
"""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(       lowerCamelCase      ):



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3]))
							self.assertEqual(arr.type							,						pa.intaa())



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






							'''simple docstring'''
							with self.assertRaises(__a):
												_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3])							,						type=pa.intaa())



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							with self.assertRaises(__a):
												_UpperCamelCase									=    pa.array(TypedSequence([1, 2, 3]							,						try_type=Value('''bool''')							,						type=Value('''int64''')))



		def 		UpperCAmelCase				(   self)			->			Tuple:






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



		def 		UpperCAmelCase				(   self)			->			int:






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



		def 		UpperCAmelCase				(   self)			->			int:






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



		def 		UpperCAmelCase				(   self)			->			Tuple:






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



		def 		UpperCAmelCase				(   self)			->			Any:






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



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






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



		def 		UpperCAmelCase				(   self)			->			Dict:






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



		def 		UpperCAmelCase				(   self)			->			Tuple:






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




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






							'''simple docstring'''
							import PIL.Image

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





def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferReader(__snake_case      ) if isinstance(__snake_case,	pa.Buffer      ) else pa.memory_map(__snake_case      )
					_UpperCamelCase									=    pa.ipc.open_stream(__snake_case      )
					_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[int]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']      )}      )
					with ArrowWriter(stream=__snake_case,	features=__snake_case      ) as writer:
										writer.write({'''labels''': 0}      )
										writer.write({'''labels''': 1}      )
										_UpperCamelCase					,    _UpperCamelCase									=    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									=    pa.BufferReader(output.getvalue()      )
					_UpperCamelCase									=    pa.ipc.open_stream(__snake_case      )
					_UpperCamelCase									=    f.read_all()
					_UpperCamelCase									=    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(__snake_case      )



@pytest.mark.parametrize('''writer_batch_size''',	[None, 1, 10]      )
def  lowerCamelCase__						(     __snake_case      )				->						Dict:


					"""simple docstring"""





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



@pytest.mark.parametrize('''writer_batch_size''',	[None, 2, 10]      )
def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





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



@pytest.mark.parametrize('''writer_batch_size''',	[None, 2, 10]      )
def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					with ArrowWriter(
					    stream=__snake_case,	writer_batch_size=__snake_case,	hash_salt='''split_name''',	check_duplicates=__snake_case,	) as writer:
										writer.write({'''col_1''': '''foo''', '''col_2''': 1},	key=1      )
										writer.write({'''col_1''': '''bar''', '''col_2''': 2},	key=2      )
										_UpperCamelCase					,    _UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    pa.schema(__snake_case      ) if fields else None
					with ArrowWriter(stream=__snake_case,	schema=__snake_case,	writer_batch_size=__snake_case      ) as writer:
										writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}      )
										writer.write_batch({'''col_1''': [], '''col_2''': []}      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__snake_case,	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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    pa.schema(__snake_case      ) if fields else None
					with ArrowWriter(stream=__snake_case,	schema=__snake_case,	writer_batch_size=__snake_case      ) as writer:
										writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}      )      )
										_UpperCamelCase					,    _UpperCamelCase									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__snake_case,	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  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





					_UpperCamelCase									=    pa.BufferOutputStream()
					_UpperCamelCase									=    pa.schema(__snake_case      ) if fields else None
					with ArrowWriter(stream=__snake_case,	schema=__snake_case,	writer_batch_size=__snake_case      ) 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									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					if not fields:
										_UpperCamelCase									=    {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
					assert writer._schema == pa.schema(__snake_case,	metadata=writer._schema.metadata      )
					_check_output(output.getvalue(),	expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



def  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					if isinstance(lst[0],	__snake_case      ):
										change_first_primitive_element_in_list(lst[0],	__snake_case      )
					else:
										_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.array(TypedSequence(__snake_case,	optimized_int_type=__snake_case      )      )
					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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    pa.array(OptimizedTypedSequence(__snake_case,	col=__snake_case      )      )
					assert get_base_dtype(arr.type      ) == expected_dtype

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



@pytest.mark.parametrize('''raise_exception''',	[False, True]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''mock://dataset-train.arrow'''
					with ArrowWriter(path=__snake_case,	storage_options=mockfs.storage_options      ) as writer:
										assert isinstance(writer._fs,	type(__snake_case      )      )
										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									=    writer.finalize()
					assert num_examples == 2
					assert num_bytes > 0
					assert mockfs.exists(__snake_case      )



def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





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



@require_pil
@pytest.mark.parametrize('''embed_local_files''',	[False, True]      )
def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					import PIL.Image

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


					"""simple docstring"""





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













'''simple docstring'''

import warnings

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






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     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(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

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

     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(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



