'''simple docstring'''




# Copyright 2022 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.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  """Run commands across TPU VMs for initial setup before running `accelerate launch`."""

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]=None    )    ->    Tuple:


       """simple docstring"""







       if subparsers is not None:
              _UpperCAmelCase     :  List[Any]							      =    subparsers.add_parser("tpu-config"			,	description=_description    )
       else:
              _UpperCAmelCase     :  Any							      =    argparse.ArgumentParser("Accelerate tpu-config command"			,	description=_description    )
       # Core arguments
       _UpperCAmelCase     :  Optional[Any]							      =    parser.add_argument_group(
           "Config Arguments"			,	"Arguments that can be configured through `accelerate config`."    )
       config_args.add_argument(
           "--config_file"			,	type=_UpperCAmelCase			,	default=_UpperCAmelCase			,	help="Path to the config file to use for accelerate."			,	)
       config_args.add_argument(
           "--tpu_name"			,	default=_UpperCAmelCase			,	help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."			,	)
       config_args.add_argument(
           "--tpu_zone"			,	default=_UpperCAmelCase			,	help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."			,	)
       _UpperCAmelCase     :  Tuple							      =    parser.add_argument_group("TPU Arguments"			,	"Arguments for options ran inside the TPU."    )
       pod_args.add_argument(
           "--use_alpha"			,	action="store_true"			,	help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."			,	)
       pod_args.add_argument(
           "--command_file"			,	default=_UpperCAmelCase			,	help="The path to the file containing the commands to run on the pod on startup."			,	)
       pod_args.add_argument(
           "--command"			,	action="append"			,	nargs="+"			,	help="A command to run on the pod. Can be passed multiple times."			,	)
       pod_args.add_argument(
           "--install_accelerate"			,	action="store_true"			,	help="Whether to install accelerate on the pod. Defaults to False."			,	)
       pod_args.add_argument(
           "--accelerate_version"			,	default="latest"			,	help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."			,	)
       pod_args.add_argument(
           "--debug"			,	action="store_true"			,	help="If set, will print the command that would be run instead of running it."    )

       if subparsers is not None:
              parser.set_defaults(func=_UpperCAmelCase    )
       return parser

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Union[str, Any]							      =    None

       # Get the default from the config file if it exists.
       if args.config_file is not None or os.path.isfile(_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[Any]							      =    load_config_from_file(args.config_file    )
              if not args.command_file and defaults.command_file is not None and not args.command:
                     _UpperCAmelCase     :  List[Any]							      =    defaults.command_file
              if not args.command and defaults.commands is not None:
                     _UpperCAmelCase     :  Tuple							      =    defaults.commands
              if not args.tpu_name:
                     _UpperCAmelCase     :  Union[str, Any]							      =    defaults.tpu_name
              if not args.tpu_zone:
                     _UpperCAmelCase     :  List[str]							      =    defaults.tpu_zone
       if args.accelerate_version == "dev":
              _UpperCAmelCase     :  int							      =    "git+https://github.com/huggingface/accelerate.git"
       elif args.accelerate_version == "latest":
              _UpperCAmelCase     :  List[Any]							      =    "accelerate -U"
       elif isinstance(parse(args.accelerate_version    )			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  int							      =    F"""accelerate=={args.accelerate_version}"""

       if not args.command_file and not args.command:
              raise ValueError("You must specify either a command file or a command to run on the pod."    )

       if args.command_file:
              with open(args.command_file			,	"r"    ) as f:
                     _UpperCAmelCase     :  Tuple							      =    [f.read().splitlines()]

    # To turn list of lists into list of strings
       if isinstance(args.command[0]			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Tuple							      =    [line for cmd in args.command for line in cmd]
       # Default to the shared folder and install accelerate
       _UpperCAmelCase     :  Any							      =    ["cd /usr/share"]
       if args.install_accelerate:
              new_cmd += [F"""pip install {args.accelerate_version}"""]
       new_cmd += args.command
       _UpperCAmelCase     :  int							      =    "; ".join(_UpperCAmelCase    )

       # Then send it to gcloud
       # Eventually try to use google-api-core to do this instead of subprocess
       _UpperCAmelCase     :  Tuple							      =    ["gcloud"]
       if args.use_alpha:
              cmd += ["alpha"]
       cmd += [
           "compute",
           "tpus",
           "tpu-vm",
           "ssh",
           args.tpu_name,
           "--zone",
           args.tpu_zone,
           "--command",
           args.command,
           "--worker",
           "all",
       ]
       if args.debug:
              print(F"""Running {' '.join(_UpperCAmelCase    )}"""    )
              return
       subprocess.run(_UpperCAmelCase    )
       print("Successfully setup pod."    )


def 	UpperCamelCase_							(					)    ->    Any:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    tpu_command_parser()
       _UpperCAmelCase     :  Tuple							      =    parser.parse_args()

       tpu_command_launcher(_UpperCAmelCase    )


'''simple docstring'''




import unittest

import numpy as np

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_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VivitImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				int						,					A						:				Dict						,					A						:				Optional[int]=7						,					A						:				Tuple=3						,					A						:				Optional[Any]=10						,					A						:				int=18						,					A						:				Dict=30						,					A						:				List[str]=400						,					A						:				int=True						,					A						:				Optional[Any]=None						,					A						:				Optional[Any]=True						,					A						:				List[Any]=[0.5, 0.5, 0.5]						,					A						:				List[str]=[0.5, 0.5, 0.5]						,					A						:				Optional[int]=None						,					):
             _UpperCAmelCase     :  Dict							      =    size if size is not None else {"shortest_edge": 18}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}

             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  Optional[int]							      =    num_channels
             _UpperCAmelCase     :  Optional[Any]							      =    num_frames
             _UpperCAmelCase     :  Any							      =    image_size
             _UpperCAmelCase     :  Dict							      =    min_resolution
             _UpperCAmelCase     :  Any							      =    max_resolution
             _UpperCAmelCase     :  Optional[int]							      =    do_resize
             _UpperCAmelCase     :  str							      =    size
             _UpperCAmelCase     :  List[Any]							      =    do_normalize
             _UpperCAmelCase     :  Any							      =    image_mean
             _UpperCAmelCase     :  Tuple							      =    image_std
             _UpperCAmelCase     :  Any							      =    crop_size





      def      _A	(       self						:				List[Any]       ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_size": self.crop_size,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						VivitImageProcessor if is_vision_available() else None




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    VivitImageProcessingTester(self       )




      @property
      def      _A	(       self						:				Optional[Any]       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"image_mean"       )       )
             self.assertTrue(hasattr(A						,					"image_std"       )       )
             self.assertTrue(hasattr(A						,					"do_normalize"       )       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 18}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Optional[int]							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Tuple       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL videos
             _UpperCAmelCase     :  Any							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  str							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  List[Any]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  Tuple							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[int]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

