




'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
				import jax

				from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class     A					(	unittest.TestCase					):







							def __init__(						self :						Optional[Any]							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Dict=13							,    lowerCAmelCase_ :						Dict=30							,    lowerCAmelCase_ :						Union[str, Any]=2							,    lowerCAmelCase_ :						int=3							,    lowerCAmelCase_ :						int=True							,    lowerCAmelCase_ :						Union[str, Any]=True							,    lowerCAmelCase_ :						Optional[Any]=32							,    lowerCAmelCase_ :						Optional[Any]=5							,    lowerCAmelCase_ :						str=4							,    lowerCAmelCase_ :						Any=37							,    lowerCAmelCase_ :						List[Any]="gelu"							,    lowerCAmelCase_ :						List[str]=0.1							,    lowerCAmelCase_ :						Optional[Any]=0.1							,    lowerCAmelCase_ :						int=10							,    lowerCAmelCase_ :						Union[str, Any]=0.0_2							,    )    ->							List[str]:


														"""simple docstring"""


														_a			  =       parent
														_a			  =       batch_size
														_a			  =       image_size
														_a			  =       patch_size
														_a			  =       num_channels
														_a			  =       is_training
														_a			  =       use_labels
														_a			  =       hidden_size
														_a			  =       num_hidden_layers
														_a			  =       num_attention_heads
														_a			  =       intermediate_size
														_a			  =       hidden_act
														_a			  =       hidden_dropout_prob
														_a			  =       attention_probs_dropout_prob
														_a			  =       type_sequence_label_size
														_a			  =       initializer_range

														# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														_a			  =       (image_size // patch_size) ** 2
														_a			  =       num_patches + 1







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


														"""simple docstring"""


														_a			  =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

														_a			  =       ViTConfig(
														    image_size=self.image_size							,    patch_size=self.patch_size							,    num_channels=self.num_channels							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    is_decoder=lowerCAmelCase_							,    initializer_range=self.initializer_range							,    )

														return config, pixel_values







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						List[str]      )    ->							int:


														"""simple docstring"""


														_a			  =       FlaxViTModel(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
														_a			  =       (self.image_size, self.image_size)
														_a			  =       (self.patch_size, self.patch_size)
														_a			  =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
														self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, num_patches + 1, self.hidden_size)      )







							def      __lowerCAmelCase    (						self :						int							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						List[str]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       self.type_sequence_label_size
														_a			  =       FlaxViTForImageClassification(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.type_sequence_label_size)      )

														# test greyscale images
														_a			  =       1
														_a			  =       FlaxViTForImageClassification(lowerCAmelCase_      )

														_a			  =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
														_a			  =       model(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       self.prepare_config_and_inputs()
														(
														    (
														    _a							
														)  ,							(
														    _a							
														)  ,														
														)			  =       config_and_inputs
														_a			  =       {'''pixel_values''': pixel_values}
														return config, inputs_dict



@require_flax
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







							def      __lowerCAmelCase    (						self :						Any      )    ->							None:


														"""simple docstring"""


														_a			  =       FlaxViTModelTester(self      )
														_a			  =       ConfigTester(self							,    config_class=lowerCAmelCase_							,    has_text_modality=lowerCAmelCase_							,    hidden_size=37      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Any:


														"""simple docstring"""


														self.config_tester.run_common_tests()







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


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_      )







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


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_a			  =       model_class(lowerCAmelCase_      )
																					_a			  =       inspect.signature(model.__call__      )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					_a			  =       [*signature.parameters.keys()]

																					_a			  =       ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]							,    lowerCAmelCase_      )







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


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__      ):
																												_a			  =       self._prepare_for_class(lowerCAmelCase_							,    lowerCAmelCase_      )
																												_a			  =       model_class(lowerCAmelCase_      )

																												@jax.jit
																												def model_jitted(lowerCAmelCase_ :						Union[str, Any]							,    **lowerCAmelCase_ :						Optional[Any]      ):
																																			return model(pixel_values=lowerCAmelCase_							,    **lowerCAmelCase_      )

																												with self.subTest('''JIT Enabled'''      ):
																																			_a			  =       model_jitted(**lowerCAmelCase_      ).to_tuple()

																												with self.subTest('''JIT Disabled'''      ):
																																			with jax.disable_jit():
																																										_a			  =       model_jitted(**lowerCAmelCase_      ).to_tuple()

																												self.assertEqual(len(lowerCAmelCase_      )							,    len(lowerCAmelCase_      )      )
																												for jitted_output, output in zip(lowerCAmelCase_							,    lowerCAmelCase_      ):
																																			self.assertEqual(jitted_output.shape							,    output.shape      )







							@slow
							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""simple docstring"""


														for model_class_name in self.all_model_classes:
																					_a			  =       model_class_name.from_pretrained('''google/vit-base-patch16-224'''      )
																					_a			  =       model(np.ones((1, 3, 2_24, 2_24)      )      )
																					self.assertIsNotNone(lowerCAmelCase_      )







'''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     A					(	_a					):







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							List[Any]:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							str:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Dict:


														"""simple docstring"""


														with self.assertRaises(lowerCAmelCase_      ):
																					_a			  =       pa.array(TypedSequence([1, 2, 3]							,    try_type=Value('''bool'''      )							,    type=Value('''int64'''      )      )      )







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


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[Any]:


														"""simple docstring"""


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







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


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Any      )    ->							Tuple:


														"""simple docstring"""


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







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


														"""simple docstring"""


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







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


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Tuple:


														"""simple docstring"""


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







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


														"""simple docstring"""


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







							@require_pil
							def      __lowerCAmelCase    (						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=lowerCAmelCase_      ) 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'''							,    lowerCAmelCase_      )
																					self.assertFalse(kwargs['''optimize_list_casting''']      )



def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       pa.BufferReader(UpperCamelCase   ) if isinstance(UpperCamelCase				,		pa.Buffer   ) else pa.memory_map(UpperCamelCase   )
							_a			  =       pa.ipc.open_stream(UpperCamelCase   )
							_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 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							_a			  =       pa.schema(UpperCamelCase   ) if fields else None
							with ArrowWriter(stream=UpperCamelCase				,		schema=UpperCamelCase				,		writer_batch_size=UpperCamelCase   ) 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(UpperCamelCase				,		metadata=writer._schema.metadata   )
							_check_output(output.getvalue()				,		expected_num_chunks=num_examples if writer_batch_size == 1 else 1   )
def 	snake_case_					():





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							_a			  =       Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']   )}   )
							with ArrowWriter(stream=UpperCamelCase				,		features=UpperCamelCase   ) 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(UpperCamelCase   )
							_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(UpperCamelCase   )
@pytest.mark.parametrize('''writer_batch_size'''				,		[None, 1, 10]   )
def 	snake_case_					(UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							with ArrowWriter(
							    stream=UpperCamelCase				,		writer_batch_size=UpperCamelCase				,		hash_salt='''split_name'''				,		check_duplicates=UpperCamelCase				,		) as writer:
														with pytest.raises(UpperCamelCase   ):
																					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 	snake_case_					(UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							with ArrowWriter(
							    stream=UpperCamelCase				,		writer_batch_size=UpperCamelCase				,		hash_salt='''split_name'''				,		check_duplicates=UpperCamelCase				,		) as writer:
														with pytest.raises(UpperCamelCase   ):
																					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 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							with ArrowWriter(
							    stream=UpperCamelCase				,		writer_batch_size=UpperCamelCase				,		hash_salt='''split_name'''				,		check_duplicates=UpperCamelCase				,		) 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 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						List[str]   ):





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							_a			  =       pa.schema(UpperCamelCase   ) if fields else None
							with ArrowWriter(stream=UpperCamelCase				,		schema=UpperCamelCase				,		writer_batch_size=UpperCamelCase   ) 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(UpperCamelCase				,		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_					(UpperCamelCase :						Any				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							_a			  =       pa.schema(UpperCamelCase   ) if fields else None
							with ArrowWriter(stream=UpperCamelCase				,		schema=UpperCamelCase				,		writer_batch_size=UpperCamelCase   ) 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(UpperCamelCase				,		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_					(UpperCamelCase :						int				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							_a			  =       pa.schema(UpperCamelCase   ) if fields else None
							with ArrowWriter(stream=UpperCamelCase				,		schema=UpperCamelCase				,		writer_batch_size=UpperCamelCase   ) 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(UpperCamelCase				,		metadata=writer._schema.metadata   )
							_check_output(output.getvalue()				,		expected_num_chunks=num_examples if writer_batch_size == 1 else 1   )
def 	snake_case_					():





							'''simple docstring'''


							with tempfile.TemporaryDirectory() as tmp_dir:
														_a			  =       {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
														_a			  =       os.path.join(UpperCamelCase				,		'''test.arrow'''   )
														with ArrowWriter(path=UpperCamelCase				,		schema=pa.schema(UpperCamelCase   )   ) 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(UpperCamelCase				,		metadata=writer._schema.metadata   )
														_check_output(UpperCamelCase				,		1   )
def 	snake_case_					(UpperCamelCase :						List[str]   ):





							'''simple docstring'''


							if pa.types.is_list(UpperCamelCase   ):
														return get_base_dtype(arr_type.value_type   )
							else:
														return arr_type
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							if isinstance(lst[0]				,		UpperCamelCase   ):
														change_first_primitive_element_in_list(lst[0]				,		UpperCamelCase   )
							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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       pa.array(TypedSequence(UpperCamelCase				,		optimized_int_type=UpperCamelCase   )   )
							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_					(UpperCamelCase :						List[Any]				,		UpperCamelCase :						str				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       pa.array(OptimizedTypedSequence(UpperCamelCase				,		col=UpperCamelCase   )   )
							assert get_base_dtype(arr.type   ) == expected_dtype

							# not in range
							if col != "other":
														# avoids errors due to in-place modifications
														_a			  =       copy.deepcopy(UpperCamelCase   )
														_a			  =       np.iinfo(expected_dtype.to_pandas_dtype()   ).max + 1
														change_first_primitive_element_in_list(UpperCamelCase				,		UpperCamelCase   )
														_a			  =       pa.array(OptimizedTypedSequence(UpperCamelCase				,		col=UpperCamelCase   )   )
														assert get_base_dtype(arr.type   ) == pa.intaa()
@pytest.mark.parametrize('''raise_exception'''				,		[False, True]   )
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Optional[Any]   ):





							'''simple docstring'''


							_a			  =       str(tmp_path / '''dataset-train.arrow'''   )
							try:
														with ArrowWriter(path=UpperCamelCase   ) 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_					(UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       '''mock://dataset-train.arrow'''
							with ArrowWriter(path=UpperCamelCase				,		storage_options=mockfs.storage_options   ) as writer:
														assert isinstance(writer._fs				,		type(UpperCamelCase   )   )
														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(UpperCamelCase   )
def 	snake_case_					():





							'''simple docstring'''


							_a			  =       pa.BufferOutputStream()
							with ParquetWriter(stream=UpperCamelCase   ) 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(UpperCamelCase   )
							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_					(UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							import PIL.Image

							_a			  =       str(tmp_path / '''test_image_rgb.jpg'''   )
							PIL.Image.fromarray(np.zeros((5, 5)				,		dtype=np.uinta   )   ).save(UpperCamelCase				,		format='''png'''   )
							_a			  =       pa.BufferOutputStream()
							with ParquetWriter(
							    stream=UpperCamelCase				,		features=Features({'''image''': Image()}   )				,		embed_local_files=UpperCamelCase   ) as writer:
														writer.write({'''image''': image_path}   )
														writer.finalize()
							_a			  =       pa.BufferReader(output.getvalue()   )
							_a			  =       pq.read_table(UpperCamelCase   )
							_a			  =       pa_table.to_pydict()
							if embed_local_files:
														assert isinstance(out['''image'''][0]['''path''']				,		UpperCamelCase   )
														with open(UpperCamelCase				,		'''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_					():





							'''simple docstring'''


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

