


'''simple docstring'''



import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      if is_vision_available():
            from transformers import OneFormerImageProcessor
            from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
            from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
      from PIL import Image




def      _a     (						_lowercase :     Optional[Any]      , _lowercase :     Any="shi-labs/oneformer_demo"  ):





     '''simple docstring'''






     with open(hf_hub_download(_lowercase      , _lowercase      , repo_type='''dataset'''  )      , '''r'''  ) as f:
          __UpperCAmelCase     :			Tuple  							=      json.load(_lowercase  )
     __UpperCAmelCase     :			Dict  							=      {}
     __UpperCAmelCase     :			Any  							=      []
     __UpperCAmelCase     :			str  							=      []
     for key, info in class_info.items():
          __UpperCAmelCase     :			List[str]  							=      info['''name''']
          class_names.append(info['''name''']  )
          if info["isthing"]:
               thing_ids.append(int(_lowercase  )  )
     __UpperCAmelCase     :			Dict  							=      thing_ids
     __UpperCAmelCase     :			Optional[int]  							=      class_names
     return metadata



class      a    (		unittest.TestCase	):


     """simple docstring"""






     def __init__(    self					:		str							,			snake_case					:		Union[str, Any]							,			snake_case					:		Any=7							,			snake_case					:		Any=3							,			snake_case					:		Any=30							,			snake_case					:		str=400							,			snake_case					:		Any=None							,			snake_case					:		Tuple=True							,			snake_case					:		Optional[int]=True							,			snake_case					:		Tuple=[0.5, 0.5, 0.5]							,			snake_case					:		Tuple=[0.5, 0.5, 0.5]							,			snake_case					:		Tuple=10							,			snake_case					:		Union[str, Any]=False							,			snake_case					:		int=255							,			snake_case					:		Dict="shi-labs/oneformer_demo"							,			snake_case					:		Optional[int]="ade20k_panoptic.json"							,			snake_case					:		Tuple=10							,			)						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      parent
          __UpperCAmelCase     :			Optional[Any]  							=      batch_size
          __UpperCAmelCase     :			List[Any]  							=      num_channels
          __UpperCAmelCase     :			Optional[Any]  							=      min_resolution
          __UpperCAmelCase     :			Optional[int]  							=      max_resolution
          __UpperCAmelCase     :			Dict  							=      do_resize
          __UpperCAmelCase     :			Optional[int]  							=      {'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
          __UpperCAmelCase     :			List[Any]  							=      do_normalize
          __UpperCAmelCase     :			str  							=      image_mean
          __UpperCAmelCase     :			Union[str, Any]  							=      image_std
          __UpperCAmelCase     :			str  							=      class_info_file
          __UpperCAmelCase     :			Optional[Any]  							=      prepare_metadata(snake_case							,			snake_case      )
          __UpperCAmelCase     :			List[str]  							=      num_text
          __UpperCAmelCase     :			List[str]  							=      repo_path

          # for the post_process_functions
          __UpperCAmelCase     :			int  							=      2
          __UpperCAmelCase     :			Dict  							=      10
          __UpperCAmelCase     :			Optional[Any]  							=      10
          __UpperCAmelCase     :			Any  							=      3
          __UpperCAmelCase     :			Union[str, Any]  							=      4
          __UpperCAmelCase     :			Union[str, Any]  							=      num_labels
          __UpperCAmelCase     :			str  							=      do_reduce_labels
          __UpperCAmelCase     :			Any  							=      ignore_index

     def 			lowerCamelCase__			(    self					:		str      )						->							List[str]:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "num_labels": self.num_labels,
              "do_reduce_labels": self.do_reduce_labels,
              "ignore_index": self.ignore_index,
              "class_info_file": self.class_info_file,
              "metadata": self.metadata,
              "num_text": self.num_text,
          }

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		List[str]							,			snake_case					:		Tuple=False      )						->							Tuple:
          if not batched:
               __UpperCAmelCase     :			Union[str, Any]  							=      image_inputs[0]
               if isinstance(snake_case							,			Image.Image      ):
                    __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      image.size
               else:
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Optional[int]  							=      image.shape[1], image.shape[2]
               if w < h:
                    __UpperCAmelCase     :			Union[str, Any]  							=      int(self.size['''shortest_edge'''] * h / w      )
                    __UpperCAmelCase     :			Dict  							=      self.size['''shortest_edge''']
               elif w > h:
                    __UpperCAmelCase     :			Union[str, Any]  							=      self.size['''shortest_edge''']
                    __UpperCAmelCase     :			Union[str, Any]  							=      int(self.size['''shortest_edge'''] * w / h      )
               else:
                    __UpperCAmelCase     :			List[Any]  							=      self.size['''shortest_edge''']
                    __UpperCAmelCase     :			Optional[int]  							=      self.size['''shortest_edge''']

          else:
               __UpperCAmelCase     :			int  							=      []
               for image in image_inputs:
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Union[str, Any]  							=      self.get_expected_values([image]      )
                    expected_values.append((expected_height, expected_width)      )
               __UpperCAmelCase     :			Optional[int]  							=      max(snake_case							,			key=lambda snake_case      : item[0]      )[0]
               __UpperCAmelCase     :			Any  							=      max(snake_case							,			key=lambda snake_case      : item[1]      )[1]

          return expected_height, expected_width






     def 			lowerCamelCase__			(    self					:		Dict      )						->							int:
          return OneFormerForUniversalSegmentationOutput(
              # +1 for null class
              class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)      )							,			masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)      )							,			)



@require_torch
@require_vision
class      a    (		_a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Any							       =       OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       image_processing_class

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							str:
          __UpperCAmelCase     :			Dict  							=      OneFormerImageProcessorTester(self      )

     @property
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[int]:
          return self.image_processing_tester.prepare_image_processor_dict()

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(snake_case							,			'''image_mean'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''image_std'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_normalize'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_resize'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''size'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''ignore_index'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''class_info_file'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''num_text'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''repo_path'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''metadata'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_reduce_labels'''      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[int]:
          pass

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Tuple:
          # Initialize image_processor
          __UpperCAmelCase     :			Optional[Any]  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          __UpperCAmelCase     :			Optional[int]  							=      prepare_image_inputs(self.image_processing_tester							,			equal_resolution=snake_case      )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,			Image.Image      )

          # Test not batched input
          __UpperCAmelCase     :			List[Any]  							=      image_processor(image_inputs[0]							,			['''semantic''']							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Union[str, Any]  							=      self.image_processing_tester.get_expected_values(snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,			)

          # Test batched
          __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      self.image_processing_tester.get_expected_values(snake_case							,			batched=snake_case      )

          __UpperCAmelCase     :			List[Any]  							=      image_processor(
              snake_case							,			['''semantic'''] * len(snake_case      )							,			return_tensors='''pt'''      ).pixel_values
          self.assertEqual(
              encoded_images.shape							,			(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )							,			)

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          # Initialize image_processor
          __UpperCAmelCase     :			str  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          __UpperCAmelCase     :			Dict  							=      prepare_image_inputs(self.image_processing_tester							,			equal_resolution=snake_case							,			numpify=snake_case      )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,			np.ndarray      )

          # Test not batched input
          __UpperCAmelCase     :			Optional[Any]  							=      image_processor(image_inputs[0]							,			['''semantic''']							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      self.image_processing_tester.get_expected_values(snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,			)

          # Test batched
          __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.image_processing_tester.get_expected_values(snake_case							,			batched=snake_case      )

          __UpperCAmelCase     :			Tuple  							=      image_processor(
              snake_case							,			['''semantic'''] * len(snake_case      )							,			return_tensors='''pt'''      ).pixel_values
          self.assertEqual(
              encoded_images.shape							,			(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )							,			)

     def 			lowerCamelCase__			(    self					:		Any      )						->							str:
          # Initialize image_processor
          __UpperCAmelCase     :			str  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          __UpperCAmelCase     :			Any  							=      prepare_image_inputs(self.image_processing_tester							,			equal_resolution=snake_case							,			torchify=snake_case      )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,			torch.Tensor      )

          # Test not batched input
          __UpperCAmelCase     :			List[str]  							=      image_processor(image_inputs[0]							,			['''semantic''']							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      self.image_processing_tester.get_expected_values(snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,			)

          # Test batched
          __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      self.image_processing_tester.get_expected_values(snake_case							,			batched=snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      image_processor(
              snake_case							,			['''semantic'''] * len(snake_case      )							,			return_tensors='''pt'''      ).pixel_values
          self.assertEqual(
              encoded_images.shape							,			(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )							,			)

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		str=False							,			snake_case					:		Tuple=False							,			snake_case					:		List[str]="np"      )						->							Tuple:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.image_processing_class(**self.image_processor_dict      )
          # prepare image and target
          __UpperCAmelCase     :			List[Any]  							=      self.image_processing_tester.num_labels
          __UpperCAmelCase     :			Optional[Any]  							=      None
          __UpperCAmelCase     :			str  							=      None
          __UpperCAmelCase     :			Any  							=      prepare_image_inputs(self.image_processing_tester							,			equal_resolution=snake_case      )
          if with_segmentation_maps:
               __UpperCAmelCase     :			Any  							=      num_labels
               if is_instance_map:
                    __UpperCAmelCase     :			int  							=      list(range(snake_case      )      ) * 2
                    __UpperCAmelCase     :			Tuple  							=      dict(enumerate(snake_case      )      )
               __UpperCAmelCase     :			Union[str, Any]  							=      [
                   np.random.randint(0							,			high * 2							,			(img.size[1], img.size[0])      ).astype(np.uinta      ) for img in image_inputs
               ]
               if segmentation_type == "pil":
                    __UpperCAmelCase     :			Dict  							=      [Image.fromarray(snake_case      ) for annotation in annotations]

          __UpperCAmelCase     :			int  							=      image_processor(
              snake_case							,			['''semantic'''] * len(snake_case      )							,			snake_case							,			return_tensors='''pt'''							,			instance_id_to_semantic_id=snake_case							,			pad_and_return_pixel_mask=snake_case							,			)

          return inputs

     def 			lowerCamelCase__			(    self					:		str      )						->							List[str]:
          pass

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          def common(snake_case					:		str=False							,			snake_case					:		List[Any]=None      ):
               __UpperCAmelCase     :			Optional[int]  							=      self.comm_get_image_processor_inputs(
                   with_segmentation_maps=snake_case							,			is_instance_map=snake_case							,			segmentation_type=snake_case      )

               __UpperCAmelCase     :			Union[str, Any]  							=      inputs['''mask_labels''']
               __UpperCAmelCase     :			Union[str, Any]  							=      inputs['''class_labels''']
               __UpperCAmelCase     :			Optional[int]  							=      inputs['''pixel_values''']
               __UpperCAmelCase     :			Optional[int]  							=      inputs['''text_inputs''']

               # check the batch_size
               for mask_label, class_label, text_input in zip(snake_case							,			snake_case							,			snake_case      ):
                    self.assertEqual(mask_label.shape[0]							,			class_label.shape[0]      )
                    # this ensure padding has happened
                    self.assertEqual(mask_label.shape[1:]							,			pixel_values.shape[2:]      )
                    self.assertEqual(len(snake_case      )							,			self.image_processing_tester.num_text      )

          common()
          common(is_instance_map=snake_case      )
          common(is_instance_map=snake_case							,			segmentation_type='''pil'''      )
          common(is_instance_map=snake_case							,			segmentation_type='''pil'''      )

     def 			lowerCamelCase__			(    self					:		str      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      np.zeros((20, 50)      )
          __UpperCAmelCase     :			Optional[Any]  							=      1
          __UpperCAmelCase     :			Optional[Any]  							=      1
          __UpperCAmelCase     :			List[str]  							=      1

          __UpperCAmelCase     :			List[str]  							=      binary_mask_to_rle(snake_case      )
          self.assertEqual(len(snake_case      )							,			4      )
          self.assertEqual(rle[0]							,			21      )
          self.assertEqual(rle[1]							,			45      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Dict:
          __UpperCAmelCase     :			Tuple  							=      self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes							,			max_seq_length=77							,			task_seq_length=77							,			class_info_file='''ade20k_panoptic.json'''							,			num_text=self.image_processing_tester.num_text							,			repo_path='''shi-labs/oneformer_demo'''							,			)
          __UpperCAmelCase     :			Any  							=      self.image_processing_tester.get_fake_oneformer_outputs()

          __UpperCAmelCase     :			List[str]  							=      fature_extractor.post_process_semantic_segmentation(snake_case      )

          self.assertEqual(len(snake_case      )							,			self.image_processing_tester.batch_size      )
          self.assertEqual(
              segmentation[0].shape							,			(
                  self.image_processing_tester.height,
                  self.image_processing_tester.width,
              )							,			)

          __UpperCAmelCase     :			List[str]  							=      [(1, 4) for i in range(self.image_processing_tester.batch_size      )]
          __UpperCAmelCase     :			Optional[int]  							=      fature_extractor.post_process_semantic_segmentation(snake_case							,			target_sizes=snake_case      )

          self.assertEqual(segmentation[0].shape							,			target_sizes[0]      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[str]:
          __UpperCAmelCase     :			Optional[Any]  							=      self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes							,			max_seq_length=77							,			task_seq_length=77							,			class_info_file='''ade20k_panoptic.json'''							,			num_text=self.image_processing_tester.num_text							,			repo_path='''shi-labs/oneformer_demo'''							,			)
          __UpperCAmelCase     :			Optional[int]  							=      self.image_processing_tester.get_fake_oneformer_outputs()
          __UpperCAmelCase     :			int  							=      image_processor.post_process_instance_segmentation(snake_case							,			threshold=0      )

          self.assertTrue(len(snake_case      ) == self.image_processing_tester.batch_size      )
          for el in segmentation:
               self.assertTrue('''segmentation''' in el      )
               self.assertTrue('''segments_info''' in el      )
               self.assertEqual(type(el['''segments_info''']      )							,			snake_case      )
               self.assertEqual(
                   el['''segmentation'''].shape							,			(self.image_processing_tester.height, self.image_processing_tester.width)      )






     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[int]:
          __UpperCAmelCase     :			Dict  							=      self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes							,			max_seq_length=77							,			task_seq_length=77							,			class_info_file='''ade20k_panoptic.json'''							,			num_text=self.image_processing_tester.num_text							,			repo_path='''shi-labs/oneformer_demo'''							,			)
          __UpperCAmelCase     :			Tuple  							=      self.image_processing_tester.get_fake_oneformer_outputs()
          __UpperCAmelCase     :			Dict  							=      image_processor.post_process_panoptic_segmentation(snake_case							,			threshold=0      )

          self.assertTrue(len(snake_case      ) == self.image_processing_tester.batch_size      )
          for el in segmentation:
               self.assertTrue('''segmentation''' in el      )
               self.assertTrue('''segments_info''' in el      )
               self.assertEqual(type(el['''segments_info''']      )							,			snake_case      )
               self.assertEqual(
                   el['''segmentation'''].shape							,			(self.image_processing_tester.height, self.image_processing_tester.width)      )



'''simple docstring'''



# 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


__UpperCAmelCase      :Tuple			    =       "Create a default config file for Accelerate with only a few flags set."




def      _a     (						_lowercase :     List[Any]="no"      , _lowercase :     str = default_json_config_file      , _lowercase :     bool = False  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Dict  							=      Path(_lowercase  )
     path.parent.mkdir(parents=_lowercase      , exist_ok=_lowercase  )
     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
     __UpperCAmelCase     :			List[str]  							=      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}'  )
     __UpperCAmelCase     :			int  							=      {
         '''compute_environment''': '''LOCAL_MACHINE''',
         '''mixed_precision''': mixed_precision,
     }
     if torch.cuda.is_available():
          __UpperCAmelCase     :			Optional[Any]  							=      torch.cuda.device_count()
          __UpperCAmelCase     :			List[str]  							=      num_gpus
          __UpperCAmelCase     :			int  							=      False
          if num_gpus > 1:
               __UpperCAmelCase     :			Any  							=      '''MULTI_GPU'''
          else:
               __UpperCAmelCase     :			int  							=      '''NO'''
     elif is_xpu_available() and use_xpu:
          __UpperCAmelCase     :			List[Any]  							=      torch.xpu.device_count()
          __UpperCAmelCase     :			List[Any]  							=      num_xpus
          __UpperCAmelCase     :			Optional[int]  							=      False
          if num_xpus > 1:
               __UpperCAmelCase     :			Any  							=      '''MULTI_XPU'''
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      '''NO'''
     elif is_npu_available():
          __UpperCAmelCase     :			Dict  							=      torch.npu.device_count()
          __UpperCAmelCase     :			Any  							=      num_npus
          __UpperCAmelCase     :			Any  							=      False
          if num_npus > 1:
               __UpperCAmelCase     :			Dict  							=      '''MULTI_NPU'''
          else:
               __UpperCAmelCase     :			Optional[int]  							=      '''NO'''
     else:
          __UpperCAmelCase     :			List[str]  							=      0
          __UpperCAmelCase     :			Dict  							=      True
          __UpperCAmelCase     :			Dict  							=      1
          __UpperCAmelCase     :			Tuple  							=      '''NO'''
     __UpperCAmelCase     :			List[Any]  							=      ClusterConfig(**_lowercase  )
     config.to_json_file(_lowercase  )
     return path




def      _a     (						_lowercase :     Union[str, Any]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      parser.add_parser('''default'''      , parents=_lowercase      , help=_lowercase      , formatter_class=_lowercase  )
     parser.add_argument(
         '''--config_file'''      , default=_lowercase      , 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=_lowercase      , 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=_lowercase  )
     return parser




def      _a     (						_lowercase :     List[Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      write_basic_config(args.mixed_precision      , args.save_location  )
     if config_file:
          print(F'accelerate configuration saved at {config_file}'  )