



"""simple docstring"""







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      UpperCAmelCase__		(      unittest.TestCase     ):



		def __init__( self  :    Optional[int]      ,							snake_case  :    List[Any]      ,							snake_case  :    bool = True      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    int = 32      ,							snake_case  :    bool = True      ,							snake_case  :    Union[int, float] = 1 / 255      ,							snake_case  :    bool = True      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]      ,							snake_case  :    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]      ,							snake_case  :    bool = True      ,							snake_case  :    Union[str, Any]=7      ,							snake_case  :    Tuple=30      ,							snake_case  :    Union[str, Any]=400      ,							snake_case  :    Optional[Any]=3      ,							)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					parent
									A         =					do_resize
									A         =					size if size is not None else {'shortest_edge': 288}
									A         =					size_divisor
									A         =					do_rescale
									A         =					rescale_factor
									A         =					do_normalize
									A         =					do_center_crop
									A         =					image_mean
									A         =					image_std
									A         =					do_pad
									A         =					batch_size
									A         =					num_channels
									A         =					min_resolution
									A         =					max_resolution



		def 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

									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 					A_		( self  :    int      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]=False				)			->						List[Any]:






									'''simple docstring'''

									if not batched:
																A         =					self.size['shortest_edge']
																A         =					image_inputs[0]
																if isinstance(snake_case      ,							Image.Image				):
																							A      ,  A         =					image.size
																else:
																							A      ,  A         =					image.shape[1], image.shape[2]
																A         =					size / min(snake_case      ,							snake_case				)
																if h < w:
																							A      ,  A         =					size, scale * w
																else:
																							A      ,  A         =					scale * h, size

																A         =					int((1_333 / 800) * size				)
																if max(snake_case      ,							snake_case				) > max_size:
																							A         =					max_size / max(snake_case      ,							snake_case				)
																							A         =					newh * scale
																							A         =					neww * scale

																A      ,  A         =					int(newh + 0.5				), int(neww + 0.5				)
																A      ,  A         =					(
																    newh // self.size_divisor * self.size_divisor,
																    neww // self.size_divisor * self.size_divisor,
																)

									else:
																A         =					[]
																for image in image_inputs:
																							A      ,  A         =					self.get_expected_values([image]				)
																							expected_values.append((expected_height, expected_width)				)
																A         =					max(snake_case      ,							key=lambda snake_case				: item[0]				)[0]
																A         =					max(snake_case      ,							key=lambda snake_case				: item[1]				)[1]

									return expected_height, expected_width






@require_torch
@require_vision
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Tuple 							=     BridgeTowerImageProcessor if is_vision_available() else None



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									A         =					BridgeTowerImageProcessingTester(self				)



		@property
		def 					A_		( self  :    Any				)			->						List[str]:






									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()



		def 					A_		( self  :    str				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									self.assertTrue(hasattr(snake_case      ,							'image_mean'				)				)
									self.assertTrue(hasattr(snake_case      ,							'image_std'				)				)
									self.assertTrue(hasattr(snake_case      ,							'do_normalize'				)				)
									self.assertTrue(hasattr(snake_case      ,							'do_resize'				)				)
									self.assertTrue(hasattr(snake_case      ,							'size'				)				)
									self.assertTrue(hasattr(snake_case      ,							'size_divisor'				)				)



		def 					A_		( self  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									pass



		def 					A_		( self  :    Tuple				)			->						Dict:






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									# create random PIL images
									A         =					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
									A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)
									self.assertEqual(
									    encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

									# Test batched
									A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)
									self.assertEqual(
									    encoded_images.shape      ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,							)



		def 					A_		( self  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									# create random numpy tensors
									A         =					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
									A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)
									self.assertEqual(
									    encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

									# Test batched
									A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)
									self.assertEqual(
									    encoded_images.shape      ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,							)



		def 					A_		( self  :    Union[str, Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									# create random PyTorch tensors
									A         =					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
									A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)
									self.assertEqual(
									    encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

									# Test batched
									A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)
									self.assertEqual(
									    encoded_images.shape      ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,							)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =			{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			['PLBartTokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PLBartForCausalLM',
							    'PLBartForConditionalGeneration',
							    'PLBartForSequenceClassification',
							    'PLBartModel',
							    'PLBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



