






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        LevitForImageClassification,
        LevitForImageClassificationWithTeacher,
        LevitModel,
    )
    from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.config_class(**self.inputs_dict      )
       self.parent.assertTrue(hasattr(UpperCAmelCase_					, "hidden_sizes"      )      )
       self.parent.assertTrue(hasattr(UpperCAmelCase_					, "num_attention_heads"      )      )





class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[int]=13					, UpperCAmelCase_       :	Optional[int]=64					, UpperCAmelCase_       :	Union[str, Any]=3					, UpperCAmelCase_       :	Tuple=3					, UpperCAmelCase_       :	Tuple=2					, UpperCAmelCase_       :	Optional[Any]=1					, UpperCAmelCase_       :	int=16					, UpperCAmelCase_       :	List[str]=[128, 256, 384]					, UpperCAmelCase_       :	List[str]=[4, 6, 8]					, UpperCAmelCase_       :	Tuple=[2, 3, 4]					, UpperCAmelCase_       :	Dict=[16, 16, 16]					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	Union[str, Any]=[2, 2, 2]					, UpperCAmelCase_       :	List[str]=[2, 2, 2]					, UpperCAmelCase_       :	Any=0.02					, UpperCAmelCase_       :	Any=True					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Optional[Any]=2					, ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    parent
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    batch_size
       SCREAMING_SNAKE_CASE       :      int							=    image_size
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    num_channels
       SCREAMING_SNAKE_CASE       :      Tuple							=    kernel_size
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    stride
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    padding
       SCREAMING_SNAKE_CASE       :      int							=    hidden_sizes
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      List[Any]							=    depths
       SCREAMING_SNAKE_CASE       :      str							=    key_dim
       SCREAMING_SNAKE_CASE       :      str							=    drop_path_rate
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    patch_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    attention_ratio
       SCREAMING_SNAKE_CASE       :      Dict							=    mlp_ratio
       SCREAMING_SNAKE_CASE       :      str							=    initializer_range
       SCREAMING_SNAKE_CASE       :      str							=    [
           ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       SCREAMING_SNAKE_CASE       :      str							=    is_training
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    use_labels
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_labels
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    initializer_range


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    None
       if self.use_labels:
         SCREAMING_SNAKE_CASE       :      str							=    ids_tensor([self.batch_size]					, self.num_labels      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    self.get_config()
       return config, pixel_values, labels


     def 				_A       (				self       :	Optional[int]      ):
       return LevitConfig(
           image_size=self.image_size					, num_channels=self.num_channels					, kernel_size=self.kernel_size					, stride=self.stride					, padding=self.padding					, patch_size=self.patch_size					, hidden_sizes=self.hidden_sizes					, num_attention_heads=self.num_attention_heads					, depths=self.depths					, key_dim=self.key_dim					, drop_path_rate=self.drop_path_rate					, mlp_ratio=self.mlp_ratio					, attention_ratio=self.attention_ratio					, initializer_range=self.initializer_range					, down_ops=self.down_ops					, )


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    LevitModel(config=UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    model(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    (self.image_size, self.image_size)
       SCREAMING_SNAKE_CASE       :      Tuple							=    image_size[0], image_size[1]
       for _ in range(4      ):
         SCREAMING_SNAKE_CASE       :      int							=    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1      )
         SCREAMING_SNAKE_CASE       :      List[str]							=    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1      )
       self.parent.assertEqual(
           result.last_hidden_state.shape					, (self.batch_size, ceil(height / 4      ) * ceil(width / 4      ), self.hidden_sizes[-1])					, )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.num_labels
       SCREAMING_SNAKE_CASE       :      Tuple							=    LevitForImageClassification(UpperCAmelCase_      )
       model.to(UpperCAmelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Tuple							=    model(UpperCAmelCase_					, labels=UpperCAmelCase_      )
       self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)      )






     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    config_and_inputs
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	(
         (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
         if is_torch_available()
         else ()
     )
     UpperCamelCase_							:		int			=	(
         {
             '''feature-extraction''': LevitModel,
             '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     UpperCamelCase_							:		List[Any]			=	False
     UpperCamelCase_							:		Tuple			=	False
     UpperCamelCase_							:		Dict			=	False
     UpperCamelCase_							:		Optional[int]			=	False
     UpperCamelCase_							:		Tuple			=	False


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    LevitModelTester(self      )
       SCREAMING_SNAKE_CASE       :      int							=    ConfigTester(self					, config_class=UpperCAmelCase_					, has_text_modality=UpperCAmelCase_					, hidden_size=37      )


     def 				_A       (				self       :	Optional[Any]      ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()


     def 				_A       (				self       :	Tuple      ):
       return


     @unittest.skip(reason="Levit does not use inputs_embeds"      )
     def 				_A       (				self       :	Tuple      ):
       pass


     @unittest.skip(reason="Levit does not support input and output embeddings"      )
     def 				_A       (				self       :	int      ):
       pass


     @unittest.skip(reason="Levit does not output attentions"      )
     def 				_A       (				self       :	int      ):
       pass


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      Dict							=    model_class(UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Tuple							=    inspect.signature(model.forward      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         SCREAMING_SNAKE_CASE       :      Any							=    [*signature.parameters.keys()]

         SCREAMING_SNAKE_CASE       :      str							=    ["pixel_values"]
         self.assertListEqual(arg_names[:1]					, UpperCAmelCase_      )


     def 				_A       (				self       :	int      ):
       def check_hidden_states_output(UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple      ):
         SCREAMING_SNAKE_CASE       :      Dict							=    model_class(UpperCAmelCase_      )
         model.to(UpperCAmelCase_      )
         model.eval()

         with torch.no_grad():
           SCREAMING_SNAKE_CASE       :      Dict							=    model(**self._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_      )      )

         SCREAMING_SNAKE_CASE       :      Optional[int]							=    outputs.hidden_states

         SCREAMING_SNAKE_CASE       :      List[str]							=    len(self.model_tester.depths      ) + 1
         self.assertEqual(len(UpperCAmelCase_      )					, UpperCAmelCase_      )

         SCREAMING_SNAKE_CASE       :      List[str]							=    (self.model_tester.image_size, self.model_tester.image_size)
         SCREAMING_SNAKE_CASE       :      List[str]							=    image_size[0], image_size[1]
         for _ in range(4      ):
           SCREAMING_SNAKE_CASE       :      Dict							=    floor(
               (
                   (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1      )
           SCREAMING_SNAKE_CASE       :      Any							=    floor(
               (
                   (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1      )
         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]      )					, [
                 height * width,
                 self.model_tester.hidden_sizes[0],
             ]					, )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      str							=    True
         check_hidden_states_output(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    True

         check_hidden_states_output(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )


     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
     def 				_A       (				self       :	Optional[int]      ):
       pass


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	List[str]=False      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    super()._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_					, return_labels=UpperCAmelCase_      )

       if return_labels:
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           del inputs_dict["labels"]

       return inputs_dict


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )


     def 				_A       (				self       :	int      ):
       if not self.model_tester.is_training:
         return

       SCREAMING_SNAKE_CASE       :      List[str]							=    self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE       :      str							=    True

       for model_class in self.all_model_classes:
         # LevitForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(UpperCAmelCase_      )
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
           continue
         SCREAMING_SNAKE_CASE       :      Dict							=    model_class(UpperCAmelCase_      )
         model.to(UpperCAmelCase_      )
         model.train()
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_					, return_labels=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(**UpperCAmelCase_      ).loss
         loss.backward()


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
         return

       SCREAMING_SNAKE_CASE       :      Tuple							=    False
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    True

       for model_class in self.all_model_classes:
         if model_class in get_values(UpperCAmelCase_      ) or not model_class.supports_gradient_checkpointing:
           continue
         # LevitForImageClassificationWithTeacher supports inference-only
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           continue
         SCREAMING_SNAKE_CASE       :      int							=    model_class(UpperCAmelCase_      )
         model.gradient_checkpointing_enable()
         model.to(UpperCAmelCase_      )
         model.train()
         SCREAMING_SNAKE_CASE       :      Tuple							=    self._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_					, return_labels=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model(**UpperCAmelCase_      ).loss
         loss.backward()


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE       :      List[str]							=    [
           {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
           {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
           {"title": "regression", "num_labels": 1, "dtype": torch.float},
       ]

       for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(UpperCAmelCase_      ),
             ]
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
           continue

         for problem_type in problem_types:
           with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''      ):
             SCREAMING_SNAKE_CASE       :      Tuple							=    problem_type["title"]
             SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    problem_type["num_labels"]

             SCREAMING_SNAKE_CASE       :      List[Any]							=    model_class(UpperCAmelCase_      )
             model.to(UpperCAmelCase_      )
             model.train()

             SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self._prepare_for_class(UpperCAmelCase_					, UpperCAmelCase_					, return_labels=UpperCAmelCase_      )

             if problem_type["num_labels"] > 1:
               SCREAMING_SNAKE_CASE       :      List[Any]							=    inputs["labels"].unsqueeze(1      ).repeat(1					, problem_type["num_labels"]      )

             SCREAMING_SNAKE_CASE       :      Optional[int]							=    inputs["labels"].to(problem_type["dtype"]      )

             # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
             # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
             # they have the same size." which is a symptom something in wrong for the regression problem.
             # See https://github.com/huggingface/transformers/issues/11780
             with warnings.catch_warnings(record=UpperCAmelCase_      ) as warning_list:
               SCREAMING_SNAKE_CASE       :      int							=    model(**UpperCAmelCase_      ).loss
             for w in warning_list:
               if "Using a target size that is different to the input size" in str(w.message      ):
                 raise ValueError(
                     f'''Something is going wrong in the regression problem: intercepted {w.message}'''      )

             loss.backward()






     @slow
     def 				_A       (				self       :	Optional[int]      ):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE       :      int							=    LevitModel.from_pretrained(UpperCAmelCase_      )
         self.assertIsNotNone(UpperCAmelCase_      )





def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
  return image


@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @cached_property
     def 				_A       (				self       :	Union[str, Any]      ):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )






     @slow
     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      int							=    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(
           UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.default_image_processor
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_img()
       SCREAMING_SNAKE_CASE       :      Dict							=    image_processor(images=UpperCAmelCase_					, return_tensors="pt"      ).to(UpperCAmelCase_      )

       # forward pass
       with torch.no_grad():
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    model(**UpperCAmelCase_      )

       # verify the logits
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.Size((1, 1000)      )
       self.assertEqual(outputs.logits.shape					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.tensor([1.0_448, -0.3_745, -1.8_317]      ).to(UpperCAmelCase_      )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]					, UpperCAmelCase_					, atol=1E-4      )      )














# 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    				=		"""Create a default config file for Accelerate with only a few flags set."""


def   lowerCamelCase__			(     lowercase="no"						,     lowercase = default_json_config_file						,     lowercase = False				):

  """simple docstring"""






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


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    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   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






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






