





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
	import torch

if is_vision_available():
	import PIL


lowerCAmelCase_    =    logging.get_logger(__name__)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple[int, int]:






				"""simple docstring"""


				def constraint_to_multiple_of(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=0			,    _UpperCamelCase=None			):
								snake_case_						:   Dict           =  round(val / multiple			) * multiple

								if max_val is not None and x > max_val:
												snake_case_						:   Tuple           =  math.floor(val / multiple			) * multiple

								if x < min_val:
												snake_case_						:   Optional[int]           =  math.ceil(val / multiple			) * multiple

								return x

				snake_case_						:   Tuple           =  (output_size, output_size) if isinstance(_UpperCamelCase			,    _UpperCamelCase			) else output_size

				snake_case_      ,							snake_case_						:   Any           =  get_image_size(_UpperCamelCase			)
				snake_case_      ,							snake_case_						:   int           =  output_size

				# determine new height and width
				snake_case_						:   List[Any]           =  output_height / input_height
				snake_case_						:   Optional[Any]           =  output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width			) < abs(1 - scale_height			):
												# fit width
												snake_case_						:   int           =  scale_width
								else:
												# fit height
												snake_case_						:   Union[str, Any]           =  scale_height

				snake_case_						:   int           =  constraint_to_multiple_of(scale_height * input_height			,    multiple=_UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  constraint_to_multiple_of(scale_width * input_width			,    multiple=_UpperCamelCase			)

				return (new_height, new_width)





class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					List[Any]	      =				['''pixel_values''']






			def __init__(self ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = PILImageResampling.BILINEAR ,							__magic_name__ = False ,							__magic_name__ = 1 ,							__magic_name__ = True ,							__magic_name__ = 1 / 255 ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       None:


							'''simple docstring'''



							super().__init__(**__magic_name__   )
							snake_case_						:   Union[str, Any]           =  size if size is not None else {'''height''': 384, '''width''': 384}
							snake_case_						:   Optional[Any]           =  get_size_dict(__magic_name__   )
							snake_case_						:   str           =  do_resize
							snake_case_						:   Union[str, Any]           =  size
							snake_case_						:   Optional[Any]           =  keep_aspect_ratio
							snake_case_						:   str           =  ensure_multiple_of
							snake_case_						:   List[Any]           =  resample
							snake_case_						:   Any           =  do_rescale
							snake_case_						:   Dict           =  rescale_factor
							snake_case_						:   Optional[Any]           =  do_normalize
							snake_case_						:   Tuple           =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							snake_case_						:   Union[str, Any]           =  image_std if image_std is not None else IMAGENET_STANDARD_STD






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = False ,							__magic_name__ = 1 ,							__magic_name__ = PILImageResampling.BICUBIC ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  get_size_dict(__magic_name__   )
							if "height" not in size or "width" not in size:
											raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''   )
							snake_case_						:   Any           =  get_resize_output_image_size(
							    __magic_name__ ,							output_size=(size['''height'''], size['''width''']) ,							keep_aspect_ratio=__magic_name__ ,							multiple=__magic_name__ ,							)
							return resize(__magic_name__ ,							size=__magic_name__ ,							resample=__magic_name__ ,							data_format=__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       int:


							'''simple docstring'''



							return rescale(__magic_name__ ,							scale=__magic_name__ ,							data_format=__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



							return normalize(__magic_name__ ,							mean=__magic_name__ ,							std=__magic_name__ ,							data_format=__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = ChannelDimension.FIRST ,							**__magic_name__ ,							)					->       PIL.Image.Image:


							'''simple docstring'''



							snake_case_						:   int           =  do_resize if do_resize is not None else self.do_resize
							snake_case_						:   int           =  size if size is not None else self.size
							snake_case_						:   Dict           =  get_size_dict(__magic_name__   )
							snake_case_						:   List[str]           =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							snake_case_						:   Optional[Any]           =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							snake_case_						:   Optional[Any]           =  resample if resample is not None else self.resample
							snake_case_						:   Optional[Any]           =  do_rescale if do_rescale is not None else self.do_rescale
							snake_case_						:   Tuple           =  rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_						:   Tuple           =  do_normalize if do_normalize is not None else self.do_normalize
							snake_case_						:   int           =  image_mean if image_mean is not None else self.image_mean
							snake_case_						:   Tuple           =  image_std if image_std is not None else self.image_std

							snake_case_						:   Tuple           =  make_list_of_images(__magic_name__   )

							if not valid_images(__magic_name__   ):
											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 or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''   )

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

							if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

							# All transformations expect numpy arrays.
							snake_case_						:   Any           =  [to_numpy_array(__magic_name__   ) for image in images]

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

							if do_rescale:
											snake_case_						:   Any           =  [self.rescale(image=__magic_name__ ,							scale=__magic_name__   ) for image in images]

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

							snake_case_						:   Dict           =  [to_channel_dimension_format(__magic_name__ ,							__magic_name__   ) for image in images]

							snake_case_						:   Tuple           =  {'''pixel_values''': images}
							return BatchFeature(data=__magic_name__ ,							tensor_type=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
											if len(__magic_name__   ) != len(__magic_name__   ):
															raise ValueError(
															    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

											if is_torch_tensor(__magic_name__   ):
															snake_case_						:   Optional[int]           =  target_sizes.numpy()

											snake_case_						:   Dict           =  []

											for idx in range(len(__magic_name__   )   ):
															snake_case_						:   int           =  torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0   ) ,							size=target_sizes[idx] ,							mode='''bilinear''' ,							align_corners=__magic_name__   )
															snake_case_						:   List[str]           =  resized_logits[0].argmax(dim=0   )
															semantic_segmentation.append(__magic_name__   )
							else:
											snake_case_						:   List[Any]           =  logits.argmax(dim=1   )
											snake_case_						:   int           =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

							return semantic_segmentation







import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						int:






				"""simple docstring"""


				try:
								snake_case_						:   Optional[Any]           =  os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								snake_case_						:   Tuple           =  default
				else:
								# KEY is set, convert it to True or False.
								try:
												snake_case_						:   Union[str, Any]           =  strtobool(_UpperCamelCase			)
								except ValueError:
												# More values are supported, but let's keep the message simple.
												raise ValueError(f'''If set, {key} must be yes or no.'''			)
				return _value


lowerCAmelCase_    =    parse_flag_from_env('''RUN_SLOW''', default=False)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skip('''Test was skipped'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				return unittest.skipUnless(_run_slow_tests			,    '''test is slow'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return unittest.skipUnless(not torch.cuda.is_available()			,    '''test requires only a CPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.is_available()			,    '''test requires a GPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				return unittest.skipUnless(is_xpu_available()			,    '''test requires a XPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_mps_available()			,    '''test requires a `mps` backend support in `torch`'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()			,    '''test requires the Hugging Face suite'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(is_bnb_available()			,    '''test requires the bitsandbytes library'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_tpu_available()			,    '''test requires TPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() == 1			,    '''test requires a GPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() == 1			,    '''test requires a XPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() > 1			,    '''test requires multiple GPUs'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() > 1			,    '''test requires multiple XPUs'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				return unittest.skipUnless(is_safetensors_available()			,    '''test requires safetensors'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(is_deepspeed_available()			,    '''test requires DeepSpeed'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(is_torch_version('''>='''			,    '''1.12.0'''			)			,    '''test requires torch version >= 1.12.0'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase=None			,    _UpperCamelCase=None			)		->						Tuple:






				"""simple docstring"""


				if test_case is None:
								return partial(_UpperCamelCase			,    version=_UpperCamelCase			)
				return unittest.skipUnless(is_torch_version('''>='''			,    _UpperCamelCase			)			,    f'''test requires torch version >= {version}'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(is_tensorboard_available()			,    '''test requires Tensorboard'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				return unittest.skipUnless(is_wandb_available()			,    '''test requires wandb'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_comet_ml_available()			,    '''test requires comet_ml'''			)(_UpperCamelCase			)


lowerCAmelCase_    =    (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				return unittest.skipUnless(
				    _atleast_one_tracker_available			,    '''test requires at least one tracker to be available and for `comet_ml` to not be installed'''			,    )(_UpperCamelCase			)



class  __lowerCAmelCase	(      unittest.TestCase					):
			lowerCamelCase_					:					str	      =				True






			@classmethod
			def       lowerCamelCase  (cls   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  tempfile.mkdtemp()






			@classmethod
			def       lowerCamelCase  (cls   )					->       List[Any]:


							'''simple docstring'''



							if os.path.exists(cls.tmpdir   ):
											shutil.rmtree(cls.tmpdir   )






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							if self.clear_on_setup:
											for path in Path(self.tmpdir   ).glob('''**/*'''   ):
															if path.is_file():
																			path.unlink()
															elif path.is_dir():
																			shutil.rmtree(__magic_name__   )




class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()




class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   str           =  mocks if isinstance(__magic_name__ ,							(tuple, list)   ) else [mocks]
							for m in self.mocks:
											m.start()
											self.addCleanup(m.stop   )






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  AcceleratorState()
				snake_case_						:   List[str]           =  tensor[None].clone().to(state.device			)
				snake_case_						:   Optional[Any]           =  gather(_UpperCamelCase			).cpu()
				snake_case_						:   Optional[Any]           =  tensor[0].cpu()
				for i in range(tensors.shape[0]			):
								if not torch.equal(tensors[i]			,    _UpperCamelCase			):
												return False
				return True



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  returncode
							snake_case_						:   List[Any]           =  stdout
							snake_case_						:   Tuple           =  stderr






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				while True:
								snake_case_						:   Tuple           =  await stream.readline()
								if line:
												callback(_UpperCamelCase			)
								else:
												break






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=False			,    _UpperCamelCase=False			)		->						_RunOutput:






				"""simple docstring"""


				if echo:
								print('''\nRunning: '''			,    ''' '''.join(_UpperCamelCase			)			)

				snake_case_						:   List[str]           =  await asyncio.create_subprocess_exec(
				    cmd[0]			,    *cmd[1:]			,    stdin=_UpperCamelCase			,    stdout=asyncio.subprocess.PIPE			,    stderr=asyncio.subprocess.PIPE			,    env=_UpperCamelCase			,    )

				# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
				# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
				#
				# If it starts hanging, will need to switch to the following code. The problem is that no data
				# will be seen until it's done and if it hangs for example there will be no debug info.
				# out, err = await p.communicate()
				# return _RunOutput(p.returncode, out, err)

				snake_case_						:   List[Any]           =  []
				snake_case_						:   List[Any]           =  []

				def tee(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=""			):
								snake_case_						:   Union[str, Any]           =  line.decode('''utf-8'''			).rstrip()
								sink.append(_UpperCamelCase			)
								if not quiet:
												print(_UpperCamelCase			,    _UpperCamelCase			,    file=_UpperCamelCase			)

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        asyncio.create_task(_read_stream(p.stdout			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stdout			,    label='''stdout:'''			)			)			),
				        asyncio.create_task(_read_stream(p.stderr			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stderr			,    label='''stderr:'''			)			)			),
				    ]			,    timeout=_UpperCamelCase			,    )
				return _RunOutput(await p.wait()			,    _UpperCamelCase			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=180			,    _UpperCamelCase=False			,    _UpperCamelCase=True			)		->						_RunOutput:






				"""simple docstring"""


				snake_case_						:   List[str]           =  asyncio.get_event_loop()
				snake_case_						:   List[Any]           =  loop.run_until_complete(
				    _stream_subprocess(_UpperCamelCase			,    env=_UpperCamelCase			,    stdin=_UpperCamelCase			,    timeout=_UpperCamelCase			,    quiet=_UpperCamelCase			,    echo=_UpperCamelCase			)			)

				snake_case_						:   Optional[int]           =  ''' '''.join(_UpperCamelCase			)
				if result.returncode > 0:
								snake_case_						:   Union[str, Any]           =  '''\n'''.join(result.stderr			)
								raise RuntimeError(
								    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
								    f'''The combined stderr from workers follows:\n{stderr}'''			)

				return result



class  __lowerCAmelCase	(      _a					):
			pass






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						List[Any]:






				"""simple docstring"""


				try:
								snake_case_						:   List[str]           =  subprocess.check_output(_UpperCamelCase			,    stderr=subprocess.STDOUT			)
								if return_stdout:
												if hasattr(_UpperCamelCase			,    '''decode'''			):
																snake_case_						:   Tuple           =  output.decode('''utf-8'''			)
												return output
				except subprocess.CalledProcessError as e:
								raise SubprocessCallException(
								    f'''Command `{" ".join(_UpperCamelCase			)}` failed with the following error:\n\n{e.output.decode()}'''			) from e
