
import unittest

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 PoolFormerImageProcessor





class     lowerCamelCase      (       unittest.TestCase					):







	def __init__(     self      :List[Any]   ,     lowercase      :Union[str, Any]   ,     lowercase      :List[str]=7   ,     lowercase      :Dict=3   ,     lowercase      :Union[str, Any]=3_0   ,     lowercase      :str=4_0_0   ,     lowercase      :List[Any]=True   ,     lowercase      :Dict=None   ,     lowercase      :Tuple=0.9   ,     lowercase      :Any=None   ,     lowercase      :str=True   ,     lowercase      :Dict=[0.5, 0.5, 0.5]   ,     lowercase      :List[str]=[0.5, 0.5, 0.5]   ,     )		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= size if size is not None else {'''shortest_edge''': 3_0}
				SCREAMING_SNAKE_CASE     						= crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
				SCREAMING_SNAKE_CASE     						= parent
				SCREAMING_SNAKE_CASE     						= batch_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= min_resolution
				SCREAMING_SNAKE_CASE     						= max_resolution
				SCREAMING_SNAKE_CASE     						= do_resize_and_center_crop
				SCREAMING_SNAKE_CASE     						= size
				SCREAMING_SNAKE_CASE     						= crop_pct
				SCREAMING_SNAKE_CASE     						= crop_size
				SCREAMING_SNAKE_CASE     						= do_normalize
				SCREAMING_SNAKE_CASE     						= image_mean
				SCREAMING_SNAKE_CASE     						= image_std







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







				"""simple docstring"""






				return {
				    "size": self.size,
				    "do_resize_and_center_crop": self.do_resize_and_center_crop,
				    "crop_pct": self.crop_pct,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}





@require_torch
@require_vision
class     lowerCamelCase      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					Tuple    		=	PoolFormerImageProcessor if is_vision_available() else None







	def  snake_case__     (     self      :List[str]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= PoolFormerImageProcessingTester(self			)







	@property
	def  snake_case__     (     self      :Any			)		->      Any:







				"""simple docstring"""






				return self.image_processor_tester.prepare_image_processor_dict()







	def  snake_case__     (     self      :str			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.image_processing_class(**self.image_processor_dict			)
				self.assertTrue(hasattr(lowercase   ,     '''do_resize_and_center_crop'''			)			)
				self.assertTrue(hasattr(lowercase   ,     '''size'''			)			)
				self.assertTrue(hasattr(lowercase   ,     '''crop_pct'''			)			)
				self.assertTrue(hasattr(lowercase   ,     '''do_normalize'''			)			)
				self.assertTrue(hasattr(lowercase   ,     '''image_mean'''			)			)
				self.assertTrue(hasattr(lowercase   ,     '''image_std'''			)			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.image_processing_class.from_dict(self.image_processor_dict			)
				self.assertEqual(image_processor.size   ,     {'''shortest_edge''': 3_0}			)
				self.assertEqual(image_processor.crop_size   ,     {'''height''': 3_0, '''width''': 3_0}			)

				SCREAMING_SNAKE_CASE     						= self.image_processing_class.from_dict(self.image_processor_dict   ,     size=4_2   ,     crop_size=8_4			)
				self.assertEqual(image_processor.size   ,     {'''shortest_edge''': 4_2}			)
				self.assertEqual(image_processor.crop_size   ,     {'''height''': 8_4, '''width''': 8_4}			)







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







				"""simple docstring"""






				pass







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







				"""simple docstring"""






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

				# Test not batched input
				SCREAMING_SNAKE_CASE     						= 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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				SCREAMING_SNAKE_CASE     						= image_processing(lowercase   ,     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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.image_processing_class(**self.image_processor_dict			)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE     						= prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=lowercase   ,     numpify=lowercase			)
				for image in image_inputs:
							self.assertIsInstance(lowercase   ,     np.ndarray			)

				# Test not batched input
				SCREAMING_SNAKE_CASE     						= 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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				SCREAMING_SNAKE_CASE     						= image_processing(lowercase   ,     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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )







	def  snake_case__     (     self      :Any			)		->      Any:







				"""simple docstring"""






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

				# Test not batched input
				SCREAMING_SNAKE_CASE     						= 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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				SCREAMING_SNAKE_CASE     						= image_processing(lowercase   ,     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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase					   =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BridgeTowerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BridgeTowerForContrastiveLearning',
							    'BridgeTowerForImageAndTextRetrieval',
							    'BridgeTowerForMaskedLM',
							    'BridgeTowerModel',
							    'BridgeTowerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_bridgetower import (
							    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BridgeTowerConfig,
							    BridgeTowerTextConfig,
							    BridgeTowerVisionConfig,
							)
							from .processing_bridgetower import BridgeTowerProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_bridgetower import BridgeTowerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bridgetower import (
														    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BridgeTowerForContrastiveLearning,
														    BridgeTowerForImageAndTextRetrieval,
														    BridgeTowerForMaskedLM,
														    BridgeTowerModel,
														    BridgeTowerPreTrainedModel,
														)


else:
							import sys

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