

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  _A		(     snake_case		,							snake_case					)      ->     List[Any]:
   assert isinstance(__lowerCamelCase		,							__lowerCamelCase					)
   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  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Tuple:
   _lowercase      :				Union[str, Any]      					=       tmp_path / "cache"
   _lowercase      :				str      					=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _lowercase      :				str      					=       JsonDatasetReader(__lowerCamelCase		,							cache_dir=__lowerCamelCase		,							keep_in_memory=__lowerCamelCase					).read()
   _check_json_dataset(__lowerCamelCase		,							__lowerCamelCase					)







@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  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     int:
   _lowercase      :				Optional[Any]      					=       tmp_path / "cache"
   _lowercase      :				Optional[int]      					=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowercase      :				int      					=       features.copy() if features else default_expected_features
   _lowercase      :				Optional[Any]      					=       (
       Features({feature: Value(__lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   _lowercase      :				Any      					=       JsonDatasetReader(__lowerCamelCase		,							features=__lowerCamelCase		,							cache_dir=__lowerCamelCase					).read()
   _check_json_dataset(__lowerCamelCase		,							__lowerCamelCase					)







@pytest.mark.parametrize(
    "features"		,							[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]		,							)
def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Any:
   _lowercase      :				List[str]      					=       tmp_path / "cache"
   _lowercase      :				Dict      					=       {"col_3": "float64", "col_1": "string", "col_2": "int64"}
   _lowercase      :				Any      					=       features.copy() if features else default_expected_features
   _lowercase      :				Dict      					=       (
       Features({feature: Value(__lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   _lowercase      :				Dict      					=       JsonDatasetReader(__lowerCamelCase		,							features=__lowerCamelCase		,							cache_dir=__lowerCamelCase					).read()
   assert isinstance(__lowerCamelCase		,							__lowerCamelCase					)
   assert dataset.num_rows == 2
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_3", "col_1", "col_2"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype







def  _A		(     snake_case		,							snake_case					)      ->     int:
   # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
   _lowercase      :				str      					=       {"col_2": "int64", "col_3": "float64", "col_1": "string"}
   _lowercase      :				str      					=       features.copy()
   _lowercase      :				Union[str, Any]      					=       (
       Features({feature: Value(__lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   _lowercase      :				Any      					=       tmp_path / "cache"
   _lowercase      :				str      					=       JsonDatasetReader(__lowerCamelCase		,							features=__lowerCamelCase		,							cache_dir=__lowerCamelCase					).read()
   assert isinstance(__lowerCamelCase		,							__lowerCamelCase					)
   assert dataset.num_rows == 2
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_2", "col_3", "col_1"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("split"		,							[None, NamedSplit("train"					), "train", "test"]					)
def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Any:
   _lowercase      :				Dict      					=       tmp_path / "cache"
   _lowercase      :				str      					=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowercase      :				Dict      					=       JsonDatasetReader(__lowerCamelCase		,							cache_dir=__lowerCamelCase		,							split=__lowerCamelCase					).read()
   _check_json_dataset(__lowerCamelCase		,							__lowerCamelCase					)
   assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"		,							[str, list]					)
def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Optional[Any]:
   if issubclass(__lowerCamelCase		,							__lowerCamelCase					):
      _lowercase      :				Dict      					=       jsonl_path
   elif issubclass(__lowerCamelCase		,							__lowerCamelCase					):
      _lowercase      :				List[Any]      					=       [jsonl_path]
   _lowercase      :				str      					=       tmp_path / "cache"
   _lowercase      :				str      					=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowercase      :				str      					=       JsonDatasetReader(__lowerCamelCase		,							cache_dir=__lowerCamelCase					).read()
   _check_json_dataset(__lowerCamelCase		,							__lowerCamelCase					)







def  _A		(     snake_case		,							snake_case		,							snake_case=("train",)					)      ->     Dict:
   assert isinstance(__lowerCamelCase		,							__lowerCamelCase					)
   for split in splits:
      _lowercase      :				List[str]      					=       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  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Union[str, Any]:
   _lowercase      :				Tuple      					=       tmp_path / "cache"
   _lowercase      :				Optional[Any]      					=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _lowercase      :				List[str]      					=       JsonDatasetReader({"train": jsonl_path}		,							cache_dir=__lowerCamelCase		,							keep_in_memory=__lowerCamelCase					).read()
   _check_json_datasetdict(__lowerCamelCase		,							__lowerCamelCase					)







@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  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     int:
   _lowercase      :				int      					=       tmp_path / "cache"
   _lowercase      :				str      					=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowercase      :				Any      					=       features.copy() if features else default_expected_features
   _lowercase      :				Union[str, Any]      					=       (
       Features({feature: Value(__lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   _lowercase      :				Optional[Any]      					=       JsonDatasetReader({"train": jsonl_path}		,							features=__lowerCamelCase		,							cache_dir=__lowerCamelCase					).read()
   _check_json_datasetdict(__lowerCamelCase		,							__lowerCamelCase					)







@pytest.mark.parametrize("split"		,							[None, NamedSplit("train"					), "train", "test"]					)
def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     List[str]:
   if split:
      _lowercase      :				Dict      					=       {split: jsonl_path}
   else:
      _lowercase      :				Any      					=       "train"
      _lowercase      :				Union[str, Any]      					=       {"train": jsonl_path, "test": jsonl_path}
   _lowercase      :				Any      					=       tmp_path / "cache"
   _lowercase      :				str      					=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _lowercase      :				Dict      					=       JsonDatasetReader(__lowerCamelCase		,							cache_dir=__lowerCamelCase					).read()
   _check_json_datasetdict(__lowerCamelCase		,							__lowerCamelCase		,							splits=list(path.keys()					)					)
   assert all(dataset[split].split == split for split in path.keys()					)







def  _A		(     snake_case					)      ->     Any:
   return json.load(__lowerCamelCase					)







def  _A		(     snake_case					)      ->     List[str]:
   return [json.loads(__lowerCamelCase					) for line in buffer]


class        a__  :


   @pytest.mark.parametrize("lines, load_json_function"      ,    [(True, load_json_lines), (False, load_json)]  )
   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      with io.BytesIO() as buffer:
         JsonDatasetWriter(lowercase_      ,    lowercase_      ,    lines=lowercase_  ).write()
         buffer.seek(0  )
         _lowercase      :				Dict      					=       load_json_function(lowercase_  )
      assert isinstance(lowercase_      ,    lowercase_  )
      assert isinstance(exported_content[0]      ,    lowercase_  )
      assert len(lowercase_  ) == 10


   @pytest.mark.parametrize(
       "orient, container, keys, len_at"      ,    [
           ("records", list, {"tokens", "labels", "answers", "id"}, None),
           ("split", dict, {"columns", "data"}, "data"),
           ("index", dict, set("0123456789"  ), None),
           ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
           ("values", list, None, None),
           ("table", dict, {"schema", "data"}, "data"),
       ]      ,    )
   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      with io.BytesIO() as buffer:
         JsonDatasetWriter(lowercase_      ,    lowercase_      ,    lines=lowercase_      ,    orient=lowercase_  ).write()
         buffer.seek(0  )
         _lowercase      :				Optional[Any]      					=       load_json(lowercase_  )
      assert isinstance(lowercase_      ,    lowercase_  )
      if keys:
         if container is dict:
            assert exported_content.keys() == keys
         else:
            assert exported_content[0].keys() == keys
      else:
         assert not hasattr(lowercase_      ,    "keys"  ) and not hasattr(exported_content[0]      ,    "keys"  )
      if len_at:
         assert len(exported_content[len_at]  ) == 10
      else:
         assert len(lowercase_  ) == 10


   @pytest.mark.parametrize("lines, load_json_function"      ,    [(True, load_json_lines), (False, load_json)]  )
   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      with io.BytesIO() as buffer:
         JsonDatasetWriter(lowercase_      ,    lowercase_      ,    lines=lowercase_      ,    num_proc=2  ).write()
         buffer.seek(0  )
         _lowercase      :				Optional[Any]      					=       load_json_function(lowercase_  )
      assert isinstance(lowercase_      ,    lowercase_  )
      assert isinstance(exported_content[0]      ,    lowercase_  )
      assert len(lowercase_  ) == 10


   @pytest.mark.parametrize(
       "orient, container, keys, len_at"      ,    [
           ("records", list, {"tokens", "labels", "answers", "id"}, None),
           ("split", dict, {"columns", "data"}, "data"),
           ("index", dict, set("0123456789"  ), None),
           ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
           ("values", list, None, None),
           ("table", dict, {"schema", "data"}, "data"),
       ]      ,    )
   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      with io.BytesIO() as buffer:
         JsonDatasetWriter(lowercase_      ,    lowercase_      ,    lines=lowercase_      ,    orient=lowercase_      ,    num_proc=2  ).write()
         buffer.seek(0  )
         _lowercase      :				List[Any]      					=       load_json(lowercase_  )
      assert isinstance(lowercase_      ,    lowercase_  )
      if keys:
         if container is dict:
            assert exported_content.keys() == keys
         else:
            assert exported_content[0].keys() == keys
      else:
         assert not hasattr(lowercase_      ,    "keys"  ) and not hasattr(exported_content[0]      ,    "keys"  )
      if len_at:
         assert len(exported_content[len_at]  ) == 10
      else:
         assert len(lowercase_  ) == 10


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      with pytest.raises(lowercase_  ):
         with io.BytesIO() as buffer:
            JsonDatasetWriter(lowercase_      ,    lowercase_      ,    num_proc=0  )






   @pytest.mark.parametrize("compression, extension"      ,    [("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]  )
   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       tmp_path_factory.mktemp("data"  ) / f'''test.json.{extension}'''
      _lowercase      :				str      					=       str(shared_datadir / f'''test_file.json.{extension}'''  )
      JsonDatasetWriter(lowercase_      ,    lowercase_      ,    compression=lowercase_  ).write()

      with fsspec.open(lowercase_      ,    "rb"      ,    compression="infer"  ) as f:
         _lowercase      :				List[str]      					=       f.read()
      with fsspec.open(lowercase_      ,    "rb"      ,    compression="infer"  ) as f:
         _lowercase      :				Dict      					=       f.read()
      assert exported_content == original_content









'''simple docstring'''
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					str     	=							'OwlViTImageProcessor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							('CLIPTokenizer', 'CLIPTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,    _UpperCamelCase      ,    )
         _lowercase      :				Optional[int]      					=       kwargs.pop("feature_extractor"  )

      _lowercase      :				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`."  )

      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase="max_length"      ,    _UpperCamelCase="np"      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if text is None and query_images is None and images is None:
         raise ValueError(
             "You have to specify at least one text or query image or image. All three cannot be none."  )

      if text is not None:
         if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) or (isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and not isinstance(text[0]      ,    _UpperCamelCase  )):
            _lowercase      :				int      					=       [self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )]

         elif isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and isinstance(text[0]      ,    _UpperCamelCase  ):
            _lowercase      :				str      					=       []

            # Maximum number of queries across batch
            _lowercase      :				str      					=       max([len(_UpperCamelCase  ) for t in text]  )

            # Pad all batch samples to max number of text queries
            for t in text:
               if len(_UpperCamelCase  ) != max_num_queries:
                  _lowercase      :				List[Any]      					=       t + [" "] * (max_num_queries - len(_UpperCamelCase  ))

               _lowercase      :				Tuple      					=       self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )
               encodings.append(_UpperCamelCase  )
         else:
            raise TypeError("Input text should be a string, a list of strings or a nested list of strings"  )

         if return_tensors == "np":
            _lowercase      :				List[Any]      					=       np.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				Optional[Any]      					=       np.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "jax" and is_flax_available():
            import jax.numpy as jnp

            _lowercase      :				Union[str, Any]      					=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				int      					=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "pt" and is_torch_available():
            import torch

            _lowercase      :				int      					=       torch.cat([encoding["input_ids"] for encoding in encodings]      ,    dim=0  )
            _lowercase      :				Dict      					=       torch.cat([encoding["attention_mask"] for encoding in encodings]      ,    dim=0  )

         elif return_tensors == "tf" and is_tf_available():
            import tensorflow as tf

            _lowercase      :				Optional[int]      					=       tf.stack([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				List[str]      					=       tf.stack([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         else:
            raise ValueError("Target return tensor type could not be returned"  )

         _lowercase      :				Optional[int]      					=       BatchEncoding()
         _lowercase      :				List[Any]      					=       input_ids
         _lowercase      :				Dict      					=       attention_mask

      if query_images is not None:
         _lowercase      :				int      					=       BatchEncoding()
         _lowercase      :				Any      					=       self.image_processor(
             _UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  ).pixel_values
         _lowercase      :				Any      					=       query_pixel_values

      if images is not None:
         _lowercase      :				str      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is not None and images is not None:
         _lowercase      :				List[Any]      					=       image_features.pixel_values
         return encoding
      elif query_images is not None and images is not None:
         _lowercase      :				Optional[Any]      					=       image_features.pixel_values
         return encoding
      elif text is not None or query_images is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**_UpperCamelCase  )      ,    tensor_type=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_object_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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






