'''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


snake_case__					:      str				      =  '''Create a default config file for Accelerate with only a few flags set.'''


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]="no"     ,					lowerCamelCase_ :							str = default_json_config_file     ,					lowerCamelCase_ :							bool = False       ):



     """simple docstring"""





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


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				parser.add_parser('default'     ,					parents=lowerCamelCase_     ,					help=lowerCamelCase_     ,					formatter_class=lowerCamelCase_       )
     parser.add_argument(
         '--config_file'     ,					default=lowerCamelCase_     ,					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=lowerCamelCase_     ,					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=lowerCamelCase_       )
     return parser





def  _lowerCamelCase   (				lowerCamelCase_ :							Dict       ):



     """simple docstring"""





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







'''simple docstring'''


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				depth_estimator(
           [
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						),
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               # RGBA
               dataset[0]['file'],
               # LA
               dataset[1]['file'],
               # L
               dataset[2]['file'],
           ]						)
       self.assertEqual(
           [
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






