






"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


lowerCamelCase_							      = logging.get_logger(__name__)

def 							snake_case (  ):
			# Get the sagemaker specific mp parameters from smp_options variable.
			UpperCAmelCase_	:					Union[str, Any]     	=   os.getenv("SM_HP_MP_PARAMETERS"      ,"{}"		)
			try:
						# Parse it and check the field "partitions" is included, it is required for model parallel.
						UpperCAmelCase_	:					str     	=   json.loads(A__		)
						if "partitions" not in smp_options:
									return False
			except json.JSONDecodeError:
						return False

			# Get the sagemaker specific framework parameters from mpi_options variable.
			UpperCAmelCase_	:					Tuple     	=   os.getenv("SM_FRAMEWORK_PARAMS"      ,"{}"		)
			try:
						# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
						UpperCAmelCase_	:					Dict     	=   json.loads(A__		)
						if not mpi_options.get("sagemaker_mpi_enabled"      ,A__		):
									return False
			except json.JSONDecodeError:
						return False
			# Lastly, check if the `smdistributed` module is present.
			return importlib.util.find_spec("smdistributed"		) is not None


if is_sagemaker_model_parallel_available():
			import smdistributed.modelparallel.torch as smp

			smp.init()







@dataclass
class      UpperCamelCase_			(__A	):
	__magic_name__       =							field(
	    default=''''''							,		metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}							,		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				super().__post_init__()
				warnings.warn(
				    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
				    "`TrainingArguments` instead."      ,      lowerCAmelCase_      ,      )





	@cached_property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     "torch.device":
				logger.info("PyTorch: setting up devices"		)
				if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
							logger.warning(
							    "torch.distributed process group is initialized, but local_rank == -1. "
							    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"		)
				if self.no_cuda:
							UpperCAmelCase_	:					int     	=   torch.device("cpu"		)
							UpperCAmelCase_	:					str     	=   0
				elif is_sagemaker_model_parallel_available():
							UpperCAmelCase_	:					Any     	=   smp.local_rank()
							UpperCAmelCase_	:					int     	=   torch.device("cuda"      ,      lowerCAmelCase_		)
							UpperCAmelCase_	:					Dict     	=   1
				elif is_sagemaker_dp_enabled():
							import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

							torch.distributed.init_process_group(backend="smddp"      ,      timeout=self.ddp_timeout_delta		)
							UpperCAmelCase_	:					Optional[Any]     	=   int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"		)		)
							UpperCAmelCase_	:					Any     	=   torch.device("cuda"      ,      self.local_rank		)
							UpperCAmelCase_	:					str     	=   1
				elif self.local_rank == -1:
							# if n_gpu is > 1 we'll use nn.DataParallel.
							# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
							# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
							# trigger an error that a device index is missing. Index 0 takes into account the
							# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
							# will use the first GPU in that env, i.e. GPU#1
							UpperCAmelCase_	:					Union[str, Any]     	=   torch.device("cuda:0" if torch.cuda.is_available() else "cpu"		)
							# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
							# the default value.
							UpperCAmelCase_	:					Dict     	=   torch.cuda.device_count()
				else:
							# Here, we'll use torch.distributed.
							# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
							if not torch.distributed.is_initialized():
										torch.distributed.init_process_group(backend="nccl"      ,      timeout=self.ddp_timeout_delta		)
							UpperCAmelCase_	:					Optional[int]     	=   torch.device("cuda"      ,      self.local_rank		)
							UpperCAmelCase_	:					List[Any]     	=   1

				if device.type == "cuda":
							torch.cuda.set_device(lowerCAmelCase_		)

				return device





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Tuple:
				if is_sagemaker_model_parallel_available():
							return smp.dp_size()

				return super().world_size





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				return not is_sagemaker_model_parallel_available()





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Optional[Any]:
				return False










import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor

class   lowerCAmelCase				(   unittest.TestCase     ):
 def __init__( self    : Any     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Dict=7     ,	UpperCAmelCase    : Union[str, Any]=3     ,	UpperCAmelCase    : List[str]=18     ,	UpperCAmelCase    : Dict=30     ,	UpperCAmelCase    : List[Any]=400     ,	UpperCAmelCase    : Optional[Any]=True     ,	UpperCAmelCase    : Tuple=None     ,	UpperCAmelCase    : str=True     ,	UpperCAmelCase    : Tuple=False     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Optional[int]=True     ,	UpperCAmelCase    : Optional[Any]=[0.5, 0.5, 0.5]     ,	UpperCAmelCase    : List[str]=[0.5, 0.5, 0.5]     ,	)     ->      List[str]:
     lowerCamelCase__       : List[Any]   					=						parent
     lowerCamelCase__       : str   					=						batch_size
     lowerCamelCase__       : List[str]   					=						num_channels
     lowerCamelCase__       : str   					=						image_size
     lowerCamelCase__       : List[Any]   					=						min_resolution
     lowerCamelCase__       : int   					=						max_resolution
     lowerCamelCase__       : int   					=						do_resize
     lowerCamelCase__       : int   					=						size if size is not None else {'height': 18, 'width': 20}
     lowerCamelCase__       : Tuple   					=						do_thumbnail
     lowerCamelCase__       : str   					=						do_align_axis
     lowerCamelCase__       : str   					=						do_pad
     lowerCamelCase__       : Optional[Any]   					=						do_normalize
     lowerCamelCase__       : List[str]   					=						image_mean
     lowerCamelCase__       : Dict   					=						image_std







 def 	A_      ( self    : str      )     ->      Any:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }


@require_torch
@require_vision
class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						DonutImageProcessor if is_vision_available() else None
 def 	A_      ( self    : List[Any]      )     ->      int:
     lowerCamelCase__       : Union[str, Any]   					=						DonutImageProcessingTester(self      )
 @property
 def 	A_      ( self    : Dict      )     ->      Union[str, Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def 	A_      ( self    : Dict      )     ->      Any:
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_resize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'size'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_thumbnail'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_align_long_axis'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_pad'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_normalize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_mean'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_std'      )      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     lowerCamelCase__       : List[Any]   					=						self.image_processing_class.from_dict(self.image_processor_dict      )
     self.assertEqual(image_processor.size     ,	{'height': 18, 'width': 20}      )

     lowerCamelCase__       : int   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42      )
     self.assertEqual(image_processor.size     ,	{'height': 42, 'width': 42}      )

     # Previous config had dimensions in (width, height) order
     lowerCamelCase__       : Dict   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=(42, 84)      )
     self.assertEqual(image_processor.size     ,	{'height': 84, 'width': 42}      )
 def 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     pass
 @is_flaky()
 def 	A_      ( self    : List[str]      )     ->      Any:
     # Initialize image_processing
     lowerCamelCase__       : str   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PIL images
     lowerCamelCase__       : List[str]   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	Image.Image      )

     # Test not batched input
     lowerCamelCase__       : List[Any]   					=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Tuple   					=						image_processing(UpperCAmelCase     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)
 @is_flaky()
 def 	A_      ( self    : int      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     lowerCamelCase__       : List[str]   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase     ,	numpify=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	np.ndarray      )

     # Test not batched input
     lowerCamelCase__       : Optional[Any]   					=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Tuple   					=						image_processing(UpperCAmelCase     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)







 @is_flaky()
 def 	A_      ( self    : Any      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : int   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     lowerCamelCase__       : Tuple   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase     ,	torchify=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	torch.Tensor      )

     # Test not batched input
     lowerCamelCase__       : List[str]   					=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Dict   					=						image_processing(UpperCAmelCase     ,	return_tensors='pt'      ).pixel_values
     self.assertEqual(
         encoded_images.shape     ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)





