

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCAmelCase  :      int			 =logging.get_logger(__name__)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   ["input_features"]





					def __init__(				self				,      lowerCamelCase__=8_0				,      lowerCamelCase__=1_6_0_0_0				,      lowerCamelCase__=1_6_0				,      lowerCamelCase__=3_0				,      lowerCamelCase__=4_0_0				,      lowerCamelCase__=0.0				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						super().__init__(
						    feature_size=lowerCamelCase__				,      sampling_rate=lowerCamelCase__				,      padding_value=lowerCamelCase__				,      return_attention_mask=lowerCamelCase__				,      **lowerCamelCase__				,      )
						UpperCAmelCase__:	Any									= n_fft
						UpperCAmelCase__:	List[Any]									= hop_length
						UpperCAmelCase__:	Union[str, Any]									= chunk_length
						UpperCAmelCase__:	Optional[int]									= chunk_length * sampling_rate
						UpperCAmelCase__:	Tuple									= self.n_samples // hop_length
						UpperCAmelCase__:	Optional[int]									= sampling_rate
						UpperCAmelCase__:	Optional[int]									= mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2				,      num_mel_filters=lowerCamelCase__				,      min_frequency=0.0				,      max_frequency=8_000.0				,      sampling_rate=lowerCamelCase__				,      norm="slaney"				,      mel_scale="slaney"				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[Any]									= spectrogram(
						    lowerCamelCase__				,      window_function(self.n_fft				,      "hann"						)				,      frame_length=self.n_fft				,      hop_length=self.hop_length				,      power=2.0				,      mel_filters=self.mel_filters				,      log_mel="log10"				,      )
						UpperCAmelCase__:	Optional[int]									= log_spec[:, :-1]
						UpperCAmelCase__:	Optional[int]									= np.maximum(lowerCamelCase__				,      log_spec.max() - 8.0						)
						UpperCAmelCase__:	Any									= (log_spec + 4.0) / 4.0
						return log_spec





					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def    _UpperCAmelCase    (				lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 0.0						):
						if attention_mask is not None:
							UpperCAmelCase__:	Optional[Any]									= np.array(lowerCamelCase__				,      np.intaa						)
							UpperCAmelCase__:	str									= []

							for vector, length in zip(lowerCamelCase__				,      attention_mask.sum(-1						)						):
								UpperCAmelCase__:	Any									= (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
								if length < normed_slice.shape[0]:
									UpperCAmelCase__:	List[str]									= padding_value

								normed_input_values.append(lowerCamelCase__						)
						else:
							UpperCAmelCase__:	Union[str, Any]									= [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

						return normed_input_values





					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__ = True				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "max_length"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):

						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
								    F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
								    F" was sampled with {self.sampling_rate} and not {sampling_rate}."						)
						else:
							logger.warning(
							    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
							    "Failing to do so can result in silent errors that might be hard to debug."						)

						UpperCAmelCase__:	Dict									= isinstance(lowerCamelCase__				,      np.ndarray						) and len(raw_speech.shape						) > 1
						if is_batched_numpy and len(raw_speech.shape						) > 2:
							raise ValueError(F"Only mono-channel audio is supported for input to {self}"						)
						UpperCAmelCase__:	Optional[int]									= is_batched_numpy or (
						    isinstance(lowerCamelCase__				,      (list, tuple)						) and (isinstance(raw_speech[0]				,      (np.ndarray, tuple, list)						))
						)

						if is_batched:
							UpperCAmelCase__:	str									= [np.asarray([speech]				,      dtype=np.floataa						).T for speech in raw_speech]
						elif not is_batched and not isinstance(lowerCamelCase__				,      np.ndarray						):
							UpperCAmelCase__:	List[Any]									= np.asarray(lowerCamelCase__				,      dtype=np.floataa						)
						elif isinstance(lowerCamelCase__				,      np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
							UpperCAmelCase__:	Dict									= raw_speech.astype(np.floataa						)

						# always return batch
						if not is_batched:
							UpperCAmelCase__:	Any									= [np.asarray([raw_speech]						).T]

						UpperCAmelCase__:	Tuple									= BatchFeature({"input_features": raw_speech}						)

						# convert into correct format for padding

						UpperCAmelCase__:	Optional[int]									= self.pad(
						    lowerCamelCase__				,      padding=lowerCamelCase__				,      max_length=max_length if max_length else self.n_samples				,      truncation=lowerCamelCase__				,      pad_to_multiple_of=lowerCamelCase__				,      return_attention_mask=return_attention_mask or do_normalize				,      )

						# zero-mean and unit-variance normalization
						if do_normalize:
							UpperCAmelCase__:	int									= self.zero_mean_unit_var_norm(
							    padded_inputs["input_features"]				,      attention_mask=padded_inputs["attention_mask"]				,      padding_value=self.padding_value				,      )
							UpperCAmelCase__:	Tuple									= np.stack(padded_inputs["input_features"]				,      axis=0						)

						# make sure list is in array format
						UpperCAmelCase__:	Dict									= padded_inputs.get("input_features"						).transpose(2				,      0				,      1						)

						UpperCAmelCase__:	int									= [self._np_extract_fbank_features(lowerCamelCase__						) for waveform in input_features[0]]

						if isinstance(input_features[0]				,      lowerCamelCase__						):
							UpperCAmelCase__:	int									= [np.asarray(lowerCamelCase__				,      dtype=np.floataa						) for feature in input_features]
						else:
							UpperCAmelCase__:	List[str]									= input_features

						if return_attention_mask:
							# rescale from sample (48000) to feature (3000)
							UpperCAmelCase__:	Optional[int]									= padded_inputs["attention_mask"][:, :: self.hop_length]

						if return_tensors is not None:
							UpperCAmelCase__:	int									= padded_inputs.convert_to_tensors(lowerCamelCase__						)

						return padded_inputs





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= copy.deepcopy(self.__dict__						)
						UpperCAmelCase__:	Dict									= self.__class__.__name__
						if "mel_filters" in output:
							del output["mel_filters"]
						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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([1, 2, 3]						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises(lowerCamelCase__						):
							UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([1, 2, 3]						)				,      type=pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises(lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= pa.array(TypedSequence([1, 2, 3]				,      try_type=Value("bool"						)				,      type=Value("int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= pa.array(TypedSequence([1, 2, 3]				,      type=Value("int32"						)						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						with self.assertRaises((TypeError, pa.lib.ArrowInvalid)						):
							UpperCAmelCase__:	List[str]									= pa.array(TypedSequence(["foo", "bar"]				,      type=Value("int64"						)						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= pa.array(TypedSequence([1, 2, 3]				,      try_type=Value("int32"						)						)						)
						self.assertEqual(arr.type				,      pa.intaa()						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= pa.array(TypedSequence(["foo", "bar"]				,      try_type=Value("int64"						)						)						)
						self.assertEqual(arr.type				,      pa.string()						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= pa.array(TypedSequence([[[1, 2, 3]]]				,      type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      ArrayaDExtensionType((1, 3)				,      "int64"						)						)





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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= pa.array(TypedSequence([[[1, 2, 3]]]				,      try_type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      ArrayaDExtensionType((1, 3)				,      "int64"						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= pa.array(TypedSequence(["foo", "bar"]				,      try_type=ArrayaD((1, 3)				,      "int64"						)						)						)
						self.assertEqual(arr.type				,      pa.string()						)





					@require_pil
					def    _UpperCAmelCase    (				self						):
						import PIL.Image

						UpperCAmelCase__:	int									= PIL.Image.fromarray(np.arange(1_0				,      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__:	Tuple									= pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]				,      type=Image()						)						)
							UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= mock_cast_to_python_objects.call_args_list[-1]
							self.assertIn("optimize_list_casting"				,      lowerCamelCase__						)
							self.assertFalse(kwargs["optimize_list_casting"]						)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= pa.BufferReader(SCREAMING_SNAKE_CASE      ) if isinstance(SCREAMING_SNAKE_CASE	,pa.Buffer      ) else pa.memory_map(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	str									= pa.ipc.open_stream(SCREAMING_SNAKE_CASE      )
	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, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	UpperCAmelCase__:	Tuple									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Optional[int]									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



def 			_A	(      ):
	UpperCAmelCase__:	Tuple									= pa.BufferOutputStream()
	UpperCAmelCase__:	Any									= Features({"labels": ClassLabel(names=["neg", "pos"]      )}      )
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,features=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"labels": 0}      )
		writer.write({"labels": 1}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= 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__:	Dict									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	Any									= pa.ipc.open_stream(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	pa.Table									= f.read_all()
	UpperCAmelCase__:	Union[str, Any]									= 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(SCREAMING_SNAKE_CASE      )



@pytest.mark.parametrize("writer_batch_size"	,[None, 1, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		with pytest.raises(SCREAMING_SNAKE_CASE      ):
			writer.write({"col_1": "foo", "col_2": 1}	,key=[1, 2]      )
			UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()



@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_SNAKE_CASE	,) as writer:
		with pytest.raises(SCREAMING_SNAKE_CASE      ):
			writer.write({"col_1": "foo", "col_2": 1}	,key=1_0      )
			writer.write({"col_1": "bar", "col_2": 2}	,key=1_0      )
			UpperCAmelCase__							,						UpperCAmelCase__:	str									= writer.finalize()



@pytest.mark.parametrize("writer_batch_size"	,[None, 2, 1_0]      )
def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Dict									= pa.BufferOutputStream()
	with ArrowWriter(
	    stream=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE	,hash_salt="split_name"	,check_duplicates=SCREAMING_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__:	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, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= pa.BufferOutputStream()
	UpperCAmelCase__:	Optional[Any]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )
		writer.write_batch({"col_1": [], "col_2": []}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	Optional[Any]									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_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, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= pa.BufferOutputStream()
	UpperCAmelCase__:	List[str]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Any									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	if not fields:
		UpperCAmelCase__:	str									= {"col_1": pa.string(), "col_2": pa.intaa()}
	assert writer._schema == pa.schema(SCREAMING_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, 1_0]      )
@pytest.mark.parametrize(
    "fields"	,[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= pa.BufferOutputStream()
	UpperCAmelCase__:	List[Any]									= pa.schema(SCREAMING_SNAKE_CASE      ) if fields else None
	with ArrowWriter(stream=SCREAMING_SNAKE_CASE	,schema=SCREAMING_SNAKE_CASE	,writer_batch_size=SCREAMING_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__:	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(SCREAMING_SNAKE_CASE	,metadata=writer._schema.metadata      )
	_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )



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



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	if pa.types.is_list(SCREAMING_SNAKE_CASE      ):
		return get_base_dtype(arr_type.value_type      )
	else:
		return arr_type



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if isinstance(lst[0]	,SCREAMING_SNAKE_CASE      ):
		change_first_primitive_element_in_list(lst[0]	,SCREAMING_SNAKE_CASE      )
	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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= pa.array(TypedSequence(SCREAMING_SNAKE_CASE	,optimized_int_type=SCREAMING_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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	# in range
	UpperCAmelCase__:	List[Any]									= pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE	,col=SCREAMING_SNAKE_CASE      )      )
	assert get_base_dtype(arr.type      ) == expected_dtype

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



@pytest.mark.parametrize("raise_exception"	,[False, True]      )
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= str(tmp_path / "dataset-train.arrow"      )
	try:
		with ArrowWriter(path=SCREAMING_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 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= "mock://dataset-train.arrow"
	with ArrowWriter(path=SCREAMING_SNAKE_CASE	,storage_options=mockfs.storage_options      ) as writer:
		assert isinstance(writer._fs	,type(SCREAMING_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__:	List[Any]									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	assert mockfs.exists(SCREAMING_SNAKE_CASE      )



def 			_A	(      ):
	UpperCAmelCase__:	Dict									= pa.BufferOutputStream()
	with ParquetWriter(stream=SCREAMING_SNAKE_CASE      ) as writer:
		writer.write({"col_1": "foo", "col_2": 1}      )
		writer.write({"col_1": "bar", "col_2": 2}      )
		UpperCAmelCase__							,						UpperCAmelCase__:	str									= writer.finalize()
	assert num_examples == 2
	assert num_bytes > 0
	UpperCAmelCase__:	str									= pa.BufferReader(output.getvalue()      )
	UpperCAmelCase__:	pa.Table									= pq.read_table(SCREAMING_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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	import PIL.Image

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