


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     __lowercase    (				_snake_case   ):



		"""simple docstring"""

		def  __A					(      self     )   ->      Any:







					'''simple docstring'''







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







		def  __A					(      self     )   ->      str:







					'''simple docstring'''







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







		def  __A					(      self     )   ->      Any:







					'''simple docstring'''







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







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







					'''simple docstring'''







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







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







					'''simple docstring'''







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







		def  __A					(      self     )   ->      str:







					'''simple docstring'''







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







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







					'''simple docstring'''







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







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







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







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







					'''simple docstring'''







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







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







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







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







					'''simple docstring'''







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







		@require_pil
		def  __A					(      self     )   ->      str:







					'''simple docstring'''







					import PIL.Image

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






def      __lowerCamelCase		(				lowerCamelCase__						:		Optional[int]   ,				lowerCamelCase__						:		int	):





			'''simple docstring'''




			lowerCamelCase  						=   pa.BufferReader(UpperCAmelCase_	) if isinstance(UpperCAmelCase_   ,				pa.Buffer	) else pa.memory_map(UpperCAmelCase_	)
			lowerCamelCase  						=   pa.ipc.open_stream(UpperCAmelCase_	)
			lowerCamelCase  						=   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		(				lowerCamelCase__						:		Any   ,				lowerCamelCase__						:		List[Any]	):





			'''simple docstring'''




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






def      __lowerCamelCase		(				):





			'''simple docstring'''




			lowerCamelCase  						=   pa.BufferOutputStream()
			lowerCamelCase  						=   Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]	)}	)
			with ArrowWriter(stream=UpperCAmelCase_   ,				features=UpperCAmelCase_	) as writer:
						writer.write({"""labels""": 0}	)
						writer.write({"""labels""": 1}	)
						lowerCamelCase  						=   writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert writer._schema == features.arrow_schema
			assert writer._schema.metadata == features.arrow_schema.metadata
			lowerCamelCase  						=   pa.BufferReader(output.getvalue()	)
			lowerCamelCase  						=   pa.ipc.open_stream(UpperCAmelCase_	)
			lowerCamelCase  						=   f.read_all()
			lowerCamelCase  						=   pa_table.schema
			assert pa_table.num_rows == 2
			assert schema == features.arrow_schema
			assert schema.metadata == features.arrow_schema.metadata
			assert features == Features.from_arrow_schema(UpperCAmelCase_	)






@pytest.mark.parametrize("""writer_batch_size"""   ,				[None, 1, 10]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		List[str]	):





			'''simple docstring'''




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






@pytest.mark.parametrize("""writer_batch_size"""   ,				[None, 2, 10]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		Union[str, Any]	):





			'''simple docstring'''




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






@pytest.mark.parametrize("""writer_batch_size"""   ,				[None, 2, 10]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		str	):





			'''simple docstring'''




			lowerCamelCase  						=   pa.BufferOutputStream()
			with ArrowWriter(
			    stream=UpperCAmelCase_   ,				writer_batch_size=UpperCAmelCase_   ,				hash_salt="""split_name"""   ,				check_duplicates=UpperCAmelCase_   ,				) as writer:
						writer.write({"""col_1""": """foo""", """col_2""": 1}   ,				key=1	)
						writer.write({"""col_1""": """bar""", """col_2""": 2}   ,				key=2	)
						lowerCamelCase  						=   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		(				lowerCamelCase__						:		Any   ,				lowerCamelCase__						:		List[Any]	):





			'''simple docstring'''




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






@pytest.mark.parametrize("""writer_batch_size"""   ,				[None, 1, 10]	)
@pytest.mark.parametrize(
    """fields"""   ,				[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		Tuple   ,				lowerCamelCase__						:		Any	):





			'''simple docstring'''




			lowerCamelCase  						=   pa.BufferOutputStream()
			lowerCamelCase  						=   pa.schema(UpperCAmelCase_	) if fields else None
			with ArrowWriter(stream=UpperCAmelCase_   ,				schema=UpperCAmelCase_   ,				writer_batch_size=UpperCAmelCase_	) as writer:
						writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}	)	)
						lowerCamelCase  						=   writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						lowerCamelCase  						=   {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
			assert writer._schema == pa.schema(UpperCAmelCase_   ,				metadata=writer._schema.metadata	)
			_check_output(output.getvalue()   ,				expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)






@pytest.mark.parametrize("""writer_batch_size"""   ,				[None, 1, 10]	)
@pytest.mark.parametrize(
    """fields"""   ,				[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		str   ,				lowerCamelCase__						:		Union[str, Any]	):





			'''simple docstring'''




			lowerCamelCase  						=   pa.BufferOutputStream()
			lowerCamelCase  						=   pa.schema(UpperCAmelCase_	) if fields else None
			with ArrowWriter(stream=UpperCAmelCase_   ,				schema=UpperCAmelCase_   ,				writer_batch_size=UpperCAmelCase_	) as writer:
						writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}	)	)
						writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}	)	)
						lowerCamelCase  						=   writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						lowerCamelCase  						=   {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
			assert writer._schema == pa.schema(UpperCAmelCase_   ,				metadata=writer._schema.metadata	)
			_check_output(output.getvalue()   ,				expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)






def      __lowerCamelCase		(				):





			'''simple docstring'''




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






def      __lowerCamelCase		(				lowerCamelCase__						:		Union[str, Any]	):





			'''simple docstring'''




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






def      __lowerCamelCase		(				lowerCamelCase__						:		Any   ,				lowerCamelCase__						:		List[Any]	):





			'''simple docstring'''




			if isinstance(lst[0]   ,				UpperCAmelCase_	):
						change_first_primitive_element_in_list(lst[0]   ,				UpperCAmelCase_	)
			else:
						lowerCamelCase  						=   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		(				lowerCamelCase__						:		Optional[Any]   ,				lowerCamelCase__						:		Any   ,				lowerCamelCase__						:		Any	):





			'''simple docstring'''




			lowerCamelCase  						=   pa.array(TypedSequence(UpperCAmelCase_   ,				optimized_int_type=UpperCAmelCase_	)	)
			assert get_base_dtype(arr.type	) == expected_dtype






@pytest.mark.parametrize(
    """col, expected_dtype"""   ,				[
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]   ,				)
@pytest.mark.parametrize("""sequence"""   ,				[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		Union[str, Any]   ,				lowerCamelCase__						:		str   ,				lowerCamelCase__						:		Any	):





			'''simple docstring'''




			lowerCamelCase  						=   pa.array(OptimizedTypedSequence(UpperCAmelCase_   ,				col=UpperCAmelCase_	)	)
			assert get_base_dtype(arr.type	) == expected_dtype

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






@pytest.mark.parametrize("""raise_exception"""   ,				[False, True]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		List[Any]   ,				lowerCamelCase__						:		Optional[int]	):





			'''simple docstring'''




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






def      __lowerCamelCase		(				lowerCamelCase__						:		Any	):





			'''simple docstring'''




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






def      __lowerCamelCase		(				):





			'''simple docstring'''




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






@require_pil
@pytest.mark.parametrize("""embed_local_files"""   ,				[False, True]	)
def      __lowerCamelCase		(				lowerCamelCase__						:		Union[str, Any]   ,				lowerCamelCase__						:		Tuple	):





			'''simple docstring'''




			import PIL.Image

			lowerCamelCase  						=   str(tmp_path / """test_image_rgb.jpg"""	)
			PIL.Image.fromarray(np.zeros((5, 5)   ,				dtype=np.uinta	)	).save(UpperCAmelCase_   ,				format="""png"""	)
			lowerCamelCase  						=   pa.BufferOutputStream()
			with ParquetWriter(
			    stream=UpperCAmelCase_   ,				features=Features({"""image""": Image()}	)   ,				embed_local_files=UpperCAmelCase_	) as writer:
						writer.write({"""image""": image_path}	)
						writer.finalize()
			lowerCamelCase  						=   pa.BufferReader(output.getvalue()	)
			lowerCamelCase  						=   pq.read_table(UpperCAmelCase_	)
			lowerCamelCase  						=   pa_table.to_pydict()
			if embed_local_files:
						assert isinstance(out["""image"""][0]["""path"""]   ,				UpperCAmelCase_	)
						with open(UpperCAmelCase_   ,				"""rb"""	) as f:
									assert out["image"][0]["bytes"] == f.read()
			else:
						assert out["image"][0]["path"] == image_path
						assert out["image"][0]["bytes"] is None






def      __lowerCamelCase		(				):





			'''simple docstring'''




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





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case				:    int       				=     '''Create a default config file for Accelerate with only a few flags set.'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any]="no" ,  UpperCAmelCase_						:		str = default_json_config_file ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[str] 							=       Path(UpperCAmelCase_						)
      path.parent.mkdir(parents=UpperCAmelCase_ ,  exist_ok=UpperCAmelCase_						)
      if path.exists():
            print(
                F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''						)
            return False
      a						:Optional[Any] 							=       mixed_precision.lower()
      if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
            raise ValueError(
                F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''						)
      a						:List[Any] 							=       {
          '''compute_environment''': '''LOCAL_MACHINE''',
          '''mixed_precision''': mixed_precision,
      }
      if torch.cuda.is_available():
            a						:Dict 							=       torch.cuda.device_count()
            a						:Tuple 							=       num_gpus
            a						:int 							=       False
            if num_gpus > 1:
                  a						:str 							=       '''MULTI_GPU'''
            else:
                  a						:List[Any] 							=       '''NO'''
      elif is_xpu_available() and use_xpu:
            a						:List[Any] 							=       torch.xpu.device_count()
            a						:Optional[int] 							=       num_xpus
            a						:List[Any] 							=       False
            if num_xpus > 1:
                  a						:int 							=       '''MULTI_XPU'''
            else:
                  a						:str 							=       '''NO'''
      elif is_npu_available():
            a						:List[str] 							=       torch.npu.device_count()
            a						:Any 							=       num_npus
            a						:Optional[int] 							=       False
            if num_npus > 1:
                  a						:List[str] 							=       '''MULTI_NPU'''
            else:
                  a						:Dict 							=       '''NO'''
      else:
            a						:str 							=       0
            a						:Optional[Any] 							=       True
            a						:Optional[Any] 							=       1
            a						:str 							=       '''NO'''
      a						:List[str] 							=       ClusterConfig(**UpperCAmelCase_						)
      config.to_json_file(UpperCAmelCase_						)
      return path
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:List[Any] 							=       parser.add_parser('''default''' ,  parents=UpperCAmelCase_ ,  help=UpperCAmelCase_ ,  formatter_class=UpperCAmelCase_						)
      parser.add_argument(
          '''--config_file''' ,  default=UpperCAmelCase_ ,  help=(
              '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
              '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
              '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
              '''with \'huggingface\'.'''
          ) ,  dest='''save_location''' ,  )

      parser.add_argument(
          '''--mixed_precision''' ,  choices=['''no''', '''fp16''', '''bf16'''] ,  type=UpperCAmelCase_ ,  help='''Whether or not to use mixed precision training. '''
          '''Choose between FP16 and BF16 (bfloat16) training. '''
          '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' ,  default='''no''' ,  )
      parser.set_defaults(func=UpperCAmelCase_						)
      return parser






def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:Optional[Any] 							=       write_basic_config(args.mixed_precision ,  args.save_location						)
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''						)





