






import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


_A						      =				logging.get_logger(__name__)



def 		lowercase_    (     )							->							Optional[Any]:
    """simple docstring"""

    snake_case				  =      os.getenv("SM_HP_MP_PARAMETERS"				,	"{}"	)
    try:
        # Parse it and check the field "partitions" is included, it is required for model parallel.
        snake_case				  =      json.loads(A__	)
        if "partitions" not in smp_options:
            return False
    except json.JSONDecodeError:
        return False

    # Get the sagemaker specific framework parameters from mpi_options variable.
    snake_case				  =      os.getenv("SM_FRAMEWORK_PARAMS"				,	"{}"	)
    try:
        # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
        snake_case				  =      json.loads(A__	)
        if not mpi_options.get("sagemaker_mpi_enabled"				,	A__	):
            return False
    except json.JSONDecodeError:
        return False
    # Lastly, check if the `smdistributed` module is present.
    return importlib.util.find_spec("smdistributed"	) is not None


if is_sagemaker_model_parallel_available():
   import smdistributed.modelparallel.torch as smp

   smp.init()







@dataclass
class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							str     =	field(
         default=""							,				metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}							,				)





     def 	UpperCAmelCase(self   :					int     )		->		Tuple:
         super().__post_init__()
         warnings.warn(
             "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
             "`TrainingArguments` instead."       ,					_A       ,					)





     @cached_property
     def 	UpperCAmelCase(self   :					Optional[int]     )		->		"torch.device":
         logger.info("PyTorch: setting up devices"     )
         if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
             logger.warning(
                 "torch.distributed process group is initialized, but local_rank == -1. "
                 "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"     )
         if self.no_cuda:
             snake_case				  =      torch.device("cpu"     )
             snake_case				  =      0
         elif is_sagemaker_model_parallel_available():
             snake_case				  =      smp.local_rank()
             snake_case				  =      torch.device("cuda"       ,					_A     )
             snake_case				  =      1
         elif is_sagemaker_dp_enabled():
             import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

             torch.distributed.init_process_group(backend="smddp"       ,					timeout=self.ddp_timeout_delta     )
             snake_case				  =      int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"     )     )
             snake_case				  =      torch.device("cuda"       ,					self.local_rank     )
             snake_case				  =      1
         elif self.local_rank == -1:
             # if n_gpu is > 1 we'll use nn.DataParallel.
             # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
             # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
             # trigger an error that a device index is missing. Index 0 takes into account the
             # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
             # will use the first GPU in that env, i.e. GPU#1
             snake_case				  =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu"     )
             # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
             # the default value.
             snake_case				  =      torch.cuda.device_count()
         else:
             # Here, we'll use torch.distributed.
             # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
             if not torch.distributed.is_initialized():
                 torch.distributed.init_process_group(backend="nccl"       ,					timeout=self.ddp_timeout_delta     )
             snake_case				  =      torch.device("cuda"       ,					self.local_rank     )
             snake_case				  =      1

         if device.type == "cuda":
             torch.cuda.set_device(_A     )

         return device





     @property
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		int:
         if is_sagemaker_model_parallel_available():
             return smp.dp_size()

         return super().world_size





     @property
     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict:
         return not is_sagemaker_model_parallel_available()





     @property
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Dict:
         return False



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   lowerCamelCase	(						A_						):





     def 	UpperCAmelCase(self   :					str     )		->		Dict:
         snake_case				  =      pa.array(TypedSequence([1, 2, 3]     )     )
         self.assertEqual(arr.type       ,					pa.intaa()     )





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Optional[int]:
         with self.assertRaises(_A     ):
             snake_case				  =      pa.array(TypedSequence([1, 2, 3]     )       ,					type=pa.intaa()     )





     def 	UpperCAmelCase(self   :					Dict     )		->		str:
         with self.assertRaises(_A     ):
             snake_case				  =      pa.array(TypedSequence([1, 2, 3]       ,					try_type=Value("bool"     )       ,					type=Value("int64"     )     )     )





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





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





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





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Optional[int]:
         snake_case				  =      pa.array(TypedSequence(["foo", "bar"]       ,					try_type=Value("int64"     )     )     )
         self.assertEqual(arr.type       ,					pa.string()     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         snake_case				  =      pa.array(TypedSequence([[[1, 2, 3]]]       ,					type=ArrayaD((1, 3)       ,					"int64"     )     )     )
         self.assertEqual(arr.type       ,					ArrayaDExtensionType((1, 3)       ,					"int64"     )     )





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





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





     def 	UpperCAmelCase(self   :					str     )		->		Dict:
         snake_case				  =      pa.array(TypedSequence(["foo", "bar"]       ,					try_type=ArrayaD((1, 3)       ,					"int64"     )     )     )
         self.assertEqual(arr.type       ,					pa.string()     )





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

         snake_case				  =      PIL.Image.fromarray(np.arange(1_0       ,					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 		lowercase_    (     A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      pa.BufferReader(A__	) if isinstance(A__				,	pa.Buffer	) else pa.memory_map(A__	)
    snake_case				  =      pa.ipc.open_stream(A__	)
    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, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write({"col_1": "foo", "col_2": 1}	)
        writer.write({"col_1": "bar", "col_2": 2}	)
        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(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



def 		lowercase_    (     )							->							str:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      Features({"labels": ClassLabel(names=["neg", "pos"]	)}	)
    with ArrowWriter(stream=A__				,	features=A__	) 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(A__	)
    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(A__	)



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
def 		lowercase_    (     A__	)							->							int:
    """simple docstring"""

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



@pytest.mark.parametrize("writer_batch_size"				,	[None, 2, 10]	)
def 		lowercase_    (     A__	)							->							str:
    """simple docstring"""

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



@pytest.mark.parametrize("writer_batch_size"				,	[None, 2, 10]	)
def 		lowercase_    (     A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    with ArrowWriter(
        stream=A__				,	writer_batch_size=A__				,	hash_salt="split_name"				,	check_duplicates=A__				,	) as writer:
        writer.write({"col_1": "foo", "col_2": 1}				,	key=1	)
        writer.write({"col_1": "bar", "col_2": 2}				,	key=2	)
        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, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							Dict:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)
        writer.write_batch({"col_1": [], "col_2": []}	)
        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(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							Union[str, Any]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}	)	)
        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(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



@pytest.mark.parametrize("writer_batch_size"				,	[None, 1, 10]	)
@pytest.mark.parametrize(
    "fields"				,	[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]	)
def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    snake_case				  =      pa.schema(A__	) if fields else None
    with ArrowWriter(stream=A__				,	schema=A__				,	writer_batch_size=A__	) as writer:
        writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}	)	)
        writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}	)	)
        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(A__				,	metadata=writer._schema.metadata	)
    _check_output(output.getvalue()				,	expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

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



def 		lowercase_    (     A__	)							->							int:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__	)							->							Any:
    """simple docstring"""

    if isinstance(lst[0]				,	A__	):
        change_first_primitive_element_in_list(lst[0]				,	A__	)
    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 		lowercase_    (     A__				,	A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      pa.array(TypedSequence(A__				,	optimized_int_type=A__	)	)
    assert get_base_dtype(arr.type	) == expected_dtype



@pytest.mark.parametrize(
    "col, expected_dtype"				,	[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]				,	)
@pytest.mark.parametrize("sequence"				,	[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 		lowercase_    (     A__				,	A__				,	A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      pa.array(OptimizedTypedSequence(A__				,	col=A__	)	)
    assert get_base_dtype(arr.type	) == expected_dtype

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



@pytest.mark.parametrize("raise_exception"				,	[False, True]	)
def 		lowercase_    (     A__				,	A__	)							->							Optional[Any]:
    """simple docstring"""

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



def 		lowercase_    (     A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      "mock://dataset-train.arrow"
    with ArrowWriter(path=A__				,	storage_options=mockfs.storage_options	) as writer:
        assert isinstance(writer._fs				,	type(A__	)	)
        assert writer._fs.storage_options == mockfs.storage_options
        writer.write({"col_1": "foo", "col_2": 1}	)
        writer.write({"col_1": "bar", "col_2": 2}	)
        snake_case  ,							snake_case				  =      writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert mockfs.exists(A__	)



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      pa.BufferOutputStream()
    with ParquetWriter(stream=A__	) 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(A__	)
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}



@require_pil
@pytest.mark.parametrize("embed_local_files"				,	[False, True]	)
def 		lowercase_    (     A__				,	A__	)							->							Dict:
    """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(A__				,	format="png"	)
    snake_case				  =      pa.BufferOutputStream()
    with ParquetWriter(
        stream=A__				,	features=Features({"image": Image()}	)				,	embed_local_files=A__	) as writer:
        writer.write({"image": image_path}	)
        writer.finalize()
    snake_case				  =      pa.BufferReader(output.getvalue()	)
    snake_case				  =      pq.read_table(A__	)
    snake_case				  =      pa_table.to_pydict()
    if embed_local_files:
        assert isinstance(out["image"][0]["path"]				,	A__	)
        with open(A__				,	"rb"	) as f:
            assert out["image"][0]["bytes"] == f.read()
    else:
        assert out["image"][0]["path"] == image_path
        assert out["image"][0]["bytes"] is None





def 		lowercase_    (     )							->							List[Any]:
    """simple docstring"""

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

