




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter




class 		UpperCamelCase__     (TensorFormatter[Mapping, np.ndarray, Mapping]					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__=None					,  **UpperCamelCase__					)					->				List[str]:
           super().__init__(features=UpperCamelCase__					)
           lowerCamelCase       :				List[Any]      			= np_array_kwargs







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Tuple:
           if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                  if column and all(
                      isinstance(UpperCamelCase__					,  np.ndarray					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
                         return np.stack(UpperCamelCase__					)
                  else:
                         # don't use np.array(column, dtype=object)
                         # since it fails in certain cases
                         # see https://stackoverflow.com/q/51005699
                         lowerCamelCase       :				Tuple      			= np.empty(len(UpperCamelCase__					)					,  dtype=UpperCamelCase__					)
                         lowerCamelCase       :				Tuple      			= column
                         return out
           return column







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				List[str]:
           if isinstance(UpperCamelCase__					,  (str, bytes, type(UpperCamelCase__					))					):
                  return value
           elif isinstance(UpperCamelCase__					,  (np.character, np.ndarray)					) and np.issubdtype(value.dtype					,  np.character					):
                  return value
           elif isinstance(UpperCamelCase__					,  np.number					):
                  return value

           lowerCamelCase       :				Union[str, Any]      			= {}

           if isinstance(UpperCamelCase__					,  np.ndarray					) and np.issubdtype(value.dtype					,  np.integer					):
                  lowerCamelCase       :				int      			= {"dtype": np.intaa}
           elif isinstance(UpperCamelCase__					,  np.ndarray					) and np.issubdtype(value.dtype					,  np.floating					):
                  lowerCamelCase       :				List[Any]      			= {"dtype": np.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                  import PIL.Image

                  if isinstance(UpperCamelCase__					,  PIL.Image.Image					):
                         return np.asarray(UpperCamelCase__					,  **self.np_array_kwargs					)

           return np.asarray(UpperCamelCase__					,  **{**default_dtype, **self.np_array_kwargs}					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				str:
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                  import torch

                  if isinstance(UpperCamelCase__					,  torch.Tensor					):
                         return self._tensorize(data_struct.detach().cpu().numpy()[()]					)
           if hasattr(UpperCamelCase__					,  "__array__"					) and not isinstance(UpperCamelCase__					,  (np.ndarray, np.character, np.number)					):
                  lowerCamelCase       :				List[Any]      			= data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(UpperCamelCase__					,  np.ndarray					):
                  if data_struct.dtype == object:
                         return self._consolidate([self.recursive_tensorize(UpperCamelCase__					) for substruct in data_struct]					)
           if isinstance(UpperCamelCase__					,  (list, tuple)					):
                  return self._consolidate([self.recursive_tensorize(UpperCamelCase__					) for substruct in data_struct]					)
           return self._tensorize(UpperCamelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Union[str, Any]:
           return map_nested(self._recursive_tensorize					,  UpperCamelCase__					,  map_list=UpperCamelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Mapping:
           lowerCamelCase       :				Dict      			= self.numpy_arrow_extractor().extract_row(UpperCamelCase__					)
           lowerCamelCase       :				Dict      			= self.python_features_decoder.decode_row(UpperCamelCase__					)
           return self.recursive_tensorize(UpperCamelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				np.ndarray:
           lowerCamelCase       :				List[Any]      			= self.numpy_arrow_extractor().extract_column(UpperCamelCase__					)
           lowerCamelCase       :				Union[str, Any]      			= self.python_features_decoder.decode_column(UpperCamelCase__					,  pa_table.column_names[0]					)
           lowerCamelCase       :				str      			= self.recursive_tensorize(UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= self._consolidate(UpperCamelCase__					)
           return column







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Mapping:
           lowerCamelCase       :				Optional[Any]      			= self.numpy_arrow_extractor().extract_batch(UpperCamelCase__					)
           lowerCamelCase       :				Dict      			= self.python_features_decoder.decode_batch(UpperCamelCase__					)
           lowerCamelCase       :				int      			= self.recursive_tensorize(UpperCamelCase__					)
           for column_name in batch:
                  lowerCamelCase       :				Dict      			= self._consolidate(batch[column_name]					)
           return batch












# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/






