
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


_A						      =				R"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(A_						)
class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							List[str]     =	"rag"
     UpperCAmelCase__       :							Optional[Any]     =	True





     def __init__(self   :					Tuple       ,					_A   :					List[str]=None       ,					_A   :					str=True       ,					_A   :					Dict=None       ,					_A   :					List[str]=None       ,					_A   :					Tuple=None       ,					_A   :					Union[str, Any]=None       ,					_A   :					Tuple=None       ,					_A   :					Optional[Any]=" / "       ,					_A   :					List[Any]=" // "       ,					_A   :					Dict=5       ,					_A   :					Tuple=3_0_0       ,					_A   :					List[Any]=7_6_8       ,					_A   :					Union[str, Any]=8       ,					_A   :					str="wiki_dpr"       ,					_A   :					Optional[Any]="train"       ,					_A   :					Tuple="compressed"       ,					_A   :					Dict=None       ,					_A   :					str=None       ,					_A   :					str=False       ,					_A   :					Union[str, Any]=False       ,					_A   :					List[Any]=0.0       ,					_A   :					List[Any]=True       ,					_A   :					str=False       ,					_A   :					Optional[int]=False       ,					_A   :					Tuple=False       ,					_A   :					Optional[Any]=True       ,					_A   :					str=None       ,					**_A   :					Tuple       ,					)		->		Optional[Any]:
         super().__init__(
             bos_token_id=_A       ,					pad_token_id=_A       ,					eos_token_id=_A       ,					decoder_start_token_id=_A       ,					forced_eos_token_id=_A       ,					is_encoder_decoder=_A       ,					prefix=_A       ,					vocab_size=_A       ,					**_A       ,					)
         assert (
             "question_encoder" in kwargs and "generator" in kwargs
         ), "Config has to be initialized with question_encoder and generator config"
         snake_case				  =      kwargs.pop("question_encoder"     )
         snake_case				  =      question_encoder_config.pop("model_type"     )
         snake_case				  =      kwargs.pop("generator"     )
         snake_case				  =      decoder_config.pop("model_type"     )

         from ..auto.configuration_auto import AutoConfig

         snake_case				  =      AutoConfig.for_model(_A       ,					**_A     )
         snake_case				  =      AutoConfig.for_model(_A       ,					**_A     )

         snake_case				  =      reduce_loss
         snake_case				  =      label_smoothing
         snake_case				  =      exclude_bos_score
         snake_case				  =      do_marginalize

         snake_case				  =      title_sep
         snake_case				  =      doc_sep
         snake_case				  =      n_docs
         snake_case				  =      max_combined_length

         snake_case				  =      dataset
         snake_case				  =      dataset_split
         snake_case				  =      index_name

         snake_case				  =      retrieval_vector_size
         snake_case				  =      retrieval_batch_size
         snake_case				  =      passages_path
         snake_case				  =      index_path
         snake_case				  =      use_dummy_dataset

         snake_case				  =      output_retrieved

         snake_case				  =      do_deduplication

         snake_case				  =      use_cache

         if self.forced_eos_token_id is None:
             snake_case				  =      getattr(self.generator       ,					"forced_eos_token_id"       ,					_A     )





     @classmethod
     def 	UpperCAmelCase(cls   :					List[Any]       ,					_A   :					PretrainedConfig       ,					_A   :					PretrainedConfig       ,					**_A   :					str     )		->		PretrainedConfig:
         return cls(question_encoder=question_encoder_config.to_dict()       ,					generator=generator_config.to_dict()       ,					**_A     )





     def 	UpperCAmelCase(self   :					List[Any]     )		->		List[Any]:
         snake_case				  =      copy.deepcopy(self.__dict__     )
         snake_case				  =      self.question_encoder.to_dict()
         snake_case				  =      self.generator.to_dict()
         snake_case				  =      self.__class__.model_type
         return output



import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil







class   lowerCamelCase	(						A_						):





     def 	UpperCAmelCase(self   :					str     )		->		Dict:
         snake_case				  =      pa.array(TypedSequence([1, 2, 3]     )     )
         self.assertEqual(arr.type       ,					pa.intaa()     )





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Optional[int]:
         with self.assertRaises(_A     ):
             snake_case				  =      pa.array(TypedSequence([1, 2, 3]     )       ,					type=pa.intaa()     )





     def 	UpperCAmelCase(self   :					Dict     )		->		str:
         with self.assertRaises(_A     ):
             snake_case				  =      pa.array(TypedSequence([1, 2, 3]       ,					try_type=Value("bool"     )       ,					type=Value("int64"     )     )     )





     def 	UpperCAmelCase(self   :					List[Any]     )		->		List[str]:
         snake_case				  =      pa.array(TypedSequence([1, 2, 3]       ,					type=Value("int32"     )     )     )
         self.assertEqual(arr.type       ,					pa.intaa()     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         with self.assertRaises((TypeError, pa.lib.ArrowInvalid)     ):
             snake_case				  =      pa.array(TypedSequence(["foo", "bar"]       ,					type=Value("int64"     )     )     )





     def 	UpperCAmelCase(self   :					List[Any]     )		->		Dict:
         snake_case				  =      pa.array(TypedSequence([1, 2, 3]       ,					try_type=Value("int32"     )     )     )
         self.assertEqual(arr.type       ,					pa.intaa()     )





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Optional[int]:
         snake_case				  =      pa.array(TypedSequence(["foo", "bar"]       ,					try_type=Value("int64"     )     )     )
         self.assertEqual(arr.type       ,					pa.string()     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         snake_case				  =      pa.array(TypedSequence([[[1, 2, 3]]]       ,					type=ArrayaD((1, 3)       ,					"int64"     )     )     )
         self.assertEqual(arr.type       ,					ArrayaDExtensionType((1, 3)       ,					"int64"     )     )





     def 	UpperCAmelCase(self   :					str     )		->		Union[str, Any]:
         with self.assertRaises((TypeError, pa.lib.ArrowInvalid)     ):
             snake_case				  =      pa.array(TypedSequence(["foo", "bar"]       ,					type=ArrayaD((1, 3)       ,					"int64"     )     )     )





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Tuple:
         snake_case				  =      pa.array(TypedSequence([[[1, 2, 3]]]       ,					try_type=ArrayaD((1, 3)       ,					"int64"     )     )     )
         self.assertEqual(arr.type       ,					ArrayaDExtensionType((1, 3)       ,					"int64"     )     )





     def 	UpperCAmelCase(self   :					str     )		->		Dict:
         snake_case				  =      pa.array(TypedSequence(["foo", "bar"]       ,					try_type=ArrayaD((1, 3)       ,					"int64"     )     )     )
         self.assertEqual(arr.type       ,					pa.string()     )





     @require_pil
     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Any:
         import PIL.Image

         snake_case				  =      PIL.Image.fromarray(np.arange(1_0       ,					dtype=np.uinta     ).reshape(2       ,					5     )     )
         with patch(
             "datasets.arrow_writer.cast_to_python_objects"       ,					side_effect=_A     ) as mock_cast_to_python_objects:
             snake_case				  =      pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]       ,					type=Image()     )     )
             snake_case  ,							snake_case				  =      mock_cast_to_python_objects.call_args_list[-1]
             self.assertIn("optimize_list_casting"       ,					_A     )
             self.assertFalse(kwargs["optimize_list_casting"]     )



def 		lowercase_    (     A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      pa.BufferReader(A__	) if isinstance(A__				,	pa.Buffer	) else pa.memory_map(A__	)
    snake_case				  =      pa.ipc.open_stream(A__	)
    snake_case				  =      f.read_all()
    assert len(pa_table.to_batches()	) == expected_num_chunks
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
    del pa_table



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write({"col_1": "foo", "col_2": 1}	)
        writer.write({"col_1": "bar", "col_2": 2}	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        snake_case				  =      {"col_1": pa.string(), "col_2": pa.intaa()}
    assert writer._schema == pa.schema(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



def 		lowercase_    (     )							->							str:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      Features({"labels": ClassLabel(names=["neg", "pos"]	)}	)
    with ArrowWriter(stream=A__				,	features=A__	) as writer:
        writer.write({"labels": 0}	)
        writer.write({"labels": 1}	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == features.arrow_schema
    assert writer._schema.metadata == features.arrow_schema.metadata
    snake_case				  =      pa.BufferReader(output.getvalue()	)
    snake_case				  =      pa.ipc.open_stream(A__	)
    snake_case				  =      f.read_all()
    snake_case				  =      pa_table.schema
    assert pa_table.num_rows == 2
    assert schema == features.arrow_schema
    assert schema.metadata == features.arrow_schema.metadata
    assert features == Features.from_arrow_schema(A__	)



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
def 		lowercase_    (     A__	)							->							int:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    with ArrowWriter(
        stream=A__				,	writer_batch_size=A__				,	hash_salt="split_name"				,	check_duplicates=A__				,	) as writer:
        with pytest.raises(A__	):
            writer.write({"col_1": "foo", "col_2": 1}				,	key=[1, 2]	)
            snake_case  ,							snake_case				  =      writer.finalize()



@pytest.mark.parametrize("writer_batch_size"				,	[None, 2, 10]	)
def 		lowercase_    (     A__	)							->							str:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    with ArrowWriter(
        stream=A__				,	writer_batch_size=A__				,	hash_salt="split_name"				,	check_duplicates=A__				,	) as writer:
        with pytest.raises(A__	):
            writer.write({"col_1": "foo", "col_2": 1}				,	key=10	)
            writer.write({"col_1": "bar", "col_2": 2}				,	key=10	)
            snake_case  ,							snake_case				  =      writer.finalize()



@pytest.mark.parametrize("writer_batch_size"				,	[None, 2, 10]	)
def 		lowercase_    (     A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    with ArrowWriter(
        stream=A__				,	writer_batch_size=A__				,	hash_salt="split_name"				,	check_duplicates=A__				,	) as writer:
        writer.write({"col_1": "foo", "col_2": 1}				,	key=1	)
        writer.write({"col_1": "bar", "col_2": 2}				,	key=2	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							Dict:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
        writer.write_batch({"col_1": [], "col_2": []}	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        snake_case				  =      {"col_1": pa.string(), "col_2": pa.intaa()}
    assert writer._schema == pa.schema(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							Union[str, Any]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        snake_case				  =      {"col_1": pa.string(), "col_2": pa.intaa()}
    assert writer._schema == pa.schema(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}	)	)
        writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}	)	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        snake_case				  =      {"col_1": pa.string(), "col_2": pa.intaa()}
    assert writer._schema == pa.schema(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    with tempfile.TemporaryDirectory() as tmp_dir:
        snake_case				  =      {"col_1": pa.string(), "col_2": pa.intaa()}
        snake_case				  =      os.path.join(A__				,	"test.arrow"	)
        with ArrowWriter(path=A__				,	schema=pa.schema(A__	)	) as writer:
            writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
            snake_case  ,							snake_case				  =      writer.finalize()
        assert num_examples == 2
        assert num_bytes > 0
        assert writer._schema == pa.schema(A__				,	metadata=writer._schema.metadata	)
        _check_output(A__				,	1	)



def 		lowercase_    (     A__	)							->							int:
    """simple docstring"""

    if pa.types.is_list(A__	):
        return get_base_dtype(arr_type.value_type	)
    else:
        return arr_type



def 		lowercase_    (     A__				,	A__	)							->							Any:
    """simple docstring"""

    if isinstance(lst[0]				,	A__	):
        change_first_primitive_element_in_list(lst[0]				,	A__	)
    else:
        snake_case				  =      value



@pytest.mark.parametrize("optimized_int_type, expected_dtype"				,	[(None, pa.intaa()), (Value("int32"	), pa.intaa())]	)
@pytest.mark.parametrize("sequence"				,	[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 		lowercase_    (     A__				,	A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      pa.array(TypedSequence(A__				,	optimized_int_type=A__	)	)
    assert get_base_dtype(arr.type	) == expected_dtype



@pytest.mark.parametrize(
    "col, expected_dtype"				,	[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]				,	)
@pytest.mark.parametrize("sequence"				,	[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 		lowercase_    (     A__				,	A__				,	A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      pa.array(OptimizedTypedSequence(A__				,	col=A__	)	)
    assert get_base_dtype(arr.type	) == expected_dtype

    # not in range
    if col != "other":
        # avoids errors due to in-place modifications
        snake_case				  =      copy.deepcopy(A__	)
        snake_case				  =      np.iinfo(expected_dtype.to_pandas_dtype()	).max + 1
        change_first_primitive_element_in_list(A__				,	A__	)
        snake_case				  =      pa.array(OptimizedTypedSequence(A__				,	col=A__	)	)
        assert get_base_dtype(arr.type	) == pa.intaa()



@pytest.mark.parametrize("raise_exception"				,	[False, True]	)
def 		lowercase_    (     A__				,	A__	)							->							Optional[Any]:
    """simple docstring"""

    snake_case				  =      str(tmp_path / "dataset-train.arrow"	)
    try:
        with ArrowWriter(path=A__	) as writer:
            if raise_exception:
                raise pa.lib.ArrowInvalid()
            else:
                writer.stream.close()
    except pa.lib.ArrowInvalid:
        pass
    finally:
        assert writer.stream.closed



def 		lowercase_    (     A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      "mock://dataset-train.arrow"
    with ArrowWriter(path=A__				,	storage_options=mockfs.storage_options	) as writer:
        assert isinstance(writer._fs				,	type(A__	)	)
        assert writer._fs.storage_options == mockfs.storage_options
        writer.write({"col_1": "foo", "col_2": 1}	)
        writer.write({"col_1": "bar", "col_2": 2}	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert mockfs.exists(A__	)



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    with ParquetWriter(stream=A__	) as writer:
        writer.write({"col_1": "foo", "col_2": 1}	)
        writer.write({"col_1": "bar", "col_2": 2}	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    snake_case				  =      pa.BufferReader(output.getvalue()	)
    snake_case				  =      pq.read_table(A__	)
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}



@require_pil
@pytest.mark.parametrize("embed_local_files"				,	[False, True]	)
def 		lowercase_    (     A__				,	A__	)							->							Dict:
    """simple docstring"""

    import PIL.Image

    snake_case				  =      str(tmp_path / "test_image_rgb.jpg"	)
    PIL.Image.fromarray(np.zeros((5, 5)				,	dtype=np.uinta	)	).save(A__				,	format="png"	)
    snake_case				  =      pa.BufferOutputStream()
    with ParquetWriter(
        stream=A__				,	features=Features({"image": Image()}	)				,	embed_local_files=A__	) as writer:
        writer.write({"image": image_path}	)
        writer.finalize()
    snake_case				  =      pa.BufferReader(output.getvalue()	)
    snake_case				  =      pq.read_table(A__	)
    snake_case				  =      pa_table.to_pydict()
    if embed_local_files:
        assert isinstance(out["image"][0]["path"]				,	A__	)
        with open(A__				,	"rb"	) as f:
            assert out["image"][0]["bytes"] == f.read()
    else:
        assert out["image"][0]["path"] == image_path
        assert out["image"][0]["bytes"] is None





def 		lowercase_    (     )							->							List[Any]:
    """simple docstring"""

    snake_case				  =      pa.schema([pa.field("col_1"				,	pa.string()				,	nullable=A__	)]	)
    snake_case				  =      pa.BufferOutputStream()
    with ArrowWriter(stream=A__	) as writer:
        writer._build_writer(inferred_schema=A__	)
        assert writer._schema == pa.schema([pa.field("col_1"				,	pa.string()	)]	)

