

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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Tuple=False )   ->       List[str]:

				'''simple docstring'''





				try:
								_UpperCAmelCase       :		Union[str, Any] 		=    os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								_UpperCAmelCase       :		Any 		=    default
				else:
								# KEY is set, convert it to True or False.
								try:
												_UpperCAmelCase       :		Union[str, Any] 		=    strtobool(lowerCamelCase_ )
								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


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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any] )   ->       Optional[Any]:

				'''simple docstring'''





				return unittest.skip("Test was skipped" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any] )   ->       List[str]:

				'''simple docstring'''





				return unittest.skipUnless(_run_slow_tests  ,      "test is slow" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple )   ->       Union[str, Any]:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict )   ->       Any:

				'''simple docstring'''





				return unittest.skipUnless(torch.cuda.is_available()  ,      "test requires a GPU" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       Optional[int]:

				'''simple docstring'''





				return unittest.skipUnless(is_xpu_available()  ,      "test requires a XPU" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int )   ->       int:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Tuple:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       str:

				'''simple docstring'''





				return unittest.skipUnless(is_bnb_available()  ,      "test requires the bitsandbytes library" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Dict:

				'''simple docstring'''





				return unittest.skipUnless(is_tpu_available()  ,      "test requires TPU" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Tuple:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       Optional[Any]:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Union[str, Any]:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       Optional[int]:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any] )   ->       List[str]:

				'''simple docstring'''





				return unittest.skipUnless(is_safetensors_available()  ,      "test requires safetensors" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any] )   ->       Tuple:

				'''simple docstring'''





				return unittest.skipUnless(is_deepspeed_available()  ,      "test requires DeepSpeed" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Optional[int]:

				'''simple docstring'''





				return unittest.skipUnless(is_torch_version(">="  ,      "1.12.0" )  ,      "test requires torch version >= 1.12.0" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple=None  ,      lowerCAmelCase:  Optional[Any]=None )   ->       Any:

				'''simple docstring'''





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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Optional[int]:

				'''simple docstring'''





				return unittest.skipUnless(is_tensorboard_available()  ,      "test requires Tensorboard" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       int:

				'''simple docstring'''





				return unittest.skipUnless(is_wandb_available()  ,      "test requires wandb" )(lowerCamelCase_ )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       Optional[Any]:

				'''simple docstring'''





				return unittest.skipUnless(is_comet_ml_available()  ,      "test requires comet_ml" )(lowerCamelCase_ )


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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       Optional[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"  ,      )(lowerCamelCase_ )

class 				a  (	unittest.TestCase				):
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    tempfile.mkdtemp()


						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


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





						def        _UpperCAmelCase				(       self	):







										'''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(UpperCAmelCase_	)







class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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







class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    mocks if isinstance(UpperCAmelCase_						,					(tuple, list)	) else [mocks]
										for m in self.mocks:
														m.start()
														self.addCleanup(m.stop	)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any] )   ->       int:

				'''simple docstring'''





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

class 				a  :


						def __init__(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    returncode
										_UpperCAmelCase       :		Any 		=    stdout
										_UpperCAmelCase       :		Union[str, Any] 		=    stderr







async def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Optional[Any] )   ->       Union[str, Any]:

				'''simple docstring'''





				while True:
								_UpperCAmelCase       :		Optional[int] 		=    await stream.readline()
								if line:
												callback(lowerCamelCase_ )
								else:
												break

async def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Tuple=None  ,      lowerCAmelCase:  List[str]=None  ,      lowerCAmelCase:  str=None  ,      lowerCAmelCase:  List[str]=False  ,      lowerCAmelCase:  str=False )   ->       _RunOutput:

				'''simple docstring'''





				if echo:
								print("\nRunning: "  ,      " ".join(lowerCamelCase_ ) )

				_UpperCAmelCase       :		Any 		=    await asyncio.create_subprocess_exec(
				    cmd[0]  ,      *cmd[1:]  ,      stdin=lowerCamelCase_  ,      stdout=asyncio.subprocess.PIPE  ,      stderr=asyncio.subprocess.PIPE  ,      env=lowerCamelCase_  ,      )

				# 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)

				_UpperCAmelCase       :		Optional[int] 		=    []
				_UpperCAmelCase       :		int 		=    []

				def tee(lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Any="" ):
								_UpperCAmelCase       :		Union[str, Any] 		=    line.decode("utf-8" ).rstrip()
								sink.append(lowerCamelCase_ )
								if not quiet:
												print(lowerCamelCase_  ,      lowerCamelCase_  ,      file=lowerCamelCase_ )

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  Union[str, Any]=None  ,      lowerCAmelCase:  Any=None  ,      lowerCAmelCase:  Union[str, Any]=180  ,      lowerCAmelCase:  List[str]=False  ,      lowerCAmelCase:  Union[str, Any]=True )   ->       _RunOutput:

				'''simple docstring'''





				_UpperCAmelCase       :		Union[str, Any] 		=    asyncio.get_event_loop()
				_UpperCAmelCase       :		Tuple 		=    loop.run_until_complete(
				    _stream_subprocess(lowerCamelCase_  ,      env=lowerCamelCase_  ,      stdin=lowerCamelCase_  ,      timeout=lowerCamelCase_  ,      quiet=lowerCamelCase_  ,      echo=lowerCamelCase_ ) )

				_UpperCAmelCase       :		Union[str, Any] 		=    ' '.join(lowerCamelCase_ )
				if result.returncode > 0:
								_UpperCAmelCase       :		int 		=    '\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 				a  (	__lowerCamelCase				):
						pass







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=False )   ->       Dict:

				'''simple docstring'''





				try:
								_UpperCAmelCase       :		int 		=    subprocess.check_output(lowerCamelCase_  ,      stderr=subprocess.STDOUT )
								if return_stdout:
												if hasattr(lowerCamelCase_  ,      "decode" ):
																_UpperCAmelCase       :		int 		=    output.decode("utf-8" )
												return output
				except subprocess.CalledProcessError as e:
								raise SubprocessCallException(
								    F'Command `{" ".join(lowerCamelCase_ )}` failed with the following error:\n\n{e.output.decode()}' ) from e





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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Union[str, Any] )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Dict 		=    b.T
				_UpperCAmelCase       :		Dict 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=1 )
				_UpperCAmelCase       :		Optional[Any] 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=0 )
				_UpperCAmelCase       :		str 		=    np.matmul(lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    aa[:, None] - 2 * ab + ba[None, :]
				return d

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Dict )   ->       int:
				_UpperCAmelCase       :		Any 		=    x.reshape(-1  ,      3 )
				_UpperCAmelCase       :		List[str] 		=    squared_euclidean_distance(lowerCAmelCase  ,      lowerCAmelCase )
				return np.argmin(lowerCAmelCase  ,      axis=1 )





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["pixel_values"]


						def __init__(       self						,					A_ = None						,					A_ = True						,					A_ = None						,					A_ = PILImageResampling.BILINEAR						,					A_ = True						,					A_ = True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    size if size is not None else {"height": 256, "width": 256}
										_UpperCAmelCase       :		Optional[int] 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    np.array(A_	) if clusters is not None else None
										_UpperCAmelCase       :		int 		=    do_resize
										_UpperCAmelCase       :		Union[str, Any] 		=    size
										_UpperCAmelCase       :		Optional[Any] 		=    resample
										_UpperCAmelCase       :		str 		=    do_normalize
										_UpperCAmelCase       :		List[str] 		=    do_color_quantize


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BILINEAR						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    get_size_dict(A_	)
										if "height" not in size or "width" not in size:
														raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'	)
										return resize(
										    A_						,					size=(size["height"], size["width"])						,					resample=A_						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    rescale(image=A_						,					scale=1 / 1_27.5						,					data_format=A_	)
										_UpperCAmelCase       :		List[Any] 		=    image - 1
										return image





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = ChannelDimension.FIRST						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase       :		Any 		=    size if size is not None else self.size
										_UpperCAmelCase       :		Dict 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		List[Any] 		=    resample if resample is not None else self.resample
										_UpperCAmelCase       :		Any 		=    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase       :		Optional[Any] 		=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										_UpperCAmelCase       :		Any 		=    clusters if clusters is not None else self.clusters
										_UpperCAmelCase       :		Optional[int] 		=    np.array(A_	)

										_UpperCAmelCase       :		List[str] 		=    make_list_of_images(A_	)

										if not valid_images(A_	):
														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_color_quantize and clusters is None:
														raise ValueError("Clusters must be specified if do_color_quantize is True."	)

										# All transformations expect numpy arrays.
										_UpperCAmelCase       :		List[str] 		=    [to_numpy_array(A_	) for image in images]

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

										if do_normalize:
														_UpperCAmelCase       :		List[str] 		=    [self.normalize(image=A_	) for image in images]

										if do_color_quantize:
														_UpperCAmelCase       :		Tuple 		=    [to_channel_dimension_format(A_						,					ChannelDimension.LAST	) for image in images]
														# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
														_UpperCAmelCase       :		List[str] 		=    np.array(A_	)
														_UpperCAmelCase       :		List[Any] 		=    color_quantize(A_						,					A_	).reshape(images.shape[:-1]	)

														# flatten to (batch_size, height*width)
														_UpperCAmelCase       :		Any 		=    images.shape[0]
														_UpperCAmelCase       :		List[Any] 		=    images.reshape(A_						,					-1	)

														# We need to convert back to a list of images to keep consistent behaviour across processors.
														_UpperCAmelCase       :		Union[str, Any] 		=    list(A_	)
										else:
														_UpperCAmelCase       :		Optional[Any] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

										_UpperCAmelCase       :		List[Any] 		=    {"input_ids": images}
										return BatchFeature(data=A_						,					tensor_type=A_	)


