




'''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  _snake_case		(	lowercase_							):




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




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




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

										'''simple docstring'''




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




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

										'''simple docstring'''




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




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

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




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




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

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




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




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

										'''simple docstring'''




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




			@require_pil
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										import PIL.Image

										snake_case_						=      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:
																	snake_case_						=      pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]				,							type=Image()							)							)
																	snake_case_					,      snake_case_						=      mock_cast_to_python_objects.call_args_list[-1]
																	self.assertIn("optimize_list_casting"				,							a__							)
																	self.assertFalse(kwargs["optimize_list_casting"]							)


def      UpperCamelCase_(	snake_case							:       Any							,				snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      pa.BufferReader(snake_case     ) if isinstance(snake_case							,				pa.Buffer     ) else pa.memory_map(snake_case     )
							snake_case_						=      pa.ipc.open_stream(snake_case     )
							snake_case_						=      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      UpperCamelCase_(	snake_case							:       Dict							,				snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      pa.BufferOutputStream()
							snake_case_						=      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}     )
														snake_case_					,      snake_case_						=      writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_						=      {"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      UpperCamelCase_(	):

							'''simple docstring'''






							snake_case_						=      pa.BufferOutputStream()
							snake_case_						=      Features({"labels": ClassLabel(names=["neg", "pos"]     )}     )
							with ArrowWriter(stream=snake_case							,				features=snake_case     ) as writer:
														writer.write({"labels": 0}     )
														writer.write({"labels": 1}     )
														snake_case_					,      snake_case_						=      writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							assert writer._schema == features.arrow_schema
							assert writer._schema.metadata == features.arrow_schema.metadata
							snake_case_						=      pa.BufferReader(output.getvalue()     )
							snake_case_						=      pa.ipc.open_stream(snake_case     )
							snake_case_						=      f.read_all()
							snake_case_						=      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, 1_0]     )
def      UpperCamelCase_(	snake_case							:       Optional[Any]     ):

							'''simple docstring'''






							snake_case_						=      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]     )
																					snake_case_					,      snake_case_						=      writer.finalize()


@pytest.mark.parametrize("writer_batch_size"							,				[None, 2, 1_0]     )
def      UpperCamelCase_(	snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      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_0     )
																					writer.write({"col_1": "bar", "col_2": 2}							,				key=1_0     )
																					snake_case_					,      snake_case_						=      writer.finalize()


@pytest.mark.parametrize("writer_batch_size"							,				[None, 2, 1_0]     )
def      UpperCamelCase_(	snake_case							:       Any     ):

							'''simple docstring'''






							snake_case_						=      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     )
														snake_case_					,      snake_case_						=      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      UpperCamelCase_(	snake_case							:       Union[str, Any]							,				snake_case							:       List[str]     ):

							'''simple docstring'''






							snake_case_						=      pa.BufferOutputStream()
							snake_case_						=      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": []}     )
														snake_case_					,      snake_case_						=      writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_						=      {"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, 1_0]     )
@pytest.mark.parametrize(
    "fields"							,				[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]     )
def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      pa.BufferOutputStream()
							snake_case_						=      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]}     )     )
														snake_case_					,      snake_case_						=      writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_						=      {"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, 1_0]     )
@pytest.mark.parametrize(
    "fields"							,				[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]     )
def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Optional[int]     ):

							'''simple docstring'''






							snake_case_						=      pa.BufferOutputStream()
							snake_case_						=      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]}     )     )
														snake_case_					,      snake_case_						=      writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							if not fields:
														snake_case_						=      {"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      UpperCamelCase_(	):

							'''simple docstring'''






							with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_						=      {"col_1": pa.string(), "col_2": pa.intaa()}
														snake_case_						=      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]}     )
																					snake_case_					,      snake_case_						=      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      UpperCamelCase_(	snake_case							:       Tuple     ):

							'''simple docstring'''






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


def      UpperCamelCase_(	snake_case							:       Tuple							,				snake_case							:       Dict     ):

							'''simple docstring'''






							if isinstance(lst[0]							,				snake_case     ):
														change_first_primitive_element_in_list(lst[0]							,				snake_case     )
							else:
														snake_case_						=      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      UpperCamelCase_(	snake_case							:       List[Any]							,				snake_case							:       Dict							,				snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      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      UpperCamelCase_(	snake_case							:       Optional[int]							,				snake_case							:       Any							,				snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      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
														snake_case_						=      copy.deepcopy(snake_case     )
														snake_case_						=      np.iinfo(expected_dtype.to_pandas_dtype()     ).max + 1
														change_first_primitive_element_in_list(snake_case							,				snake_case     )
														snake_case_						=      pa.array(OptimizedTypedSequence(snake_case							,				col=snake_case     )     )
														assert get_base_dtype(arr.type     ) == pa.intaa()


@pytest.mark.parametrize("raise_exception"							,				[False, True]     )
def      UpperCamelCase_(	snake_case							:       List[str]							,				snake_case							:       Union[str, Any]     ):

							'''simple docstring'''






							snake_case_						=      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      UpperCamelCase_(	snake_case							:       Union[str, Any]     ):

							'''simple docstring'''






							snake_case_						=      "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}     )
														snake_case_					,      snake_case_						=      writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							assert mockfs.exists(snake_case     )


def      UpperCamelCase_(	):

							'''simple docstring'''






							snake_case_						=      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}     )
														snake_case_					,      snake_case_						=      writer.finalize()
							assert num_examples == 2
							assert num_bytes > 0
							snake_case_						=      pa.BufferReader(output.getvalue()     )
							snake_case_						=      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      UpperCamelCase_(	snake_case							:       Optional[int]							,				snake_case							:       List[str]     ):

							'''simple docstring'''






							import PIL.Image

							snake_case_						=      str(tmp_path / "test_image_rgb.jpg"     )
							PIL.Image.fromarray(np.zeros((5, 5)							,				dtype=np.uinta     )     ).save(snake_case							,				format="png"     )
							snake_case_						=      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()
							snake_case_						=      pa.BufferReader(output.getvalue()     )
							snake_case_						=      pq.read_table(snake_case     )
							snake_case_						=      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      UpperCamelCase_(	):

							'''simple docstring'''






							snake_case_						=      pa.schema([pa.field("col_1"							,				pa.string()							,				nullable=snake_case     )]     )
							snake_case_						=      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()     )]     )







import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



