




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
     import torch






class       lowercase(  __UpperCAmelCase				):







 '''simple docstring'''





 lowercase__				      =      '''char'''
 lowercase__				      =      '''bpe'''
 lowercase__				      =      '''wp'''







A_      			=       (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class       lowercase(  __UpperCAmelCase				):







 '''simple docstring'''





 lowercase__				      =      ['''image_processor''', '''char_tokenizer''']
 lowercase__				      =      '''ViTImageProcessor'''
 lowercase__				      =      '''MgpstrTokenizer'''


 def __init__(       self:	List[Any],   a_:	Any=None,   a_:	Optional[Any]=None,   **a_:	str	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						None
   if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead.""",   __SCREAMING_SNAKE_CASE,   )
     _snake_case							:	Any			=						kwargs.pop("""feature_extractor"""	)

   _snake_case							:	List[str]			=						image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""	)
   if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""	)

   _snake_case							:	int			=						tokenizer
   _snake_case							:	Union[str, Any]			=						AutoTokenizer.from_pretrained("""gpt2"""	)
   _snake_case							:	Union[str, Any]			=						AutoTokenizer.from_pretrained("""bert-base-uncased"""	)

   super().__init__(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE	)


 def __call__(       self:	Tuple,   a_:	Dict=None,   a_:	List[str]=None,   a_:	int=None,   **a_:	Optional[int]	):




   '''simple docstring'''



   if images is None and text is None:
     raise ValueError("""You need to specify either an `images` or `text` input to process."""	)

   if images is not None:
     _snake_case							:	str			=						self.image_processor(__SCREAMING_SNAKE_CASE,   return_tensors=__SCREAMING_SNAKE_CASE,   **__SCREAMING_SNAKE_CASE	)
   if text is not None:
     _snake_case							:	Union[str, Any]			=						self.char_tokenizer(__SCREAMING_SNAKE_CASE,   return_tensors=__SCREAMING_SNAKE_CASE,   **__SCREAMING_SNAKE_CASE	)

   if text is None:
     return inputs
   elif images is None:
     return encodings
   else:
     _snake_case							:	Union[str, Any]			=						encodings["""input_ids"""]
     return inputs


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case	,      _snake_case	,      _snake_case							:	Any			=						sequences
   _snake_case							:	Optional[int]			=						char_preds.size(0	)

   _snake_case	,      _snake_case							:	Tuple			=						self._decode_helper(__SCREAMING_SNAKE_CASE,   """char"""	)
   _snake_case	,      _snake_case							:	Tuple			=						self._decode_helper(__SCREAMING_SNAKE_CASE,   """bpe"""	)
   _snake_case	,      _snake_case							:	Dict			=						self._decode_helper(__SCREAMING_SNAKE_CASE,   """wp"""	)

   _snake_case							:	Union[str, Any]			=						[]
   _snake_case							:	List[str]			=						[]
   for i in range(__SCREAMING_SNAKE_CASE	):
     _snake_case							:	Optional[int]			=						[char_scores[i], bpe_scores[i], wp_scores[i]]
     _snake_case							:	int			=						[char_strs[i], bpe_strs[i], wp_strs[i]]
     _snake_case							:	List[str]			=						scores.index(max(__SCREAMING_SNAKE_CASE	)	)
     final_strs.append(strs[max_score_index]	)
     final_scores.append(scores[max_score_index]	)

   _snake_case							:	str			=						{}
   _snake_case							:	Any			=						final_strs
   _snake_case							:	List[str]			=						final_scores
   _snake_case							:	Union[str, Any]			=						char_strs
   _snake_case							:	int			=						bpe_strs
   _snake_case							:	Optional[int]			=						wp_strs
   return out


 def 				UpperCamelCase_     (       self:	List[str],   a_:	Optional[int],   a_:	List[Any]	):




   '''simple docstring'''



   if format == DecodeType.CHARACTER:
     _snake_case							:	Optional[int]			=						self.char_decode
     _snake_case							:	Union[str, Any]			=						1
     _snake_case							:	List[Any]			=						"""[s]"""
   elif format == DecodeType.BPE:
     _snake_case							:	List[Any]			=						self.bpe_decode
     _snake_case							:	int			=						2
     _snake_case							:	Any			=						"""#"""
   elif format == DecodeType.WORDPIECE:
     _snake_case							:	Optional[int]			=						self.wp_decode
     _snake_case							:	Optional[int]			=						102
     _snake_case							:	Optional[int]			=						"""[SEP]"""
   else:
     raise ValueError(f"Format {format} is not supported."	)

   _snake_case	,      _snake_case							:	Any			=						[], []
   _snake_case							:	List[Any]			=						pred_logits.size(0	)
   _snake_case							:	str			=						pred_logits.size(1	)
   _snake_case	,      _snake_case							:	Tuple			=						pred_logits.topk(1,   dim=-1,   largest=__SCREAMING_SNAKE_CASE,   sorted=__SCREAMING_SNAKE_CASE	)
   _snake_case							:	Optional[Any]			=						preds_index.view(-1,   __SCREAMING_SNAKE_CASE	)[:, 1:]
   _snake_case							:	int			=						decoder(__SCREAMING_SNAKE_CASE	)
   _snake_case	,      _snake_case							:	str			=						torch.nn.functional.softmax(__SCREAMING_SNAKE_CASE,   dim=2	).max(dim=2	)
   _snake_case							:	Union[str, Any]			=						preds_max_prob[:, 1:]

   for index in range(__SCREAMING_SNAKE_CASE	):
     _snake_case							:	List[str]			=						preds_str[index].find(__SCREAMING_SNAKE_CASE	)
     _snake_case							:	Dict			=						preds_str[index][:pred_eos]
     _snake_case							:	List[Any]			=						preds_index[index].cpu().tolist()
     _snake_case							:	List[Any]			=						pred_index.index(__SCREAMING_SNAKE_CASE	) if eos_token in pred_index else -1
     _snake_case							:	Optional[Any]			=						preds_max_prob[index][: pred_eos_index + 1]
     _snake_case							:	Dict			=						pred_max_prob.cumprod(dim=0	)[-1] if pred_max_prob.nelement() != 0 else 0.0
     dec_strs.append(__SCREAMING_SNAKE_CASE	)
     conf_scores.append(__SCREAMING_SNAKE_CASE	)

   return dec_strs, conf_scores


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	List[str]	):




   '''simple docstring'''



   _snake_case							:	int			=						[seq.replace(""" """,   """"""	) for seq in self.char_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)]
   return decode_strs


 def 				UpperCamelCase_     (       self:	Dict,   a_:	Tuple	):




   '''simple docstring'''



   return self.bpe_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Any			=						[seq.replace(""" """,   """"""	) for seq in self.wp_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)]
   return decode_strs








"""simple docstring"""


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Dict					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""	,     [False, True]					)
def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	str			=						tmp_path / """cache"""
  _snake_case							:	int			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	str			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


@pytest.mark.parametrize(
    """features"""	,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      int	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	str			=						tmp_path / """cache"""
  _snake_case							:	List[Any]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	List[Any]			=						features.copy() if features else default_expected_features
  _snake_case							:	List[Any]			=						(
      Features({feature: Value(snake_case__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  _snake_case							:	Optional[Any]			=						ParquetDatasetReader(snake_case__	,     features=snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	List[str]			=						tmp_path / """cache"""
  _snake_case							:	Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	int			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__	,     split=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("""path_type"""	,     [str, list]					)
def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      str	,     snake_case__							:      str					):
  """simple docstring"""

  if issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	Optional[Any]			=						parquet_path
  elif issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	int			=						[parquet_path]
  _snake_case							:	Union[str, Any]			=						tmp_path / """cache"""
  _snake_case							:	Tuple			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	List[str]			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      str=("train",)					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					)
  for split in splits:
    _snake_case							:	int			=						dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""	,     [False, True]					)
def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      str	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Tuple			=						tmp_path / """cache"""
  _snake_case							:	Optional[int]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	Tuple			=						ParquetDatasetReader(
        {"""train""": parquet_path}	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__					)


@pytest.mark.parametrize(
    """features"""	,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Tuple	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Optional[int]			=						tmp_path / """cache"""
  _snake_case							:	Dict			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	Optional[Any]			=						features.copy() if features else default_expected_features
  _snake_case							:	Dict			=						(
      Features({feature: Value(snake_case__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  _snake_case							:	Optional[int]			=						ParquetDatasetReader({"""train""": parquet_path}	,     features=snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__					)


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  if split:
    _snake_case							:	int			=						{split: parquet_path}
  else:
    _snake_case							:	Optional[Any]			=						"""train"""
    _snake_case							:	int			=						{"""train""": parquet_path, """test""": parquet_path}
  _snake_case							:	Dict			=						tmp_path / """cache"""
  _snake_case							:	Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	Union[str, Any]			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__	,     splits=list(path.keys()					)					)
  assert all(dataset[split].split == split for split in path.keys()					)


def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	List[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0
  _snake_case							:	str			=						pq.ParquetFile(tmp_path / """foo.parquet"""					)
  _snake_case							:	int			=						pf.read()
  assert dataset.data.table == output_table


def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						str(shared_datadir / """test_image_rgb.jpg"""					)
  _snake_case							:	Tuple			=						{"""image""": [image_path]}
  _snake_case							:	Optional[int]			=						Features({"""image""": Image()}					)
  _snake_case							:	int			=						Dataset.from_dict(snake_case__	,     features=snake_case__					)
  _snake_case							:	Optional[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0

  _snake_case							:	List[str]			=						Dataset.from_parquet(str(tmp_path / """foo.parquet"""					)					)
  assert dataset.features == reloaded_dataset.features

  _snake_case							:	Optional[Any]			=						ParquetDatasetReader(str(tmp_path / """foo.parquet"""					)	,     streaming=snake_case__					).read()
  assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    """feature, expected"""	,     [
        (Features({"""foo""": Value("""int32"""					)}					), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      str					):
  """simple docstring"""

  assert get_writer_batch_size(snake_case__					) == expected


