




"""simple docstring"""



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      UpperCAmelCase_  (						A_							):




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([1, 2, 3]				)				)
     self.assertEqual(arr.type     ,			pa.intaa()				)




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     with self.assertRaises(snake_case_				):
      A__	=    pa.array(TypedSequence([1, 2, 3]				)     ,			type=pa.intaa()				)




    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     with self.assertRaises(snake_case_				):
      A__	=    pa.array(TypedSequence([1, 2, 3]     ,			try_type=Value("bool"				)     ,			type=Value("int64"				)				)				)




    def 							__magic_name__							(   self						:	str				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([1, 2, 3]     ,			type=Value("int32"				)				)				)
     self.assertEqual(arr.type     ,			pa.intaa()				)




    def 							__magic_name__							(   self						:	Any				)				->							Tuple:




     '''simple docstring'''

     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)				):
      A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			type=Value("int64"				)				)				)




    def 							__magic_name__							(   self						:	int				)				->							Tuple:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([1, 2, 3]     ,			try_type=Value("int32"				)				)				)
     self.assertEqual(arr.type     ,			pa.intaa()				)




    def 							__magic_name__							(   self						:	Any				)				->							Any:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			try_type=Value("int64"				)				)				)
     self.assertEqual(arr.type     ,			pa.string()				)




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([[[1, 2, 3]]]     ,			type=ArrayaD((1, 3)     ,			"int64"				)				)				)
     self.assertEqual(arr.type     ,			ArrayaDExtensionType((1, 3)     ,			"int64"				)				)




    def 							__magic_name__							(   self						:	int				)				->							Optional[Any]:




     '''simple docstring'''

     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)				):
      A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			type=ArrayaD((1, 3)     ,			"int64"				)				)				)




    def 							__magic_name__							(   self						:	str				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([[[1, 2, 3]]]     ,			try_type=ArrayaD((1, 3)     ,			"int64"				)				)				)
     self.assertEqual(arr.type     ,			ArrayaDExtensionType((1, 3)     ,			"int64"				)				)




    def 							__magic_name__							(   self						:	Dict				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			try_type=ArrayaD((1, 3)     ,			"int64"				)				)				)
     self.assertEqual(arr.type     ,			pa.string()				)




    @require_pil
    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     import PIL.Image

     A__	=    PIL.Image.fromarray(np.arange(10     ,			dtype=np.uinta				).reshape(2     ,			5				)				)
     with patch(
         "datasets.arrow_writer.cast_to_python_objects"     ,			side_effect=snake_case_				) as mock_cast_to_python_objects:
      A__	=    pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]     ,			type=Image()				)				)
      A__,				A__	=    mock_cast_to_python_objects.call_args_list[-1]
      self.assertIn("optimize_list_casting"     ,			snake_case_				)
      self.assertFalse(kwargs["optimize_list_casting"]				)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
 A__	=    pa.BufferReader(lowercase_						) if isinstance(lowercase_      ,			pa.Buffer						) else pa.memory_map(lowercase_						)
 A__	=    pa.ipc.open_stream(lowercase_						)
 A__	=    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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) as writer:
  writer.write({"col_1": "foo", "col_2": 1}						)
  writer.write({"col_1": "bar", "col_2": 2}						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			metadata=writer._schema.metadata						)
 _check_output(output.getvalue()      ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)







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







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 1, 10]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Any:
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(
     stream=lowercase_      ,			writer_batch_size=lowercase_      ,			hash_salt="split_name"      ,			check_duplicates=lowercase_      ,			) as writer:
  with pytest.raises(lowercase_						):
   writer.write({"col_1": "foo", "col_2": 1}      ,			key=[1, 2]						)
   A__,				A__	=    writer.finalize()







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 2, 10]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Optional[int]:
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(
     stream=lowercase_      ,			writer_batch_size=lowercase_      ,			hash_salt="split_name"      ,			check_duplicates=lowercase_      ,			) as writer:
  with pytest.raises(lowercase_						):
   writer.write({"col_1": "foo", "col_2": 1}      ,			key=10						)
   writer.write({"col_1": "bar", "col_2": 2}      ,			key=10						)
   A__,				A__	=    writer.finalize()







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 2, 10]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		List[Any]:
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(
     stream=lowercase_      ,			writer_batch_size=lowercase_      ,			hash_salt="split_name"      ,			check_duplicates=lowercase_      ,			) as writer:
  writer.write({"col_1": "foo", "col_2": 1}      ,			key=1						)
  writer.write({"col_1": "bar", "col_2": 2}      ,			key=2						)
  A__,				A__	=    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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) as writer:
  writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}						)
  writer.write_batch({"col_1": [], "col_2": []}						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Dict:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) as writer:
  writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}						)						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) 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]}						)						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			metadata=writer._schema.metadata						)
 _check_output(output.getvalue()      ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)







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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 if pa.types.is_list(lowercase_						):
  return get_base_dtype(arr_type.value_type						)
 else:
  return arr_type







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		List[Any]:
 if isinstance(lst[0]      ,			lowercase_						):
  change_first_primitive_element_in_list(lst[0]      ,			lowercase_						)
 else:
  A__	=    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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 A__	=    pa.array(TypedSequence(lowercase_      ,			optimized_int_type=lowercase_						)						)
 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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Optional[Any]:
 # in range
 A__	=    pa.array(OptimizedTypedSequence(lowercase_      ,			col=lowercase_						)						)
 assert get_base_dtype(arr.type						) == expected_dtype

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







@pytest.mark.parametrize("raise_exception"      ,			[False, True]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
 A__	=    str(tmp_path / "dataset-train.arrow"						)
 try:
  with ArrowWriter(path=lowercase_						) as writer:
   if raise_exception:
    raise pa.lib.ArrowInvalid()
   else:
    writer.stream.close()
 except pa.lib.ArrowInvalid:
  pass
 finally:
  assert writer.stream.closed







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







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







@require_pil
@pytest.mark.parametrize("embed_local_files"      ,			[False, True]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Tuple:
 import PIL.Image

 A__	=    str(tmp_path / "test_image_rgb.jpg"						)
 PIL.Image.fromarray(np.zeros((5, 5)      ,			dtype=np.uinta						)						).save(lowercase_      ,			format="png"						)
 A__	=    pa.BufferOutputStream()
 with ParquetWriter(
     stream=lowercase_      ,			features=Features({"image": Image()}						)      ,			embed_local_files=lowercase_						) as writer:
  writer.write({"image": image_path}						)
  writer.finalize()
 A__	=    pa.BufferReader(output.getvalue()						)
 A__	=    pq.read_table(lowercase_						)
 A__	=    pa_table.to_pydict()
 if embed_local_files:
  assert isinstance(out["image"][0]["path"]      ,			lowercase_						)
  with open(lowercase_      ,			"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   _SCREAMING_SNAKE_CASE       (							)						->		str:
 A__	=    pa.schema([pa.field("col_1"      ,			pa.string()      ,			nullable=lowercase_						)]						)
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(stream=lowercase_						) as writer:
  writer._build_writer(inferred_schema=lowercase_						)
  assert writer._schema == pa.schema([pa.field("col_1"      ,			pa.string()						)]						)











"""simple docstring"""



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''AutoImageProcessor'''
    lowercase__						 =				'''AutoTokenizer'''




    def __init__(   self						:	str     ,			snake_case_						:	Dict     ,			snake_case_						:	List[str]				)				->							str:




     '''simple docstring'''

     super().__init__(snake_case_     ,			snake_case_				)
     A__	=    self.image_processor




    def __call__(   self						:	int     ,			snake_case_						:	Any=None     ,			snake_case_						:	Any=None     ,			snake_case_						:	Union[str, Any]=None     ,			**snake_case_						:	Optional[int]				)				->							Optional[int]:




     '''simple docstring'''

     if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			*snake_case_						:	Union[str, Any]     ,			**snake_case_						:	List[Any]				)				->							int:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	List[str]     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							Tuple:




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(   self						:	List[Any]				)				->							List[Any]:




     '''simple docstring'''

     return ["input_ids", "attention_mask", "pixel_values"]





