






from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
 import tensorflow as tf

 from ..tf_utils import stable_softmax


if is_torch_available():
 import torch


lowerCamelCase	:							List[Any]				=logging.get_logger(__name__)





@add_end_docstrings(
    A__       ,   r'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''       ,   )
class 			__a  (    A__       ):







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   GenericTensor						):


      '''simple docstring'''






      if self.framework == "tf":
           UpperCamelCase__			:   Any       				=  tf.where(input_ids == self.tokenizer.mask_token_id						).numpy()
      elif self.framework == "pt":
           UpperCamelCase__			:   Any       				=  torch.nonzero(input_ids == self.tokenizer.mask_token_id							,   as_tuple=SCREAMING_SNAKE_CASE						)
      else:
           raise ValueError("Unsupported framework"						)
      return masked_index







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   GenericTensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.get_masked_index(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  np.prod(masked_index.shape						)
      if numel < 1:
           raise PipelineException(
               "fill-mask"							,   self.model.base_model_prefix							,   F'No mask_token ({self.tokenizer.mask_token}) found on the input'							,   )







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   GenericTensor						):


      '''simple docstring'''






      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           for model_input in model_inputs:
                self._ensure_exactly_one_mask_token(model_input["input_ids"][0]						)
      else:
           for input_ids in model_inputs["input_ids"]:
                self._ensure_exactly_one_mask_token(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   int=None							,   **SCREAMING_SNAKE_CASE					:   str						):


      '''simple docstring'''






      if return_tensors is None:
           UpperCamelCase__			:   Optional[int]       				=  self.framework
      UpperCamelCase__			:   str       				=  self.tokenizer(SCREAMING_SNAKE_CASE							,   return_tensors=SCREAMING_SNAKE_CASE						)
      self.ensure_exactly_one_mask_token(SCREAMING_SNAKE_CASE						)
      return model_inputs







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.model(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  model_inputs["input_ids"]
      return model_outputs







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   str=5							,   SCREAMING_SNAKE_CASE					:   List[str]=None						):


      '''simple docstring'''






      if target_ids is not None and target_ids.shape[0] < top_k:
           UpperCamelCase__			:   Dict       				=  target_ids.shape[0]
      UpperCamelCase__			:   int       				=  model_outputs["input_ids"][0]
      UpperCamelCase__			:   Tuple       				=  model_outputs["logits"]

      if self.framework == "tf":
           UpperCamelCase__			:   int       				=  tf.where(input_ids == self.tokenizer.mask_token_id						).numpy()[:, 0]

           UpperCamelCase__			:   int       				=  outputs.numpy()

           UpperCamelCase__			:   int       				=  outputs[0, masked_index, :]
           UpperCamelCase__			:   Tuple       				=  stable_softmax(SCREAMING_SNAKE_CASE							,   axis=-1						)
           if target_ids is not None:
                UpperCamelCase__			:   List[str]       				=  tf.gather_nd(tf.squeeze(SCREAMING_SNAKE_CASE							,   0						)							,   target_ids.reshape(-1							,   1						)						)
                UpperCamelCase__			:   Union[str, Any]       				=  tf.expand_dims(SCREAMING_SNAKE_CASE							,   0						)

           UpperCamelCase__			:   int       				=  tf.math.top_k(SCREAMING_SNAKE_CASE							,   k=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__				,     UpperCamelCase__			:   int       				=  topk.values.numpy(), topk.indices.numpy()
      else:
           UpperCamelCase__			:   Dict       				=  torch.nonzero(input_ids == self.tokenizer.mask_token_id							,   as_tuple=SCREAMING_SNAKE_CASE						).squeeze(-1						)
           # Fill mask pipeline supports only one ${mask_token} per sample

           UpperCamelCase__			:   int       				=  outputs[0, masked_index, :]
           UpperCamelCase__			:   Dict       				=  logits.softmax(dim=-1						)
           if target_ids is not None:
                UpperCamelCase__			:   Union[str, Any]       				=  probs[..., target_ids]

           UpperCamelCase__				,     UpperCamelCase__			:   int       				=  probs.topk(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   str       				=  []
      UpperCamelCase__			:   List[Any]       				=  values.shape[0] == 1
      for i, (_values, _predictions) in enumerate(zip(values.tolist()							,   predictions.tolist()						)						):
           UpperCamelCase__			:   Optional[Any]       				=  []
           for v, p in zip(_values							,   _predictions						):
                # Copy is important since we're going to modify this array in place
                UpperCamelCase__			:   Optional[int]       				=  input_ids.numpy().copy()
                if target_ids is not None:
                     UpperCamelCase__			:   Any       				=  target_ids[p].tolist()

                UpperCamelCase__			:   Dict       				=  p
                # Filter padding out:
                UpperCamelCase__			:   Optional[Any]       				=  tokens[np.where(tokens != self.tokenizer.pad_token_id						)]
                # Originally we skip special tokens to give readable output.
                # For multi masks though, the other [MASK] would be removed otherwise
                # making the output look odd, so we add them back
                UpperCamelCase__			:   str       				=  self.tokenizer.decode(SCREAMING_SNAKE_CASE							,   skip_special_tokens=SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   List[str]       				=  {"score": v, "token": p, "token_str": self.tokenizer.decode([p]						), "sequence": sequence}
                row.append(SCREAMING_SNAKE_CASE						)
           result.append(SCREAMING_SNAKE_CASE						)
      if single_mask:
           return result[0]
      return result







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple=None						):


      '''simple docstring'''






      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Optional[Any]       				=  [targets]
      try:
           UpperCamelCase__			:   Any       				=  self.tokenizer.get_vocab()
      except Exception:
           UpperCamelCase__			:   Dict       				=  {}
      UpperCamelCase__			:   Any       				=  []
      for target in targets:
           UpperCamelCase__			:   List[Any]       				=  vocab.get(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           if id_ is None:
                UpperCamelCase__			:   List[Any]       				=  self.tokenizer(
                    SCREAMING_SNAKE_CASE							,   add_special_tokens=SCREAMING_SNAKE_CASE							,   return_attention_mask=SCREAMING_SNAKE_CASE							,   return_token_type_ids=SCREAMING_SNAKE_CASE							,   max_length=1							,   truncation=SCREAMING_SNAKE_CASE							,   )["input_ids"]
                if len(SCREAMING_SNAKE_CASE						) == 0:
                     logger.warning(
                         F'The specified target token `{target}` does not exist in the model vocabulary. '
                         "We cannot replace it with anything meaningful, ignoring it"						)
                     continue
                UpperCamelCase__			:   List[str]       				=  input_ids[0]
                # XXX: If users encounter this pass
                # it becomes pretty slow, so let's make sure
                # The warning enables them to fix the input to
                # get faster performance.
                logger.warning(
                    F'The specified target token `{target}` does not exist in the model vocabulary. '
                    F'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_						)}`.'						)
           target_ids.append(id_						)
      UpperCamelCase__			:   Union[str, Any]       				=  list(set(SCREAMING_SNAKE_CASE						)						)
      if len(SCREAMING_SNAKE_CASE						) == 0:
           raise ValueError("At least one target must be provided when passed."						)
      UpperCamelCase__			:   str       				=  np.array(SCREAMING_SNAKE_CASE						)
      return target_ids







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=None						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  {}

      if targets is not None:
           UpperCamelCase__			:   Optional[Any]       				=  self.get_target_ids(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   int       				=  target_ids

      if top_k is not None:
           UpperCamelCase__			:   str       				=  top_k

      if self.tokenizer.mask_token_id is None:
           raise PipelineException(
               "fill-mask"							,   self.model.base_model_prefix							,   "The tokenizer does not define a `mask_token`."						)
      return {}, {}, postprocess_params







 def __call__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   *SCREAMING_SNAKE_CASE					:   int							,   **SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  super().__call__(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) and len(SCREAMING_SNAKE_CASE						) == 1:
           return outputs[0]
      return outputs







from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowerCamelCase	:							Tuple				=_symbol_database.Default()


lowerCamelCase	:							List[str]				=_descriptor_pool.Default().AddSerializedFile(
    b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

lowerCamelCase	:							str				=globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
 lowerCamelCase	:							Optional[int]				=None
 lowerCamelCase	:							Tuple				=b'''H\003'''
 # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
 # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
 # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
 # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
 # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
 lowerCamelCase	:							List[str]				=45
 lowerCamelCase	:							List[Any]				=1581
 lowerCamelCase	:							Optional[int]				=1517
 lowerCamelCase	:							Tuple				=1570
 lowerCamelCase	:							Dict				=1584
 lowerCamelCase	:							Optional[Any]				=1793
 lowerCamelCase	:							Dict				=1795
 lowerCamelCase	:							Any				=1916
 lowerCamelCase	:							Dict				=1864
 lowerCamelCase	:							Dict				=1905
 lowerCamelCase	:							Dict				=1919
 lowerCamelCase	:							Union[str, Any]				=2429
 lowerCamelCase	:							List[Any]				=2208
 lowerCamelCase	:							List[Any]				=2418
 lowerCamelCase	:							List[str]				=2323
 lowerCamelCase	:							Dict				=2407
# @@protoc_insertion_point(module_scope)