



import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__snake_case    :		str  =logging.getLogger(__name__)






def      lowerCAmelCase__		(   lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					git.Repo(search_parent_directories=lowerCamelCase_)
				lowerCAmelCase__					:    int												=					{
				    '''repo_id''': str(lowerCamelCase_),
				    '''repo_sha''': str(repo.head.object.hexsha),
				    '''repo_branch''': str(repo.active_branch),
				}

				with open(os.path.join(lowerCamelCase_	,'''git_log.json''')	,'''w''') as f:
								json.dump(lowerCamelCase_	,lowerCamelCase_	,indent=4)






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				if params.n_gpu <= 0:
								lowerCAmelCase__					:    Tuple												=					0
								lowerCAmelCase__					:    List[str]												=					-1
								lowerCAmelCase__					:    Union[str, Any]												=					True
								lowerCAmelCase__					:    Optional[Any]												=					False
								return

				assert torch.cuda.is_available()

				logger.info('''Initializing GPUs''')
				if params.n_gpu > 1:
								assert params.local_rank != -1

								lowerCAmelCase__					:    List[str]												=					int(os.environ['''WORLD_SIZE'''])
								lowerCAmelCase__					:    Optional[int]												=					int(os.environ['''N_GPU_NODE'''])
								lowerCAmelCase__					:    str												=					int(os.environ['''RANK'''])

								# number of nodes / node ID
								lowerCAmelCase__					:    Any												=					params.world_size // params.n_gpu_per_node
								lowerCAmelCase__					:    Tuple												=					params.global_rank // params.n_gpu_per_node
								lowerCAmelCase__					:    Optional[int]												=					True

								assert params.n_nodes == int(os.environ['''N_NODES'''])
								assert params.node_id == int(os.environ['''NODE_RANK'''])

				# local job (single GPU)
				else:
								assert params.local_rank == -1

								lowerCAmelCase__					:    Optional[Any]												=					1
								lowerCAmelCase__					:    Union[str, Any]												=					0
								lowerCAmelCase__					:    List[str]												=					0
								lowerCAmelCase__					:    Any												=					0
								lowerCAmelCase__					:    int												=					1
								lowerCAmelCase__					:    str												=					1
								lowerCAmelCase__					:    Dict												=					False

				# sanity checks
				assert params.n_nodes >= 1
				assert 0 <= params.node_id < params.n_nodes
				assert 0 <= params.local_rank <= params.global_rank < params.world_size
				assert params.world_size == params.n_nodes * params.n_gpu_per_node

				# define whether this is the master process / if we are in multi-node distributed mode
				lowerCAmelCase__					:    Dict												=					params.node_id == 0 and params.local_rank == 0
				lowerCAmelCase__					:    Dict												=					params.n_nodes > 1

				# summary
				lowerCAmelCase__					:    Dict												=					f"""--- Global rank: {params.global_rank} - """
				logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes)
				logger.info(PREFIX + '''Node ID        : %i''' % params.node_id)
				logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank)
				logger.info(PREFIX + '''World size     : %i''' % params.world_size)
				logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node)
				logger.info(PREFIX + '''Master         : %s''' % str(params.is_master))
				logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node))
				logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu))
				logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname())

				# set GPU device
				torch.cuda.set_device(params.local_rank)

				# initialize multi-GPU
				if params.multi_gpu:
								logger.info('''Initializing PyTorch distributed''')
								torch.distributed.init_process_group(
								    init_method='''env://'''	,backend='''nccl'''	,)






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				np.random.seed(args.seed)
				torch.manual_seed(args.seed)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed)






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):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=7		,__lowerCamelCase=3		,__lowerCamelCase=18		,__lowerCamelCase=30		,__lowerCamelCase=4_00		,__lowerCamelCase=True		,__lowerCamelCase=None		,__lowerCamelCase=True		,__lowerCamelCase=False		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=[0.5, 0.5, 0.5]		,__lowerCamelCase=[0.5, 0.5, 0.5]		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					parent
						lowerCAmelCase__					:    Optional[Any]												=					batch_size
						lowerCAmelCase__					:    str												=					num_channels
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    List[Any]												=					min_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					max_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					do_resize
						lowerCAmelCase__					:    str												=					size if size is not None else {'''height''': 18, '''width''': 20}
						lowerCAmelCase__					:    List[str]												=					do_thumbnail
						lowerCAmelCase__					:    str												=					do_align_axis
						lowerCAmelCase__					:    Optional[Any]												=					do_pad
						lowerCAmelCase__					:    Tuple												=					do_normalize
						lowerCAmelCase__					:    List[Any]												=					image_mean
						lowerCAmelCase__					:    List[str]												=					image_std



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						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__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =DonutImageProcessor if is_vision_available() else None



		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					DonutImageProcessingTester(self			)



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_resize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''size'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_thumbnail'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_align_long_axis'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_pad'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_normalize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_mean'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_std'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					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__					:    Union[str, Any]												=					self.image_processing_class.from_dict(self.image_processor_dict		,size=(42, 84)			)
						self.assertEqual(image_processor.size		,{'''height''': 84, '''width''': 42}			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						pass



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						lowerCAmelCase__					:    Dict												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,Image.Image			)

						# 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__					:    Optional[int]												=					image_processing(__lowerCamelCase		,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 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						lowerCAmelCase__					:    Any												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,numpify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,np.ndarray			)

						# Test not batched input
						lowerCAmelCase__					:    Union[str, 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__					:    int												=					image_processing(__lowerCamelCase		,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 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						lowerCAmelCase__					:    int												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,torchify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,torch.Tensor			)

						# Test not batched input
						lowerCAmelCase__					:    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__					:    Dict												=					image_processing(__lowerCamelCase		,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'''],
						    )		,)

