





import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command




class 			__UpperCamelCase    (							unittest.TestCase    ):
     __A	:  List[Any]							  =   inspect.getfile(accelerate.test_utils    )
     __A	:  Optional[Any]							  =   os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_cli.py"""]    )

     __A	:  Optional[int]							  =   ["""accelerate""", """launch"""]
     __A	:  Any							  =   Path.home() / """.cache/huggingface/accelerate"""
     __A	:  Any							  =   """default_config.yaml"""
     __A	:  Union[str, Any]							  =   config_folder / config_file
     __A	:  List[Any]							  =   config_folder / """_default_config.yaml"""

     __A	:  Union[str, Any]							  =   Path("""tests/test_configs"""    )





     @classmethod
     def 		UpperCamelCase(     cls					):
        if cls.config_path.is_file():
           cls.config_path.rename(cls.changed_path					)





     @classmethod
     def 		UpperCamelCase(     cls					):
        if cls.changed_path.is_file():
           cls.changed_path.rename(cls.config_path					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.base_cmd
        if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
           cmd += ["--multi_gpu"]
        execute_subprocess_async(cmd + [self.test_file_path]				,							env=os.environ.copy()					)





     def 		UpperCamelCase(     self					):
        for config in sorted(self.test_config_path.glob('''**/*.yaml'''					)					):
           with self.subTest(config_file=_UpperCamelCase					):
              execute_subprocess_async(
                  self.base_cmd + ['''--config_file''', str(_UpperCamelCase					), self.test_file_path]				,							env=os.environ.copy()					)





     def 		UpperCamelCase(     self					):
        execute_subprocess_async(['''accelerate''', '''test''']				,							env=os.environ.copy()					)




class 			__UpperCamelCase    (							unittest.TestCase    ):
     __A	:  Dict							  =   """test-tpu"""
     __A	:  Optional[Any]							  =   """us-central1-a"""
     __A	:  int							  =   """ls"""
     __A	:  Tuple							  =   ["""accelerate""", """tpu-config"""]
     __A	:  Union[str, Any]							  =   """cd /usr/share"""
     __A	:  Optional[Any]							  =   """tests/test_samples/test_command_file.sh"""
     __A	:  Any							  =   """Running gcloud compute tpus tpu-vm ssh"""





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd
            + ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug''']				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd
            + [
                '''--config_file''',
                '''tests/test_configs/0_12_0.yaml''',
                '''--command''',
                self.command,
                '''--tpu_zone''',
                self.tpu_zone,
                '''--tpu_name''',
                self.tpu_name,
                '''--debug''',
            ]				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug''']				,							return_stdout=_UpperCamelCase					)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug''']				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd
            + [
                '''--config_file''',
                '''tests/test_configs/latest.yaml''',
                '''--command''',
                self.command,
                '''--command''',
                '''echo "Hello World"''',
                '''--debug''',
            ]				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd
            + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug''']				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd
            + [
                '''--config_file''',
                '''tests/test_configs/0_12_0.yaml''',
                '''--command_file''',
                self.command_file,
                '''--tpu_zone''',
                self.tpu_zone,
                '''--tpu_name''',
                self.tpu_name,
                '''--debug''',
            ]				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug''']				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all'''				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   run_command(
            self.cmd
            + [
                '''--config_file''',
                '''tests/test_configs/latest.yaml''',
                '''--install_accelerate''',
                '''--accelerate_version''',
                '''12.0.0''',
                '''--debug''',
            ]				,							return_stdout=_UpperCamelCase				,							)
        self.assertIn(
            f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all'''				,							_UpperCamelCase				,							)






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if "shortest_edge" in size:
           _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           _UpperCAmelCase           =   (size['''height'''], size['''width'''])
        else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
        return center_crop(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        if not valid_images(_UpperCamelCase					):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

        if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''					)

        if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

        if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

        # All transformations expect numpy arrays.
        _UpperCAmelCase           =   [to_numpy_array(_UpperCamelCase					) for image in images]

        if do_resize:
           _UpperCAmelCase           =   [self.resize(image=_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase					) for image in images]

        if do_center_crop:
           _UpperCAmelCase           =   [self.center_crop(image=_UpperCamelCase				,							size=_UpperCamelCase					) for image in images]

        if do_rescale:
           _UpperCAmelCase           =   [self.rescale(image=_UpperCamelCase				,							scale=_UpperCamelCase					) for image in images]

        if do_normalize:
           _UpperCAmelCase           =   [self.normalize(image=_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)