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     snake_case_			(						unittest.TestCase			):






   def __init__( self							:	int      ,		lowercase_							:	Any      ,		lowercase_							:	Any=7      ,		lowercase_							:	Any=3      ,		lowercase_							:	Optional[Any]=30      ,		lowercase_							:	int=4_00      ,		lowercase_							:	List[Any]=True      ,		lowercase_							:	List[Any]=None      ,		lowercase_							:	Tuple=True      ,		lowercase_							:	int=None      ,		lowercase_							:	int=None      ,		lowercase_							:	Dict=True      ,		lowercase_							:	Tuple=1 / 2_55      ,		lowercase_							:	List[Any]=True      ,		lowercase_							:	Any=FLAVA_IMAGE_MEAN      ,		lowercase_							:	List[str]=FLAVA_IMAGE_STD      ,		lowercase_							:	Any=14      ,		lowercase_							:	str=75      ,		lowercase_							:	Union[str, Any]=None      ,		lowercase_							:	Optional[int]=16      ,		lowercase_							:	List[Any]=0.3      ,		lowercase_							:	str=None      ,		lowercase_							:	str=True      ,		lowercase_							:	Tuple=None      ,		lowercase_							:	int=None      ,		lowercase_							:	Optional[Any]=True      ,		lowercase_							:	Optional[Any]=None      ,		lowercase_							:	List[Any]=True      ,		lowercase_							:	str=True      ,		lowercase_							:	Tuple=FLAVA_CODEBOOK_MEAN      ,		lowercase_							:	List[str]=FLAVA_CODEBOOK_STD      ,		)   ->		Optional[int]:
       lowercase__					:  str          =							size if size is not None else {"height": 2_24, "width": 2_24}
       lowercase__					:  List[Any]          =							crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       lowercase__					:  Optional[int]          =							codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
       lowercase__					:  Optional[Any]          =							codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

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

       lowercase__					:  int          =							input_size_patches
       lowercase__					:  str          =							total_mask_patches
       lowercase__					:  List[Any]          =							mask_group_max_patches
       lowercase__					:  int          =							mask_group_min_patches
       lowercase__					:  Union[str, Any]          =							mask_group_min_aspect_ratio
       lowercase__					:  Union[str, Any]          =							mask_group_max_aspect_ratio

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






   def  __UpperCamelCase	( self							:	Dict    )   ->		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,
           "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  __UpperCamelCase	( self							:	Dict    )   ->		Tuple:
       return (self.size["height"], self.size["width"])






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		Tuple:
       return (
           (self.input_size_patches, self.input_size_patches)
           if not isinstance(self.input_size_patches      ,		lowercase_    )
           else self.input_size_patches
       )







   def  __UpperCamelCase	( self							:	Tuple    )   ->		str:
       return (self.codebook_size["height"], self.codebook_size["width"])



@require_torch
@require_vision
class     snake_case_			(						__A    ,unittest.TestCase			):
   __A      :						Any   =							FlavaImageProcessor if is_vision_available() else None
   __A      :						Any   =							None






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		List[str]:
       lowercase__					:  Dict          =							FlavaImageProcessingTester(self    )






   @property
   def  __UpperCamelCase	( self							:	List[str]    )   ->		int:
       return self.image_processor_tester.prepare_image_processor_dict()






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Any:
       lowercase__					:  Union[str, Any]          =							self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(lowercase_      ,		"image_mean"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"image_std"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"do_normalize"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"do_resize"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"resample"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"crop_size"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"do_center_crop"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"do_rescale"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"rescale_factor"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"masking_generator"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_do_resize"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_size"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_resample"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_do_center_crop"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_crop_size"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_do_map_pixels"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_do_normalize"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_image_mean"    )    )
       self.assertTrue(hasattr(lowercase_      ,		"codebook_image_std"    )    )






   def  __UpperCamelCase	( self							:	Dict    )   ->		Union[str, Any]:
       lowercase__					:  List[str]          =							self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size      ,		{"height": 2_24, "width": 2_24}    )
       self.assertEqual(image_processor.crop_size      ,		{"height": 2_24, "width": 2_24}    )
       self.assertEqual(image_processor.codebook_size      ,		{"height": 1_12, "width": 1_12}    )
       self.assertEqual(image_processor.codebook_crop_size      ,		{"height": 1_12, "width": 1_12}    )

       lowercase__					:  List[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  __UpperCamelCase	( self							:	str    )   ->		Optional[int]:
       pass






   def  __UpperCamelCase	( self							:	List[str]    )   ->		Tuple:
       # Initialize image_processing
       lowercase__					:  List[Any]          =							self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       lowercase__					:  str          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase_    )
       for image in image_inputs:
           self.assertIsInstance(lowercase_      ,		PIL.Image.Image    )

       # Test not batched input
       lowercase__					:  Optional[int]          =							image_processing(image_inputs[0]      ,		return_tensors="pt"    )

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

       lowercase__   ,  lowercase__					:  Optional[int]          =							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
       lowercase__					:  Optional[Any]          =							image_processing(lowercase_      ,		return_tensors="pt"    )
       lowercase__   ,  lowercase__					:  List[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  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	int    )   ->		str:
       # Initialize image_processing
       lowercase__					:  Any          =							self.image_processing_class(**self.image_processor_dict    )
       # create random tensors
       lowercase__					:  Dict          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase_      ,		**lowercase_    )
       for image in image_inputs:
           self.assertIsInstance(lowercase_      ,		lowercase_    )

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

       lowercase__   ,  lowercase__					:  Optional[Any]          =							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)      ,		)

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

       lowercase__   ,  lowercase__					:  Optional[int]          =							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,
           )      ,		)

       lowercase__   ,  lowercase__					:  int          =							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
       lowercase__					:  Any          =							image_processing(lowercase_      ,		return_tensors="pt"    ).pixel_values

       lowercase__   ,  lowercase__					:  Optional[int]          =							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
       lowercase__					:  int          =							image_processing(lowercase_      ,		return_image_mask=lowercase_      ,		return_tensors="pt"    )

       lowercase__   ,  lowercase__					:  List[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,
           )      ,		)

       lowercase__   ,  lowercase__					:  Union[str, 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  __UpperCamelCase	( self							:	Any    )   ->		Tuple:
       self._test_call_framework(np.ndarray      ,		prepare_kwargs={"numpify": True}    )






   def  __UpperCamelCase	( self							:	str    )   ->		List[Any]:
       self._test_call_framework(torch.Tensor      ,		prepare_kwargs={"torchify": True}    )






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Tuple:
       # Initialize image_processing
       random.seed(12_34    )
       lowercase__					:  List[Any]          =							self.image_processing_class(**self.image_processor_dict    )
       lowercase__					:  Optional[int]          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase_      ,		torchify=lowercase_    )

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







   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		List[str]:
       # Initialize image_processing
       lowercase__					:  int          =							self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       lowercase__					:  Tuple          =							prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase_    )
       for image in image_inputs:
           self.assertIsInstance(lowercase_      ,		PIL.Image.Image    )

       # Test not batched input
       lowercase__					:  int          =							image_processing(image_inputs[0]      ,		return_codebook_pixels=lowercase_      ,		return_tensors="pt"    )
       lowercase__   ,  lowercase__					:  List[str]          =							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
       lowercase__					:  Any          =							image_processing(lowercase_      ,		return_codebook_pixels=lowercase_      ,		return_tensors="pt"    )
       lowercase__   ,  lowercase__					:  Tuple          =							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,
           )      ,		)




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
      from PIL import Image



class     snake_case_			(						unittest.TestCase    ,__A			):






   def  __UpperCamelCase	( self							:	int    )   ->		Union[str, Any]:
       lowercase__					:  Any          =							load_tool("image-segmentation"    )
       self.tool.setup()
       lowercase__					:  Optional[int]          =							load_tool("image-segmentation"      ,		remote=lowercase_    )






   def  __UpperCamelCase	( self							:	Any    )   ->		Tuple:
       lowercase__					:  Any          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Tuple          =							self.tool(lowercase_      ,		"cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )






   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		Dict:
       lowercase__					:  List[Any]          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Optional[int]          =							self.remote_tool(lowercase_      ,		"cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Dict:
       lowercase__					:  Optional[Any]          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  int          =							self.tool(image=lowercase_      ,		label="cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )







   def  __UpperCamelCase	( self							:	Dict    )   ->		List[Any]:
       lowercase__					:  Tuple          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Union[str, Any]          =							self.remote_tool(image=lowercase_      ,		label="cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )



