






"""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      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 32     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = 1 / 255     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = [0.48145466, 0.4578275, 0.40821073]     ,     lowerCAmelCase_ = [0.26862954, 0.26130258, 0.27577711]     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_=7     ,     lowerCAmelCase_=30     ,     lowerCAmelCase_=400     ,     lowerCAmelCase_=3     ,     ):
		A_		:						Optional[Any]	    =   parent
		A_		:						Union[str, Any]	    =   do_resize
		A_		:						Any	    =   size if size is not None else {"""shortest_edge""": 288}
		A_		:						Tuple	    =   size_divisor
		A_		:						Tuple	    =   do_rescale
		A_		:						Optional[int]	    =   rescale_factor
		A_		:						Tuple	    =   do_normalize
		A_		:						Dict	    =   do_center_crop
		A_		:						Any	    =   image_mean
		A_		:						Optional[int]	    =   image_std
		A_		:						str	    =   do_pad
		A_		:						str	    =   batch_size
		A_		:						List[Any]	    =   num_channels
		A_		:						int	    =   min_resolution
		A_		:						Union[str, Any]	    =   max_resolution





	def    lowerCamelCase(self							):
		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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=False							):
		if not batched:
			A_		:						Dict	    =   self.size["""shortest_edge"""]
			A_		:						Union[str, Any]	    =   image_inputs[0]
			if isinstance(lowerCAmelCase_     ,     Image.Image							):
				A_    ,     A_		:						Union[str, Any]	    =   image.size
			else:
				A_    ,     A_		:						Any	    =   image.shape[1], image.shape[2]
			A_		:						Any	    =   size / min(lowerCAmelCase_     ,     lowerCAmelCase_							)
			if h < w:
				A_    ,     A_		:						List[Any]	    =   size, scale * w
			else:
				A_    ,     A_		:						Union[str, Any]	    =   scale * h, size

			A_		:						Optional[int]	    =   int((1333 / 800) * size							)
			if max(lowerCAmelCase_     ,     lowerCAmelCase_							) > max_size:
				A_		:						int	    =   max_size / max(lowerCAmelCase_     ,     lowerCAmelCase_							)
				A_		:						Optional[int]	    =   newh * scale
				A_		:						Optional[Any]	    =   neww * scale

			A_    ,     A_		:						List[str]	    =   int(newh + 0.5							), int(neww + 0.5							)
			A_    ,     A_		:						Tuple	    =   (
			    newh // self.size_divisor * self.size_divisor,
			    neww // self.size_divisor * self.size_divisor,
			)

		else:
			A_		:						List[str]	    =   []
			for image in image_inputs:
				A_    ,     A_		:						Tuple	    =   self.get_expected_values([image]							)
				expected_values.append((expected_height, expected_width)							)
			A_		:						int	    =   max(lowerCAmelCase_     ,     key=lambda lowerCAmelCase_							: item[0]							)[0]
			A_		:						Dict	    =   max(lowerCAmelCase_     ,     key=lambda lowerCAmelCase_							: item[1]							)[1]

		return expected_height, expected_width






@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				int    =				BridgeTowerImageProcessor if is_vision_available() else None





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   BridgeTowerImageProcessingTester(self							)





	@property
	def    lowerCamelCase(self							):
		return self.image_processor_tester.prepare_image_processor_dict()





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   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    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		# Initialize image processor
		A_		:						int	    =   self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		A_		:						Optional[Any]	    =   prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowerCAmelCase_							)
		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_     ,     Image.Image							)

		# Test not batched input
		A_		:						Tuple	    =   image_processing(image_inputs[0]     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						Tuple	    =   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
		A_		:						Tuple	    =   image_processing(lowerCAmelCase_     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						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    lowerCamelCase(self							):
		# Initialize image processor
		A_		:						Optional[int]	    =   self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		A_		:						Tuple	    =   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
		A_		:						Dict	    =   image_processing(image_inputs[0]     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						List[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
		A_		:						str	    =   image_processing(lowerCAmelCase_     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						Optional[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,
		    )     ,     )





	def    lowerCamelCase(self							):
		# Initialize image processor
		A_		:						Optional[int]	    =   self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		A_		:						List[str]	    =   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
		A_		:						Optional[int]	    =   image_processing(image_inputs[0]     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						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
		A_		:						Optional[Any]	    =   image_processing(lowerCAmelCase_     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						Optional[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,
		    )     ,     )









"""simple docstring"""
import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:



							class      SCREAMING_SNAKE_CASE  :





								"""simple docstring"""




								@staticmethod
								def    lowerCamelCase(*lowerCAmelCase_     ,     **lowerCAmelCase_							):
									pass



@is_pipeline_test
@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[Any]    =				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Union[str, Any]	    =   [
		    {
		        """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							),
		        """question""": """How many cats are there?""",
		    },
		    {
		        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
		        """question""": """How many cats are there?""",
		    },
		]
		return vqa_pipeline, examples





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   vqa_pipeline(lowerCAmelCase_     ,     top_k=1							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		    ]     ,     )





	@require_torch
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Any	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						Any	    =   """How many cats are there?"""

		A_		:						Optional[int]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question="""How many cats are there?"""     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)





	@slow
	@require_torch
	def    lowerCamelCase(self							):
		A_		:						int	    =   pipeline("""visual-question-answering"""     ,     model="""dandelin/vilt-b32-finetuned-vqa"""							)
		A_		:						Tuple	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						int	    =   """How many cats are there?"""

		A_		:						List[Any]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question=lowerCAmelCase_     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Tuple	    =   vqa_pipeline(
		    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2     ,     )





	@require_tf
	@unittest.skip("""Visual question answering not implemented in TF"""							)
	def    lowerCamelCase(self							):
		pass

