


import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import 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 BridgeTowerImageProcessor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Dict[str, int] = None			,			lowerCAmelCase__			:int = 32			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Union[int, float] = 1 / 255			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]			,			lowerCAmelCase__			:Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Tuple=7			,			lowerCAmelCase__			:Any=30			,			lowerCAmelCase__			:Optional[Any]=400			,			lowerCAmelCase__			:List[str]=3			,			)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		parent
										__SCREAMING_SNAKE_CASE						:     List[str]					=		do_resize
										__SCREAMING_SNAKE_CASE						:     List[str]					=		size if size is not None else {'''shortest_edge''': 288}
										__SCREAMING_SNAKE_CASE						:     str					=		size_divisor
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		do_rescale
										__SCREAMING_SNAKE_CASE						:     Dict					=		rescale_factor
										__SCREAMING_SNAKE_CASE						:     int					=		do_normalize
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		do_center_crop
										__SCREAMING_SNAKE_CASE						:     int					=		image_mean
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		image_std
										__SCREAMING_SNAKE_CASE						:     Any					=		do_pad
										__SCREAMING_SNAKE_CASE						:     str					=		batch_size
										__SCREAMING_SNAKE_CASE						:     Dict					=		num_channels
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		min_resolution
										__SCREAMING_SNAKE_CASE						:     Tuple					=		max_resolution


				def 		__magic_name__(       self			:str	)		->			Any:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "size_divisor": self.size_divisor,
										}



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Tuple=False	)		->			List[Any]:
										if not batched:
																__SCREAMING_SNAKE_CASE						:     Dict					=		self.size['''shortest_edge''']
																__SCREAMING_SNAKE_CASE						:     int					=		image_inputs[0]
																if isinstance(lowerCAmelCase__			,			Image.Image	):
																						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		image.size
																else:
																						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		image.shape[1], image.shape[2]
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		size / min(lowerCAmelCase__			,			lowerCAmelCase__	)
																if h < w:
																						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		size, scale * w
																else:
																						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		scale * h, size

																__SCREAMING_SNAKE_CASE						:     Tuple					=		int((1_333 / 800) * size	)
																if max(lowerCAmelCase__			,			lowerCAmelCase__	) > max_size:
																						__SCREAMING_SNAKE_CASE						:     Tuple					=		max_size / max(lowerCAmelCase__			,			lowerCAmelCase__	)
																						__SCREAMING_SNAKE_CASE						:     Any					=		newh * scale
																						__SCREAMING_SNAKE_CASE						:     List[Any]					=		neww * scale

																__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		int(newh + 0.5	), int(neww + 0.5	)
																__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		(
																    newh // self.size_divisor * self.size_divisor,
																    neww // self.size_divisor * self.size_divisor,
																)

										else:
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
																for image in image_inputs:
																						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		self.get_expected_values([image]	)
																						expected_values.append((expected_height, expected_width)	)
																__SCREAMING_SNAKE_CASE						:     int					=		max(lowerCAmelCase__			,			key=lambda lowerCAmelCase__	: item[0]	)[0]
																__SCREAMING_SNAKE_CASE						:     str					=		max(lowerCAmelCase__			,			key=lambda lowerCAmelCase__	: item[1]	)[1]

										return expected_height, expected_width




@require_torch
@require_vision
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		BridgeTowerImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:Optional[int]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		BridgeTowerImageProcessingTester(self	)


				@property
				def 		__magic_name__(       self			:Dict	)		->			Optional[int]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(       self			:List[str]	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     str					=		self.image_processing_class(**self.image_processor_dict	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_mean'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_std'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_normalize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_resize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''size'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''size_divisor'''	)	)


				def 		__magic_name__(       self			:int	)		->			List[str]:
										pass


				def 		__magic_name__(       self			:Optional[int]	)		->			int:
										# Initialize image processor
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PIL images
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     Dict					=		image_processing(image_inputs[0]			,			return_tensors='''pt'''	).pixel_values

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processor_tester.get_expected_values(lowerCAmelCase__	)
										self.assertEqual(
										    encoded_images.shape			,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		self.image_processor_tester.get_expected_values(lowerCAmelCase__			,			batched=lowerCAmelCase__	)
										self.assertEqual(
										    encoded_images.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )			,			)


				def 		__magic_name__(       self			:str	)		->			Any:
										# Initialize image processor
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     str					=		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
										__SCREAMING_SNAKE_CASE						:     Any					=		image_processing(image_inputs[0]			,			return_tensors='''pt'''	).pixel_values

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		self.image_processor_tester.get_expected_values(lowerCAmelCase__	)
										self.assertEqual(
										    encoded_images.shape			,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     int					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.image_processor_tester.get_expected_values(lowerCAmelCase__			,			batched=lowerCAmelCase__	)
										self.assertEqual(
										    encoded_images.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )			,			)



				def 		__magic_name__(       self			:List[str]	)		->			Any:
										# Initialize image processor
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Any					=		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
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(image_inputs[0]			,			return_tensors='''pt'''	).pixel_values

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.image_processor_tester.get_expected_values(lowerCAmelCase__	)
										self.assertEqual(
										    encoded_images.shape			,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Dict					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		self.image_processor_tester.get_expected_values(lowerCAmelCase__			,			batched=lowerCAmelCase__	)
										self.assertEqual(
										    encoded_images.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )			,			)




from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:int			,			lowerCAmelCase__			:NestedDataStructureLike[PathLike]			,			lowerCAmelCase__			:Optional[NamedSplit] = None			,			lowerCAmelCase__			:Optional[Features] = None			,			lowerCAmelCase__			:str = None			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:Optional[int] = None			,			**lowerCAmelCase__			:Optional[int]			,			)		->			Tuple:
										super().__init__(
										    lowerCAmelCase__			,			split=lowerCAmelCase__			,			features=lowerCAmelCase__			,			cache_dir=lowerCAmelCase__			,			keep_in_memory=lowerCAmelCase__			,			streaming=lowerCAmelCase__			,			num_proc=lowerCAmelCase__			,			**lowerCAmelCase__			,			)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		path_or_paths if isinstance(lowerCAmelCase__			,			lowerCAmelCase__	) else {self.split: path_or_paths}
										__SCREAMING_SNAKE_CASE						:     int					=		Text(
										    cache_dir=lowerCAmelCase__			,			data_files=lowerCAmelCase__			,			features=lowerCAmelCase__			,			**lowerCAmelCase__			,			)



				def 		__magic_name__(       self			:Dict	)		->			Tuple:
										# Build iterable dataset
										if self.streaming:
																__SCREAMING_SNAKE_CASE						:     int					=		self.builder.as_streaming_dataset(split=self.split	)
										# Build regular (map-style) dataset
										else:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		None
																__SCREAMING_SNAKE_CASE						:     str					=		None
																__SCREAMING_SNAKE_CASE						:     Dict					=		None
																__SCREAMING_SNAKE_CASE						:     Tuple					=		None

																self.builder.download_and_prepare(
																    download_config=lowerCAmelCase__			,			download_mode=lowerCAmelCase__			,			verification_mode=lowerCAmelCase__			,			base_path=lowerCAmelCase__			,			num_proc=self.num_proc			,			)
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.builder.as_dataset(
																    split=self.split			,			verification_mode=lowerCAmelCase__			,			in_memory=self.keep_in_memory	)
										return dataset
