


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


_A			=						logging.get_logger(__name__)






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



    """simple docstring"""






    def _a (      self ,       A_   )  ->	np.ndarray:
         if self.framework == "tf":
              __UpperCamelCase   							=tf.where(input_ids == self.tokenizer.mask_token_id   ).numpy()
         elif self.framework == "pt":
              __UpperCamelCase   							=torch.nonzero(input_ids == self.tokenizer.mask_token_id ,       as_tuple=A_   )
         else:
              raise ValueError('Unsupported framework'   )
         return masked_index






    def _a (      self ,       A_   )  ->	np.ndarray:
         __UpperCamelCase   							=self.get_masked_index(A_   )
         __UpperCamelCase   							=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 _a (      self ,       A_   )  ->	Optional[Any]:
         if isinstance(A_ ,       A_   ):
              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(A_   )






    def _a (      self ,       A_ ,       A_=None ,       **A_   )  ->	Dict[str, GenericTensor]:
         if return_tensors is None:
              __UpperCamelCase   							=self.framework
         __UpperCamelCase   							=self.tokenizer(A_ ,       return_tensors=A_   )
         self.ensure_exactly_one_mask_token(A_   )
         return model_inputs






    def _a (      self ,       A_   )  ->	Optional[Any]:
         __UpperCamelCase   							=self.model(**A_   )
         __UpperCamelCase   							=model_inputs['input_ids']
         return model_outputs






    def _a (      self ,       A_ ,       A_=5 ,       A_=None   )  ->	int:
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
              __UpperCamelCase   							=target_ids.shape[0]
         __UpperCamelCase   							=model_outputs['input_ids'][0]
         __UpperCamelCase   							=model_outputs['logits']

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

              __UpperCamelCase   							=outputs.numpy()

              __UpperCamelCase   							=outputs[0, masked_index, :]
              __UpperCamelCase   							=stable_softmax(A_ ,       axis=-1   )
              if target_ids is not None:
                   __UpperCamelCase   							=tf.gather_nd(tf.squeeze(A_ ,       0   ) ,       target_ids.reshape(-1 ,       1   )   )
                   __UpperCamelCase   							=tf.expand_dims(A_ ,       0   )

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

              __UpperCamelCase   							=outputs[0, masked_index, :]
              __UpperCamelCase   							=logits.softmax(dim=-1   )
              if target_ids is not None:
                   __UpperCamelCase   							=probs[..., target_ids]

              __UpperCamelCase							,				__UpperCamelCase   							=probs.topk(A_   )

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

                   __UpperCamelCase   							=p
                   # Filter padding out:
                   __UpperCamelCase   							=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   							=self.tokenizer.decode(A_ ,       skip_special_tokens=A_   )
                   __UpperCamelCase   							={'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]   ), 'sequence': sequence}
                   row.append(A_   )
              result.append(A_   )
         if single_mask:
              return result[0]
         return result






    def _a (      self ,       A_ ,       A_=None   )  ->	Any:
         if isinstance(A_ ,       A_   ):
              __UpperCamelCase   							=[targets]
         try:
              __UpperCamelCase   							=self.tokenizer.get_vocab()
         except Exception:
              __UpperCamelCase   							={}
         __UpperCamelCase   							=[]
         for target in targets:
              __UpperCamelCase   							=vocab.get(A_ ,       A_   )
              if id_ is None:
                   __UpperCamelCase   							=self.tokenizer(
                       A_ ,       add_special_tokens=A_ ,       return_attention_mask=A_ ,       return_token_type_ids=A_ ,       max_length=1 ,       truncation=A_ ,       )['input_ids']
                   if len(A_   ) == 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   							=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   							=list(set(A_   )   )
         if len(A_   ) == 0:
              raise ValueError('At least one target must be provided when passed.'   )
         __UpperCamelCase   							=np.array(A_   )
         return target_ids






    def _a (      self ,       A_=None ,       A_=None   )  ->	Optional[Any]:
         __UpperCamelCase   							={}

         if targets is not None:
              __UpperCamelCase   							=self.get_target_ids(A_ ,       A_   )
              __UpperCamelCase   							=target_ids

         if top_k is not None:
              __UpperCamelCase   							=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 ,       A_ ,       *A_ ,       **A_   )  ->	Tuple:
         __UpperCamelCase   							=super().__call__(A_ ,       **A_   )
         if isinstance(A_ ,       A_   ) and len(A_   ) == 1:
              return outputs[0]
         return outputs









import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Features    ):
     __UpperCamelCase   							=np.inf

     def set_batch_size(SCREAMING_SNAKE_CASE__      : FeatureType    ) -> None:
          nonlocal batch_size
          if isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ) and feature.dtype == "binary":
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS    )

     _visit(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     return None if batch_size is np.inf else batch_size






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = False ,       A_ = False ,       A_ = None ,       **A_ ,       )  ->	Dict:
         super().__init__(
             A_ ,       split=A_ ,       features=A_ ,       cache_dir=A_ ,       keep_in_memory=A_ ,       streaming=A_ ,       num_proc=A_ ,       **A_ ,       )
         __UpperCamelCase   							=path_or_paths if isinstance(A_ ,       A_   ) else {self.split: path_or_paths}
         __UpperCamelCase   							=_PACKAGED_DATASETS_MODULES['parquet'][1]
         __UpperCamelCase   							=Parquet(
             cache_dir=A_ ,       data_files=A_ ,       features=A_ ,       hash=A_ ,       **A_ ,       )






    def _a (      self   )  ->	List[Any]:
         # Build iterable dataset
         if self.streaming:
              __UpperCamelCase   							=self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None

              self.builder.download_and_prepare(
                  download_config=A_ ,       download_mode=A_ ,       verification_mode=A_ ,       base_path=A_ ,       num_proc=self.num_proc ,       )
              __UpperCamelCase   							=self.builder.as_dataset(
                  split=self.split ,       verification_mode=A_ ,       in_memory=self.keep_in_memory   )
         return dataset






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	List[Any]:
         __UpperCamelCase   							=dataset
         __UpperCamelCase   							=path_or_buf
         __UpperCamelCase   							=batch_size or get_writer_batch_size(dataset.features   )
         __UpperCamelCase   							=parquet_writer_kwargs






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf ,       (str, bytes, os.PathLike)   ):
              with open(self.path_or_buf ,       'wb+'   ) as buffer:
                   __UpperCamelCase   							=self._write(file_obj=A_ ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         else:
              __UpperCamelCase   							=self._write(file_obj=self.path_or_buf ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         return written






    def _a (      self ,       A_ ,       A_ ,       **A_   )  ->	int:
         __UpperCamelCase   							=0
         __UpperCamelCase   							=parquet_writer_kwargs.pop('path_or_buf' ,       A_   )
         __UpperCamelCase   							=self.dataset.features.arrow_schema

         __UpperCamelCase   							=pq.ParquetWriter(A_ ,       schema=A_ ,       **A_   )

         for offset in logging.tqdm(
             range(0 ,       len(self.dataset   ) ,       A_   ) ,       unit='ba' ,       disable=not logging.is_progress_bar_enabled() ,       desc='Creating parquet from Arrow format' ,       ):
              __UpperCamelCase   							=query_table(
                  table=self.dataset._data ,       key=slice(A_ ,       offset + batch_size   ) ,       indices=self.dataset._indices if self.dataset._indices is not None else None ,       )
              writer.write_table(A_   )
              written += batch.nbytes
         writer.close()
         return written





