




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


__lowerCamelCase   :						Tuple				=			logging.get_logger(__name__)


class    lowerCamelCase    (		lowerCAmelCase__						):






   '''simple docstring'''





   UpperCamelCase__								=["pixel_values"]

   def __init__( self			:   int       ,						lowerCamelCase_			:   Dict = True       ,						lowerCamelCase_			:   str = None       ,						lowerCamelCase_			:   str = PILImageResampling.BICUBIC       ,						lowerCamelCase_			:   Optional[int] = True       ,						lowerCamelCase_			:   Tuple = True       ,						lowerCamelCase_			:   str = 1 / 255       ,						lowerCamelCase_			:   Any = None       ,						lowerCamelCase_			:   List[Any] = True       ,						lowerCamelCase_			:   Dict = None       ,						lowerCamelCase_			:   Dict = None       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   None:
        super().__init__(**_lowerCamelCase  )
        __magic_name__							:	Tuple    							=							size if size is not None else {'''height''': 224, '''width''': 224}
        __magic_name__							:	Optional[Any]    							=							get_size_dict(_lowerCamelCase  )
        __magic_name__							:	Optional[Any]    							=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        __magic_name__							:	Tuple    							=							get_size_dict(_lowerCamelCase       ,						default_to_square=_lowerCamelCase       ,						param_name='''crop_size'''  )

        __magic_name__							:	Optional[Any]    							=							do_resize
        __magic_name__							:	int    							=							do_rescale
        __magic_name__							:	Union[str, Any]    							=							do_normalize
        __magic_name__							:	Optional[Any]    							=							do_center_crop
        __magic_name__							:	Optional[int]    							=							crop_size
        __magic_name__							:	List[str]    							=							size
        __magic_name__							:	List[str]    							=							resample
        __magic_name__							:	int    							=							rescale_factor
        __magic_name__							:	Dict    							=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __magic_name__							:	Dict    							=							image_std if image_std is not None else IMAGENET_DEFAULT_STD

   def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any] = PILImageResampling.BILINEAR       ,						lowerCamelCase_			:   List[Any] = None       ,						**lowerCamelCase_			:   int       ,						)						->   np.ndarray:
        __magic_name__							:	List[Any]    							=							get_size_dict(_lowerCamelCase  )

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

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Optional[Any] = None       ,						**lowerCamelCase_			:   List[Any]       ,						)						->   np.ndarray:
        __magic_name__							:	List[Any]    							=							get_size_dict(_lowerCamelCase  )
        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(_lowerCamelCase       ,						size=(size['''height'''], size['''width'''])       ,						data_format=_lowerCamelCase       ,						**_lowerCamelCase  )

   def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   List[Any] = None       ,						**lowerCamelCase_			:   Dict  )						->   np.ndarray:
        return rescale(_lowerCamelCase       ,						scale=_lowerCamelCase       ,						data_format=_lowerCamelCase       ,						**_lowerCamelCase  )

   def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Tuple       ,						lowerCamelCase_			:   Optional[int] = None       ,						**lowerCamelCase_			:   str       ,						)						->   np.ndarray:
        return normalize(_lowerCamelCase       ,						mean=_lowerCamelCase       ,						std=_lowerCamelCase       ,						data_format=_lowerCamelCase       ,						**_lowerCamelCase  )


   def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[int] = None       ,						lowerCamelCase_			:   List[Any] = None       ,						lowerCamelCase_			:   int = None       ,						lowerCamelCase_			:   Union[str, Any] = None       ,						lowerCamelCase_			:   int = None       ,						lowerCamelCase_			:   Any = None       ,						lowerCamelCase_			:   List[Any] = None       ,						lowerCamelCase_			:   Any = None       ,						lowerCamelCase_			:   List[Any] = None       ,						lowerCamelCase_			:   int = None       ,						lowerCamelCase_			:   int = None       ,						lowerCamelCase_			:   Optional[int] = ChannelDimension.FIRST       ,						**lowerCamelCase_			:   Optional[int]       ,						)						->   BatchFeature:
        __magic_name__							:	Tuple    							=							do_resize if do_resize is not None else self.do_resize
        __magic_name__							:	List[Any]    							=							do_rescale if do_rescale is not None else self.do_rescale
        __magic_name__							:	str    							=							do_normalize if do_normalize is not None else self.do_normalize
        __magic_name__							:	Optional[Any]    							=							do_center_crop if do_center_crop is not None else self.do_center_crop
        __magic_name__							:	str    							=							crop_size if crop_size is not None else self.crop_size
        __magic_name__							:	Union[str, Any]    							=							get_size_dict(_lowerCamelCase       ,						param_name='''crop_size'''       ,						default_to_square=_lowerCamelCase  )
        __magic_name__							:	Optional[int]    							=							resample if resample is not None else self.resample
        __magic_name__							:	List[Any]    							=							rescale_factor if rescale_factor is not None else self.rescale_factor
        __magic_name__							:	str    							=							image_mean if image_mean is not None else self.image_mean
        __magic_name__							:	Dict    							=							image_std if image_std is not None else self.image_std

        __magic_name__							:	Optional[Any]    							=							size if size is not None else self.size
        __magic_name__							:	int    							=							get_size_dict(_lowerCamelCase  )

        if not is_batched(_lowerCamelCase  ):
             __magic_name__							:	Union[str, Any]    							=							[images]

        if not valid_images(_lowerCamelCase  ):
             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.
        __magic_name__							:	str    							=							[to_numpy_array(_lowerCamelCase  ) for image in images]

        if do_resize:
             __magic_name__							:	Dict    							=							[self.resize(image=_lowerCamelCase       ,						size=_lowerCamelCase       ,						resample=_lowerCamelCase  ) for image in images]

        if do_center_crop:
             __magic_name__							:	int    							=							[self.center_crop(image=_lowerCamelCase       ,						size=_lowerCamelCase  ) for image in images]

        if do_rescale:
             __magic_name__							:	List[str]    							=							[self.rescale(image=_lowerCamelCase       ,						scale=_lowerCamelCase  ) for image in images]

        if do_normalize:
             __magic_name__							:	Dict    							=							[self.normalize(image=_lowerCamelCase       ,						mean=_lowerCamelCase       ,						std=_lowerCamelCase  ) for image in images]

        __magic_name__							:	str    							=							[to_channel_dimension_format(_lowerCamelCase       ,						_lowerCamelCase  ) for image in images]

        __magic_name__							:	Dict    							=							{'''pixel_values''': images}
        return BatchFeature(data=_lowerCamelCase       ,						tensor_type=_lowerCamelCase  )









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    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=inspect.getfile(accelerate.test_utils						)
			UpperCamelCase__								=os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''test_cli.py''']						)

			UpperCamelCase__								=['''accelerate''', '''launch''']
			UpperCamelCase__								=Path.home() / '''.cache/huggingface/accelerate'''
			UpperCamelCase__								='''default_config.yaml'''
			UpperCamelCase__								=config_folder / config_file
			UpperCamelCase__								=config_folder / '''_default_config.yaml'''

			UpperCamelCase__								=Path('''tests/test_configs'''						)

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Optional[int]  )						->   int:
								if cls.config_path.is_file():
													cls.config_path.rename(cls.changed_path  )

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Dict  )						->   List[str]:
								if cls.changed_path.is_file():
													cls.changed_path.rename(cls.config_path  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								__magic_name__							:	Dict    							=							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			:   int  )						->   Optional[int]:
								for config in sorted(self.test_config_path.glob('''**/*.yaml'''  )  ):
													with self.subTest(config_file=lowerCamelCase_  ):
																		execute_subprocess_async(
																		    self.base_cmd + ['''--config_file''', str(lowerCamelCase_  ), self.test_file_path]       ,						env=os.environ.copy()  )


			def 							UpperCAmelCase__       ( self			:   int  )						->   Union[str, Any]:
								execute_subprocess_async(['''accelerate''', '''test''']       ,						env=os.environ.copy()  )






class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								='''test-tpu'''
			UpperCamelCase__								='''us-central1-a'''
			UpperCamelCase__								='''ls'''
			UpperCamelCase__								=['''accelerate''', '''tpu-config''']
			UpperCamelCase__								='''cd /usr/share'''
			UpperCamelCase__								='''tests/test_samples/test_command_file.sh'''
			UpperCamelCase__								='''Running gcloud compute tpus tpu-vm ssh'''

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[Any]:
								__magic_name__							:	str    							=							run_command(
								    self.cmd
								    + ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								__magic_name__							:	int    							=							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=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Tuple:
								__magic_name__							:	Any    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug''']       ,						return_stdout=lowerCamelCase_  )
								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'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Dict:
								__magic_name__							:	str    							=							run_command(
								    self.cmd
								    + [
								        '''--config_file''',
								        '''tests/test_configs/latest.yaml''',
								        '''--command''',
								        self.command,
								        '''--command''',
								        '''echo "Hello World"''',
								        '''--debug''',
								    ]       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							run_command(
								    self.cmd
								    + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   int  )						->   List[Any]:
								__magic_name__							:	List[Any]    							=							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=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   Any  )						->   Tuple:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd
								    + [
								        '''--config_file''',
								        '''tests/test_configs/latest.yaml''',
								        '''--install_accelerate''',
								        '''--accelerate_version''',
								        '''12.0.0''',
								        '''--debug''',
								    ]       ,						return_stdout=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)



