






"""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      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			List[Any]:
				UpperCAmelCase_     : str			     =					pa.array(TypedSequence([1, 2, 3]						)						)
				self.assertEqual(arr.type	,	pa.intaa()						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			str:
				with self.assertRaises(lowerCAmelCase_						):
							UpperCAmelCase_     : List[Any]			     =					pa.array(TypedSequence([1, 2, 3]						)	,	type=pa.intaa()						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			int:
				with self.assertRaises(lowerCAmelCase_						):
							UpperCAmelCase_     : List[str]			     =					pa.array(TypedSequence([1, 2, 3]	,	try_type=Value("bool"						)	,	type=Value("int64"						)						)						)





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





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





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





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Any:
				UpperCAmelCase_     : Any			     =					pa.array(TypedSequence(["foo", "bar"]	,	try_type=Value("int64"						)						)						)
				self.assertEqual(arr.type	,	pa.string()						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					pa.array(TypedSequence([[[1, 2, 3]]]	,	type=ArrayaD((1, 3)	,	"int64"						)						)						)
				self.assertEqual(arr.type	,	ArrayaDExtensionType((1, 3)	,	"int64"						)						)





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





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





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Tuple:
				UpperCAmelCase_     : Optional[Any]			     =					pa.array(TypedSequence(["foo", "bar"]	,	try_type=ArrayaD((1, 3)	,	"int64"						)						)						)
				self.assertEqual(arr.type	,	pa.string()						)





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

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





def 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : List[str]			     =					pa.BufferReader(A__						) if isinstance(A__	,pa.Buffer						) else pa.memory_map(A__						)
			UpperCAmelCase_     : Optional[Any]			     =					pa.ipc.open_stream(A__						)
			UpperCAmelCase_     : pa.Table			     =					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 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : Dict			     =					pa.BufferOutputStream()
			UpperCAmelCase_     : List[Any]			     =					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}						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						UpperCAmelCase_     : List[str]			     =					{"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 				snake_case      (   ):
			UpperCAmelCase_     : List[str]			     =					pa.BufferOutputStream()
			UpperCAmelCase_     : int			     =					Features({"labels": ClassLabel(names=["neg", "pos"]						)}						)
			with ArrowWriter(stream=A__	,features=A__						) as writer:
						writer.write({"labels": 0}						)
						writer.write({"labels": 1}						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert writer._schema == features.arrow_schema
			assert writer._schema.metadata == features.arrow_schema.metadata
			UpperCAmelCase_     : Optional[int]			     =					pa.BufferReader(output.getvalue()						)
			UpperCAmelCase_     : Any			     =					pa.ipc.open_stream(A__						)
			UpperCAmelCase_     : pa.Table			     =					f.read_all()
			UpperCAmelCase_     : str			     =					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 				snake_case      (   A__						):
			UpperCAmelCase_     : int			     =					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]						)
									UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					writer.finalize()

@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 10]						)
def 				snake_case      (   A__						):
			UpperCAmelCase_     : Any			     =					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						)
									UpperCAmelCase_     ,	UpperCAmelCase_     : Dict			     =					writer.finalize()

@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 10]						)
def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[str]			     =					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						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					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 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					pa.BufferOutputStream()
			UpperCAmelCase_     : List[Any]			     =					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": []}						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						UpperCAmelCase_     : Dict			     =					{"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 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : Tuple			     =					pa.BufferOutputStream()
			UpperCAmelCase_     : Union[str, Any]			     =					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]}						)						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						UpperCAmelCase_     : List[Any]			     =					{"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 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : int			     =					pa.BufferOutputStream()
			UpperCAmelCase_     : Optional[Any]			     =					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]}						)						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						UpperCAmelCase_     : List[str]			     =					{"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 				snake_case      (   ):
			with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCAmelCase_     : Dict			     =					{"col_1": pa.string(), "col_2": pa.intaa()}
						UpperCAmelCase_     : Any			     =					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]}						)
									UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					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 				snake_case      (   A__						):
			if pa.types.is_list(A__						):
						return get_base_dtype(arr_type.value_type						)
			else:
						return arr_type

def 				snake_case      (   A__	,A__						):
			if isinstance(lst[0]	,A__						):
						change_first_primitive_element_in_list(lst[0]	,A__						)
			else:
						UpperCAmelCase_     : Dict			     =					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 				snake_case      (   A__	,A__	,A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					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 				snake_case      (   A__	,A__	,A__						):
			# in range
			UpperCAmelCase_     : List[str]			     =					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
						UpperCAmelCase_     : Tuple			     =					copy.deepcopy(A__						)
						UpperCAmelCase_     : Optional[Any]			     =					np.iinfo(expected_dtype.to_pandas_dtype()						).max + 1
						change_first_primitive_element_in_list(A__	,A__						)
						UpperCAmelCase_     : List[Any]			     =					pa.array(OptimizedTypedSequence(A__	,col=A__						)						)
						assert get_base_dtype(arr.type						) == pa.intaa()

@pytest.mark.parametrize("raise_exception"	,[False, True]						)
def 				snake_case      (   A__	,A__						):
			UpperCAmelCase_     : int			     =					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 				snake_case      (   A__						):
			UpperCAmelCase_     : Optional[Any]			     =					"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}						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : int			     =					writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert mockfs.exists(A__						)

def 				snake_case      (   ):
			UpperCAmelCase_     : Union[str, Any]			     =					pa.BufferOutputStream()
			with ParquetWriter(stream=A__						) as writer:
						writer.write({"col_1": "foo", "col_2": 1}						)
						writer.write({"col_1": "bar", "col_2": 2}						)
						UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			UpperCAmelCase_     : Tuple			     =					pa.BufferReader(output.getvalue()						)
			UpperCAmelCase_     : pa.Table			     =					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 				snake_case      (   A__	,A__						):
			import PIL.Image

			UpperCAmelCase_     : Dict			     =					str(tmp_path / "test_image_rgb.jpg"						)
			PIL.Image.fromarray(np.zeros((5, 5)	,dtype=np.uinta						)						).save(A__	,format="png"						)
			UpperCAmelCase_     : Tuple			     =					pa.BufferOutputStream()
			with ParquetWriter(
			    stream=A__	,features=Features({"image": Image()}						)	,embed_local_files=A__						) as writer:
						writer.write({"image": image_path}						)
						writer.finalize()
			UpperCAmelCase_     : List[str]			     =					pa.BufferReader(output.getvalue()						)
			UpperCAmelCase_     : pa.Table			     =					pq.read_table(A__						)
			UpperCAmelCase_     : Tuple			     =					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 				snake_case      (   ):
			UpperCAmelCase_     : Tuple			     =					pa.schema([pa.field("col_1"	,pa.string()	,nullable=A__						)]						)
			UpperCAmelCase_     : List[Any]			     =					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()						)]						)











"""simple docstring"""
from torch import nn

def 				snake_case      (   A__						):
			if act_fn in ["swish", "silu"]:
						return nn.SiLU()
			elif act_fn == "mish":
						return nn.Mish()
			elif act_fn == "gelu":
						return nn.GELU()
			else:
						raise ValueError(F"""Unsupported activation function: {act_fn}"""						)



