



'''simple docstring'''
import random
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():
					import PIL

					from transformers import FlavaImageProcessor
					from transformers.image_utils import PILImageResampling
					from transformers.models.flava.image_processing_flava import (
					    FLAVA_CODEBOOK_MEAN,
					    FLAVA_CODEBOOK_STD,
					    FLAVA_IMAGE_MEAN,
					    FLAVA_IMAGE_STD,
					)
else:
					_UpperCamelCase											=			_UpperCamelCase											=			_UpperCamelCase											=			_UpperCamelCase											=			None



class _A  (    unittest.TestCase    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=7 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=30 ,       __UpperCAmelCase=400 ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       __UpperCAmelCase=None ,       __UpperCAmelCase=True ,       __UpperCAmelCase=1 / 255 ,       __UpperCAmelCase=True ,       __UpperCAmelCase=FLAVA_IMAGE_MEAN ,       __UpperCAmelCase=FLAVA_IMAGE_STD ,       __UpperCAmelCase=14 ,       __UpperCAmelCase=75 ,       __UpperCAmelCase=None ,       __UpperCAmelCase=16 ,       __UpperCAmelCase=0.3 ,       __UpperCAmelCase=None ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       __UpperCAmelCase=None ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       __UpperCAmelCase=True ,       __UpperCAmelCase=True ,       __UpperCAmelCase=FLAVA_CODEBOOK_MEAN ,       __UpperCAmelCase=FLAVA_CODEBOOK_STD ,       )       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     size if size is not None else {"""height""": 224, """width""": 224}
					__UpperCAmelCase   :      Optional[Any]										=     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
					__UpperCAmelCase   :      Any										=     codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
					__UpperCAmelCase   :      Tuple										=     codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}

					__UpperCAmelCase   :      str										=     parent
					__UpperCAmelCase   :      Optional[int]										=     batch_size
					__UpperCAmelCase   :      Optional[int]										=     num_channels
					__UpperCAmelCase   :      List[str]										=     do_resize
					__UpperCAmelCase   :      Optional[Any]										=     do_rescale
					__UpperCAmelCase   :      List[Any]										=     rescale_factor
					__UpperCAmelCase   :      List[str]										=     min_resolution
					__UpperCAmelCase   :      Optional[Any]										=     max_resolution
					__UpperCAmelCase   :      Tuple										=     size
					__UpperCAmelCase   :      int										=     resample if resample is not None else PILImageResampling.BICUBIC
					__UpperCAmelCase   :      Optional[int]										=     do_normalize
					__UpperCAmelCase   :      int										=     image_mean
					__UpperCAmelCase   :      Optional[Any]										=     image_std
					__UpperCAmelCase   :      Union[str, Any]										=     do_center_crop
					__UpperCAmelCase   :      Optional[Any]										=     crop_size

					__UpperCAmelCase   :      Union[str, Any]										=     input_size_patches
					__UpperCAmelCase   :      Optional[Any]										=     total_mask_patches
					__UpperCAmelCase   :      str										=     mask_group_max_patches
					__UpperCAmelCase   :      Any										=     mask_group_min_patches
					__UpperCAmelCase   :      Union[str, Any]										=     mask_group_min_aspect_ratio
					__UpperCAmelCase   :      int										=     mask_group_max_aspect_ratio

					__UpperCAmelCase   :      List[Any]										=     codebook_do_resize
					__UpperCAmelCase   :      Dict										=     codebook_size
					__UpperCAmelCase   :      Tuple										=     codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
					__UpperCAmelCase   :      Tuple										=     codebook_do_center_crop
					__UpperCAmelCase   :      List[Any]										=     codebook_crop_size
					__UpperCAmelCase   :      Optional[Any]										=     codebook_do_map_pixels
					__UpperCAmelCase   :      List[str]										=     codebook_do_normalize
					__UpperCAmelCase   :      Dict										=     codebook_image_mean
					__UpperCAmelCase   :      int										=     codebook_image_std





		def 							__A       (     self				)       ->     str:

					'''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,
					    "resample": self.resample,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "input_size_patches": self.input_size_patches,
					    "total_mask_patches": self.total_mask_patches,
					    "mask_group_max_patches": self.mask_group_max_patches,
					    "mask_group_min_patches": self.mask_group_min_patches,
					    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
					    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
					    "codebook_do_resize": self.codebook_do_resize,
					    "codebook_size": self.codebook_size,
					    "codebook_resample": self.codebook_resample,
					    "codebook_do_center_crop": self.codebook_do_center_crop,
					    "codebook_crop_size": self.codebook_crop_size,
					    "codebook_do_map_pixels": self.codebook_do_map_pixels,
					    "codebook_do_normalize": self.codebook_do_normalize,
					    "codebook_image_mean": self.codebook_image_mean,
					    "codebook_image_std": self.codebook_image_std,
					}





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

					'''simple docstring'''
					return (self.size["height"], self.size["width"])





		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					return (
					    (self.input_size_patches, self.input_size_patches)
					    if not isinstance(self.input_size_patches ,       __UpperCAmelCase				)
					    else self.input_size_patches
					)





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					return (self.codebook_size["height"], self.codebook_size["width"])



@require_torch
@require_vision
class _A  (    __SCREAMING_SNAKE_CASE	,				unittest.TestCase    ):
		_SCREAMING_SNAKE_CASE					:    List[Any]						      =			FlavaImageProcessor if is_vision_available() else None
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			None





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     FlavaImageProcessingTester(self				)





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

					'''simple docstring'''
					return self.image_processor_tester.prepare_image_processor_dict()





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     self.image_processing_class(**self.image_processor_dict				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """image_mean"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """image_std"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """do_normalize"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """do_resize"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """resample"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """crop_size"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """do_center_crop"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """do_rescale"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """rescale_factor"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """masking_generator"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_do_resize"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_size"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_resample"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_do_center_crop"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_crop_size"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_do_map_pixels"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_do_normalize"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_image_mean"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """codebook_image_std"""				)				)





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.image_processing_class.from_dict(self.image_processor_dict				)
					self.assertEqual(image_processor.size ,       {"""height""": 224, """width""": 224}				)
					self.assertEqual(image_processor.crop_size ,       {"""height""": 224, """width""": 224}				)
					self.assertEqual(image_processor.codebook_size ,       {"""height""": 112, """width""": 112}				)
					self.assertEqual(image_processor.codebook_crop_size ,       {"""height""": 112, """width""": 112}				)

					__UpperCAmelCase   :      str										=     self.image_processing_class.from_dict(
					    self.image_processor_dict ,       size=42 ,       crop_size=84 ,       codebook_size=33 ,       codebook_crop_size=66				)
					self.assertEqual(image_processor.size ,       {"""height""": 42, """width""": 42}				)
					self.assertEqual(image_processor.crop_size ,       {"""height""": 84, """width""": 84}				)
					self.assertEqual(image_processor.codebook_size ,       {"""height""": 33, """width""": 33}				)
					self.assertEqual(image_processor.codebook_crop_size ,       {"""height""": 66, """width""": 66}				)





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					pass





		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase   :      List[str]										=     self.image_processing_class(**self.image_processor_dict				)
					# create random PIL images
					__UpperCAmelCase   :      Tuple										=     prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__UpperCAmelCase				)
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase ,       PIL.Image.Image				)

					# Test not batched input
					__UpperCAmelCase   :      Any										=     image_processing(image_inputs[0] ,       return_tensors="""pt"""				)

					# Test no bool masked pos
					self.assertFalse("""bool_masked_pos""" in encoded_images				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      str										=     self.image_processor_tester.get_expected_image_size()

					self.assertEqual(
					    encoded_images.pixel_values.shape ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,       )

					# Test batched
					__UpperCAmelCase   :      Optional[int]										=     image_processing(__UpperCAmelCase ,       return_tensors="""pt"""				)
					__UpperCAmelCase				,  __UpperCAmelCase   :      Optional[Any]										=     self.image_processor_tester.get_expected_image_size()

					# Test no bool masked pos
					self.assertFalse("""bool_masked_pos""" in encoded_images				)

					self.assertEqual(
					    encoded_images.pixel_values.shape ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ) ,       )





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     int:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase   :      Dict										=     self.image_processing_class(**self.image_processor_dict				)
					# create random tensors
					__UpperCAmelCase   :      Union[str, Any]										=     prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__UpperCAmelCase ,       **__UpperCAmelCase				)
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase ,       __UpperCAmelCase				)

					# Test not batched input
					__UpperCAmelCase   :      List[str]										=     image_processing(image_inputs[0] ,       return_tensors="""pt"""				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.pixel_values.shape ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,       )

					__UpperCAmelCase   :      List[Any]										=     image_processing(__UpperCAmelCase ,       return_image_mask=__UpperCAmelCase ,       return_tensors="""pt"""				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.pixel_values.shape ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ) ,       )

					__UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     self.image_processor_tester.get_expected_mask_size()
					self.assertEqual(
					    encoded_images.bool_masked_pos.shape ,       (
					        self.image_processor_tester.batch_size,
					        expected_height,
					        expected_width,
					    ) ,       )

					# Test batched
					__UpperCAmelCase   :      List[str]										=     image_processing(__UpperCAmelCase ,       return_tensors="""pt"""				).pixel_values

					__UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.shape ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ) ,       )

					# Test masking
					__UpperCAmelCase   :      Optional[Any]										=     image_processing(__UpperCAmelCase ,       return_image_mask=__UpperCAmelCase ,       return_tensors="""pt"""				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      Union[str, Any]										=     self.image_processor_tester.get_expected_image_size()
					self.assertEqual(
					    encoded_images.pixel_values.shape ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ) ,       )

					__UpperCAmelCase				,  __UpperCAmelCase   :      Optional[Any]										=     self.image_processor_tester.get_expected_mask_size()
					self.assertEqual(
					    encoded_images.bool_masked_pos.shape ,       (
					        self.image_processor_tester.batch_size,
					        expected_height,
					        expected_width,
					    ) ,       )





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					self._test_call_framework(np.ndarray ,       prepare_kwargs={"""numpify""": True}				)





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					self._test_call_framework(torch.Tensor ,       prepare_kwargs={"""torchify""": True}				)





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					# Initialize image_processing
					random.seed(1_234				)
					__UpperCAmelCase   :      List[Any]										=     self.image_processing_class(**self.image_processor_dict				)
					__UpperCAmelCase   :      List[Any]										=     prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__UpperCAmelCase ,       torchify=__UpperCAmelCase				)

					# Test not batched input
					__UpperCAmelCase   :      List[Any]										=     image_processing(image_inputs[0] ,       return_image_mask=__UpperCAmelCase ,       return_tensors="""pt"""				)
					self.assertEqual(encoded_images.bool_masked_pos.sum().item() ,       75				)





		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase   :      List[str]										=     self.image_processing_class(**self.image_processor_dict				)
					# create random PIL images
					__UpperCAmelCase   :      Optional[Any]										=     prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__UpperCAmelCase				)
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase ,       PIL.Image.Image				)

					# Test not batched input
					__UpperCAmelCase   :      List[Any]										=     image_processing(image_inputs[0] ,       return_codebook_pixels=__UpperCAmelCase ,       return_tensors="""pt"""				)
					__UpperCAmelCase				,  __UpperCAmelCase   :      Dict										=     self.image_processor_tester.get_expected_codebook_image_size()
					self.assertEqual(
					    encoded_images.codebook_pixel_values.shape ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,       )

					# Test batched
					__UpperCAmelCase   :      Tuple										=     image_processing(__UpperCAmelCase ,       return_codebook_pixels=__UpperCAmelCase ,       return_tensors="""pt"""				)
					__UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     self.image_processor_tester.get_expected_codebook_image_size()
					self.assertEqual(
					    encoded_images.codebook_pixel_values.shape ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ) ,       )









'''simple docstring'''
from __future__ import annotations

from typing import Any



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     None:  # noqa: A002

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     item
					__UpperCAmelCase   :      str										=     next



class _A  :





		def __init__(     self				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Node | None										=     None
					__UpperCAmelCase   :      Optional[int]										=     0





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     Node(__UpperCAmelCase ,       self.head				)
					self.size += 1





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					# Switched 'self.is_empty()' to 'self.head is None'
					# because mypy was considering the possibility that 'self.head'
					# can be None in below else part and giving error
					if self.head is None:
								return None
					else:
								__UpperCAmelCase   :      str										=     self.head.item
								__UpperCAmelCase   :      Tuple										=     self.head.next
								self.size -= 1
								return item





		def 							__A       (     self				)       ->     bool:

					'''simple docstring'''
					return self.head is None





		def __str__(     self				)       ->     str:

					'''simple docstring'''
					if self.is_empty():
								return ""
					else:
								__UpperCAmelCase   :      Union[str, Any]										=     self.head
								__UpperCAmelCase   :      List[Any]										=     """"""
								__UpperCAmelCase   :      list[str]										=     []
								while iterate:
											item_list.append(str(iterate.item				)				)
											__UpperCAmelCase   :      Any										=     iterate.next

								__UpperCAmelCase   :      Dict										=     """ --> """.join(__UpperCAmelCase				)

								return item_str





		def __len__(     self				)       ->     int:

					'''simple docstring'''
					return self.size




