
"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			["""image_processor""", """tokenizer"""]
     A__      =			"""ViltImageProcessor"""
     A__      =			("""BertTokenizer""", """BertTokenizerFast""")
     def __init__(     self  ,     snake_case__=None  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[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."  ,     snake_case__  ,     )
         _SCREAMING_SNAKE_CASE   :				Dict							    = kwargs.pop("feature_extractor"				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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`."				)

       super().__init__(snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.image_processor
     def __call__(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = True  ,     snake_case__ = False  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = 0  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = False  ,     snake_case__ = False  ,     snake_case__ = False  ,     snake_case__ = False  ,     snake_case__ = True  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.tokenizer(
           text=snake_case__  ,     add_special_tokens=snake_case__  ,     padding=snake_case__  ,     truncation=snake_case__  ,     max_length=snake_case__  ,     stride=snake_case__  ,     pad_to_multiple_of=snake_case__  ,     return_token_type_ids=snake_case__  ,     return_attention_mask=snake_case__  ,     return_overflowing_tokens=snake_case__  ,     return_special_tokens_mask=snake_case__  ,     return_offsets_mapping=snake_case__  ,     return_length=snake_case__  ,     verbose=snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__  ,     )
       # add pixel_values + pixel_mask
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.image_processor(snake_case__  ,     return_tensors=snake_case__				)
       encoding.update(snake_case__				)

       return encoding
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.batch_decode(*snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.decode(*snake_case__  ,     **snake_case__				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self.tokenizer.model_input_names
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,     snake_case__  ,     )
       return self.image_processor_class




     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,     snake_case__  ,     )
       return self.image_processor





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    "files",						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tmp_path_factory.mktemp("dset_infos_dir"				)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"				)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write(""				)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json",						"w"				) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DatasetInfosDict.from_directory(lowerCamelCase__				)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2





@pytest.mark.parametrize(
    "dataset_info",						[
        DatasetInfo(),
        DatasetInfo(
            description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							DatasetInfo				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = str(lowerCamelCase__				)
  dataset_info.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = DatasetInfo.from_directory(lowerCamelCase__				)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowerCamelCase__,						"dataset_info.json"				)				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfo(
      description="foo",						citation="bar",						homepage="https://foo.bar",						license="CC0",						features=Features({"a": Value("int32"				)}				),						post_processed={},						supervised_keys=(),						task_templates=[],						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train", "num_examples": 4_2}],						download_checksums={},						download_size=1_3_3_7,						post_processing_size=4_4_2,						dataset_size=1_2_3_4,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset_info._to_yaml_dict()
  assert sorted(lowerCamelCase__				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key],						(list, dict, int, str)				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = yaml.safe_dump(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = yaml.safe_load(lowerCamelCase__				)
  assert dataset_info_yaml_dict == reloaded





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo()
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict",						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}				),
        DatasetInfosDict({"my_config_name": DatasetInfo()}				),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						)
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2				),
                "v2": DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							DatasetInfosDict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = str(lowerCamelCase__				)
  dataset_infos_dict.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfosDict.from_directory(lowerCamelCase__				)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(lowerCamelCase__,						"README.md"				)				)



