





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      :					Tuple      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					str=7      ,				UpperCamelCase      :					Union[str, Any]=3      ,				UpperCamelCase      :					int=10      ,				UpperCamelCase      :					Dict=18      ,				UpperCamelCase      :					Any=30      ,				UpperCamelCase      :					Optional[Any]=4_00      ,				UpperCamelCase      :					Optional[int]=True      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					List[str]=True      ,				UpperCamelCase      :					List[Any]=[0.5, 0.5, 0.5]      ,				UpperCamelCase      :					Optional[int]=[0.5, 0.5, 0.5]      ,				UpperCamelCase      :					str=None      ,				):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	size if size is not None else {'shortest_edge': 18}
    _snake_case   :				Optional[int]														=	crop_size if crop_size is not None else {'height': 18, 'width': 18}

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







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    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    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       List[str]      					=VivitImageProcessor if is_vision_available() else None







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				Any														=	VivitImageProcessingTester(self							)







  @property
  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    return self.image_processor_tester.prepare_image_processor_dict()







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				str														=	self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'image_mean'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'image_std'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_normalize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_resize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_center_crop'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'size'							)							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Optional[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}							)

    _snake_case   :				Union[str, Any]														=	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 UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.image_processing_class(**self.image_processor_dict							)
    # create random PIL videos
    _snake_case   :				Optional[int]														=	prepare_video_inputs(self.image_processor_tester      ,				equal_resolution=UpperCamelCase							)
    for video in video_inputs:
      self.assertIsInstance(UpperCamelCase      ,				UpperCamelCase							)
      self.assertIsInstance(video[0]      ,				Image.Image							)

    # Test not batched input
    _snake_case   :				Optional[int]														=	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
    _snake_case   :				Union[str, Any]														=	image_processing(UpperCamelCase      ,				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 UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				int														=	self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    _snake_case   :				int														=	prepare_video_inputs(self.image_processor_tester      ,				equal_resolution=UpperCamelCase      ,				numpify=UpperCamelCase							)
    for video in video_inputs:
      self.assertIsInstance(UpperCamelCase      ,				UpperCamelCase							)
      self.assertIsInstance(video[0]      ,				np.ndarray							)

    # Test not batched input
    _snake_case   :				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
    _snake_case   :				int														=	image_processing(UpperCamelCase      ,				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 UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    _snake_case   :				int														=	prepare_video_inputs(self.image_processor_tester      ,				equal_resolution=UpperCamelCase      ,				torchify=UpperCamelCase							)
    for video in video_inputs:
      self.assertIsInstance(UpperCamelCase      ,				UpperCamelCase							)
      self.assertIsInstance(video[0]      ,				torch.Tensor							)

    # Test not batched input
    _snake_case   :				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
    _snake_case   :				Dict														=	image_processing(UpperCamelCase      ,				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'],
        )      ,				)











# 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCAmelCase_						   =    """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""


def     lowerCamelCase_	(      )->		Tuple:
  _snake_case   :				int														=	_ask_options(
      'In which compute environment are you running?'    ,							['This machine', 'AWS (Amazon SageMaker)']    ,							_convert_compute_environment    ,							)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    _snake_case   :				int														=	get_sagemaker_input()
  else:
    _snake_case   :				Any														=	get_cluster_input()
  return config


def     lowerCamelCase_	(      lowerCAmelCase:	str=None				)->		Any:
  if subparsers is not None:
    _snake_case   :				List[Any]														=	subparsers.add_parser('config'    ,							description=lowerCAmelCase				)
  else:
    _snake_case   :				Dict														=	argparse.ArgumentParser('Accelerate config command'    ,							description=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\'.'
      )    ,							)

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


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Any:
  _snake_case   :				Dict														=	get_user_input()
  if args.config_file is not None:
    _snake_case   :				List[str]														=	args.config_file
  else:
    if not os.path.isdir(lowerCAmelCase				):
      os.makedirs(lowerCAmelCase				)
    _snake_case   :				Union[str, Any]														=	default_yaml_config_file

  if config_file.endswith('.json'				):
    config.to_json_file(lowerCAmelCase				)
  else:
    config.to_yaml_file(lowerCAmelCase				)
  print(F"""accelerate configuration saved at {config_file}"""				)


def     lowerCamelCase_	(      )->		Dict:
  _snake_case   :				List[str]														=	config_command_parser()
  _snake_case   :				str														=	parser.parse_args()
  config_command(lowerCAmelCase				)


if __name__ == "__main__":
      main()




