



"""simple docstring"""





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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase  							=					logging.get_logger(__name__)




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







							_UpperCAmelCase      				=					['''pixel_values''']






							def __init__(  self	,       snake_case = True	,       snake_case = None	,       snake_case = PILImageResampling.BICUBIC	,       snake_case = True	,       snake_case = True	,       snake_case = 1 / 255	,       snake_case = None	,       snake_case = True	,       snake_case = None	,       snake_case = None	,       **snake_case	,       )			->							None:
									super().__init__(**snake_case						)
									_UpperCAmelCase							 =		size if size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase							 =		get_size_dict(snake_case						)
									_UpperCAmelCase							 =		crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase							 =		get_size_dict(snake_case	,       default_to_square=snake_case	,       param_name='crop_size'						)

									_UpperCAmelCase							 =		do_resize
									_UpperCAmelCase							 =		do_rescale
									_UpperCAmelCase							 =		do_normalize
									_UpperCAmelCase							 =		do_center_crop
									_UpperCAmelCase							 =		crop_size
									_UpperCAmelCase							 =		size
									_UpperCAmelCase							 =		resample
									_UpperCAmelCase							 =		rescale_factor
									_UpperCAmelCase							 =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									_UpperCAmelCase							 =		image_std if image_std is not None else IMAGENET_DEFAULT_STD






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case = PILImageResampling.BILINEAR	,       snake_case = None	,       **snake_case	,       )			->							np.ndarray:
									_UpperCAmelCase							 =		get_size_dict(snake_case						)

									if "shortest_edge" in size:
											_UpperCAmelCase							 =		get_resize_output_image_size(snake_case	,       size=size['shortest_edge']	,       default_to_square=snake_case						)
											# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
									elif "height" in size and "width" in size:
											_UpperCAmelCase							 =		(size['height'], size['width'])
									else:
											raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'						)
									return resize(snake_case	,       size=snake_case	,       resample=snake_case	,       data_format=snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case = None	,       **snake_case	,       )			->							np.ndarray:
									_UpperCAmelCase							 =		get_size_dict(snake_case						)
									if "height" not in size or "width" not in size:
											raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'						)
									return center_crop(snake_case	,       size=(size['height'], size['width'])	,       data_format=snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case = None	,       **snake_case						)			->							np.ndarray:
									return rescale(snake_case	,       scale=snake_case	,       data_format=snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case = None	,       **snake_case	,       )			->							np.ndarray:
									return normalize(snake_case	,       mean=snake_case	,       std=snake_case	,       data_format=snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = None	,       snake_case = ChannelDimension.FIRST	,       **snake_case	,       )			->							BatchFeature:
									_UpperCAmelCase							 =		do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase							 =		do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase							 =		do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase							 =		do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase							 =		crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase							 =		get_size_dict(snake_case	,       param_name='crop_size'	,       default_to_square=snake_case						)
									_UpperCAmelCase							 =		resample if resample is not None else self.resample
									_UpperCAmelCase							 =		rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase							 =		image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase							 =		image_std if image_std is not None else self.image_std

									_UpperCAmelCase							 =		size if size is not None else self.size
									_UpperCAmelCase							 =		get_size_dict(snake_case						)

									if not is_batched(snake_case						):
											_UpperCAmelCase							 =		[images]

									if not valid_images(snake_case						):
											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:
											raise ValueError('Size must be specified if do_resize is True.'						)

									if do_center_crop and crop_size is None:
											raise ValueError('Crop size must be specified if do_center_crop is True.'						)

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

									# All transformations expect numpy arrays.
									_UpperCAmelCase							 =		[to_numpy_array(snake_case						) for image in images]

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

									if do_center_crop:
											_UpperCAmelCase							 =		[self.center_crop(image=snake_case	,       size=snake_case						) for image in images]

									if do_rescale:
											_UpperCAmelCase							 =		[self.rescale(image=snake_case	,       scale=snake_case						) for image in images]

									if do_normalize:
											_UpperCAmelCase							 =		[self.normalize(image=snake_case	,       mean=snake_case	,       std=snake_case						) for image in images]

									_UpperCAmelCase							 =		[to_channel_dimension_format(snake_case	,       snake_case						) for image in images]

									_UpperCAmelCase							 =		{'pixel_values': images}
									return BatchFeature(data=snake_case	,       tensor_type=snake_case						)







"""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						)						)


