



"""simple docstring"""





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 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=7	,       snake_case=3	,       snake_case=18	,       snake_case=30	,       snake_case=400	,       snake_case=True	,       snake_case=None	,       snake_case=True	,       snake_case=False	,       snake_case=True	,       snake_case=True	,       snake_case=[0.5, 0.5, 0.5]	,       snake_case=[0.5, 0.5, 0.5]	,       )			->							List[str]:
														_UpperCAmelCase							 =		parent
														_UpperCAmelCase							 =		batch_size
														_UpperCAmelCase							 =		num_channels
														_UpperCAmelCase							 =		image_size
														_UpperCAmelCase							 =		min_resolution
														_UpperCAmelCase							 =		max_resolution
														_UpperCAmelCase							 =		do_resize
														_UpperCAmelCase							 =		size if size is not None else {'height': 18, 'width': 20}
														_UpperCAmelCase							 =		do_thumbnail
														_UpperCAmelCase							 =		do_align_axis
														_UpperCAmelCase							 =		do_pad
														_UpperCAmelCase							 =		do_normalize
														_UpperCAmelCase							 =		image_mean
														_UpperCAmelCase							 =		image_std






							def        lowerCamelCase_   (  self						)			->							int:
														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 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					DonutImageProcessor if is_vision_available() else None






							def        lowerCamelCase_   (  self						)			->							List[Any]:
														_UpperCAmelCase							 =		DonutImageProcessingTester(self						)






							@property
							def        lowerCamelCase_   (  self						)			->							Any:
														return self.image_processor_tester.prepare_image_processor_dict()






							def        lowerCamelCase_   (  self						)			->							str:
														_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
														self.assertTrue(hasattr(snake_case	,       'do_resize'						)						)
														self.assertTrue(hasattr(snake_case	,       'size'						)						)
														self.assertTrue(hasattr(snake_case	,       'do_thumbnail'						)						)
														self.assertTrue(hasattr(snake_case	,       'do_align_long_axis'						)						)
														self.assertTrue(hasattr(snake_case	,       'do_pad'						)						)
														self.assertTrue(hasattr(snake_case	,       'do_normalize'						)						)
														self.assertTrue(hasattr(snake_case	,       'image_mean'						)						)
														self.assertTrue(hasattr(snake_case	,       'image_std'						)						)






							def        lowerCamelCase_   (  self						)			->							str:
														_UpperCAmelCase							 =		self.image_processing_class.from_dict(self.image_processor_dict						)
														self.assertEqual(image_processor.size	,       {'height': 18, 'width': 20}						)

														_UpperCAmelCase							 =		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
														_UpperCAmelCase							 =		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						)			->							Any:
														pass






							@is_flaky()
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
														# Initialize image_processing
														_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
														# create random PIL images
														_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case						)
														for image in image_inputs:
																					self.assertIsInstance(snake_case	,       Image.Image						)

														# Test not batched input
														_UpperCAmelCase							 =		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
														_UpperCAmelCase							 =		image_processing(snake_case	,       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						)			->							List[Any]:
														# Initialize image_processing
														_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
														# create random numpy tensors
														_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case	,       numpify=snake_case						)
														for image in image_inputs:
																					self.assertIsInstance(snake_case	,       np.ndarray						)

														# Test not batched input
														_UpperCAmelCase							 =		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
														_UpperCAmelCase							 =		image_processing(snake_case	,       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						)			->							Dict:
														# Initialize image_processing
														_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
														# create random PyTorch tensors
														_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case	,       torchify=snake_case						)
														for image in image_inputs:
																					self.assertIsInstance(snake_case	,       torch.Tensor						)

														# Test not batched input
														_UpperCAmelCase							 =		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
														_UpperCAmelCase							 =		image_processing(snake_case	,       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'],
														    )	,       )







"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

lowercase  							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()
									_UpperCAmelCase							 =		[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
									_dump_articles(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([T5_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_UpperCAmelCase							 =		{
									    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
									    'de': [
									        'Maschinelles Lernen ist großartig, oder?',
									        'Ich esse gerne Bananen',
									        'Morgen ist wieder ein toller Tag!',
									    ],
									}

									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(snake_case						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
									testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']						)

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(snake_case						)
											for w in expected_strings:
													assert w in cs.out
											for w in un_expected_strings:
													assert w not in cs.out
											assert Path(snake_case						).exists()
											os.remove(Path(snake_case						)						)


