		(     snake_case					)      ->     Tuple:

   def _expand_single_ad_tensor(snake_case					):
      if isinstance(snake_case		,							tf.Tensor					) and t.shape.rank == 1:
         return tf.expand_dims(snake_case		,							axis=-1					)
      return t

   return tf.nest.map_structure(_expand_single_ad_tensor		,							snake_case					)









'''simple docstring'''
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


_snake_case         =  '▁'

_snake_case         =  {'vocab_file': 'spiece.model'}

_snake_case         =  {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

_snake_case         =  {
    'google/pegasus-xsum': 512,
}


_snake_case         =  logging.get_logger(__name__)


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							VOCAB_FILES_NAMES

   _SCREAMING_SNAKE_CASE   :					Tuple     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Any     	=							PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['input_ids', 'attention_mask']


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase="<pad>"      ,    _UpperCamelCase="</s>"      ,    _UpperCamelCase="<unk>"      ,    _UpperCamelCase="<mask_2>"      ,    _UpperCamelCase="<mask_1>"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=103      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       offset
      if additional_special_tokens is not None:
         if not isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
            raise TypeError(
                f'''additional_special_tokens should be of type {type(_UpperCamelCase  )}, but is'''
                f''' {type(_UpperCamelCase  )}'''  )

         _lowercase      :				Dict      					=       (
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f'''<unk_{i}>''' for i in range(len(_UpperCamelCase  )      ,    self.offset - 1  )
         ]

         if len(set(_UpperCamelCase  )  ) != len(_UpperCamelCase  ):
            raise ValueError(
                "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''  )
         _lowercase      :				List[str]      					=       additional_special_tokens_extended
      else:
         _lowercase      :				Dict      					=       [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'''<unk_{i}>''' for i in range(2      ,    self.offset  )]

      _lowercase      :				int      					=       {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=_UpperCamelCase      ,    unk_token=_UpperCamelCase      ,    mask_token=_UpperCamelCase      ,    pad_token=_UpperCamelCase      ,    mask_token_sent=_UpperCamelCase      ,    offset=_UpperCamelCase      ,    additional_special_tokens=_UpperCamelCase      ,    sp_model_kwargs=self.sp_model_kwargs      ,    **_UpperCamelCase      ,    )
      _lowercase      :				Union[str, Any]      					=       mask_token_sent
      _lowercase      :				str      					=       vocab_file
      _lowercase      :				Dict      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(_UpperCamelCase  )

      # add special tokens to encoder dict
      _lowercase      :				Dict[int, str]      					=       {
          0: self.pad_token,
          1: self.eos_token,
      }

      if self.mask_token_sent is not None:
         self.encoder.update(
             {
                 2: self.mask_token_sent,
                 3: self.mask_token,
             }  )

      if self.offset > 0:
         # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
         # mask_token_sent is already added to list -> so start at 1
         self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,    self.offset - 1  )}  )

      _lowercase      :				Dict[str, int]      					=       {v: k for k, v in self.encoder.items()}


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return len(self.sp_model  ) + self.offset


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       {self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab


   def __getstate__(			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.__dict__.copy()
      _lowercase      :				Union[str, Any]      					=       None
      return state


   def __setstate__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       d

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

      _lowercase      :				Any      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if token in self.decoder:
         return self.decoder[token]
      elif token in self.added_tokens_decoder:
         return self.added_tokens_decoder[token]
      _lowercase      :				int      					=       self.sp_model.piece_to_id(_UpperCamelCase  )
      return sp_id + self.offset


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if index in self.encoder:
         return self.encoder[index]
      elif index in self.added_tokens_encoder:
         return self.added_tokens_encoder[index]
      else:
         _lowercase      :				Any      					=       self.sp_model.IdToPiece(index - self.offset  )
      return token


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       []
      _lowercase      :				Optional[Any]      					=       ""
      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(_UpperCamelCase  ) + token
            _lowercase      :				Tuple      					=       []
         else:
            current_sub_tokens.append(_UpperCamelCase  )
      out_string += self.sp_model.decode(_UpperCamelCase  )
      return out_string.strip()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase=False  ):





      """simple docstring"""





      return 1


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       set(self.all_special_ids  )  # call it once instead of inside list comp
      all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

      return [1 if x in all_special_ids else 0 for x in seq]


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False  ):





      """simple docstring"""





      if already_has_special_tokens:
         return self._special_token_mask(_UpperCamelCase  )
      elif token_ids_a is None:
         return self._special_token_mask(_UpperCamelCase  ) + [1]
      else:
         return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





      if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





      if not os.path.isdir(_UpperCamelCase  ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
         return
      _lowercase      :				List[Any]      					=       os.path.join(
          _UpperCamelCase      ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(_UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
         copyfile(self.vocab_file      ,    _UpperCamelCase  )
      elif not os.path.isfile(self.vocab_file  ):
         with open(_UpperCamelCase      ,    "wb"  ) as fi:
            _lowercase      :				Optional[int]      					=       self.sp_model.serialized_model_proto()
            fi.write(_UpperCamelCase  )

      return (out_vocab_file,)






