

"""simple docstring"""


import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=7 ,lowerCamelCase_=3 ,lowerCamelCase_=1_8 ,lowerCamelCase_=3_0 ,lowerCamelCase_=4_0_0 ,lowerCamelCase_=True ,lowerCamelCase_=None ,lowerCamelCase_=True ,lowerCamelCase_=None ,lowerCamelCase_=True ,lowerCamelCase_=[0.5, 0.5, 0.5] ,lowerCamelCase_=[0.5, 0.5, 0.5] ,lowerCamelCase_=False ,)    ->      Optional[Any]:
          A				  =		size if size is not None else {'height': 2_0, 'width': 2_0}
          A				  =		crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
          A				  =		parent
          A				  =		batch_size
          A				  =		num_channels
          A				  =		image_size
          A				  =		min_resolution
          A				  =		max_resolution
          A				  =		do_resize
          A				  =		size
          A				  =		do_center_crop
          A				  =		crop_size
          A				  =		do_normalize
          A				  =		image_mean
          A				  =		image_std
          A				  =		do_reduce_labels




    def UpperCamelCase__		( self    )    ->      int:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_reduce_labels": self.do_reduce_labels,
          }




def   _A					(	):



      """simple docstring"""






      A				  =		load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,   split="""test"""				)

      A				  =		Image.open(dataset[0]["""file"""]				)
      A				  =		Image.open(dataset[1]["""file"""]				)

      return image, map



def   _A					(	):



      """simple docstring"""






      A				  =		load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,   split="""test"""				)

      A				  =		Image.open(ds[0]["""file"""]				)
      A				  =		Image.open(ds[1]["""file"""]				)
      A				  =		Image.open(ds[2]["""file"""]				)
      A				  =		Image.open(ds[3]["""file"""]				)

      return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE_     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   BeitImageProcessor if is_vision_available() else None




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		BeitImageProcessingTester(self    )




    @property
    def UpperCamelCase__		( self    )    ->      List[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(__a ,"""do_resize"""    )    )
          self.assertTrue(hasattr(__a ,"""size"""    )    )
          self.assertTrue(hasattr(__a ,"""do_center_crop"""    )    )
          self.assertTrue(hasattr(__a ,"""center_crop"""    )    )
          self.assertTrue(hasattr(__a ,"""do_normalize"""    )    )
          self.assertTrue(hasattr(__a ,"""image_mean"""    )    )
          self.assertTrue(hasattr(__a ,"""image_std"""    )    )




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		self.image_processing_class.from_dict(self.image_processor_dict    )
          self.assertEqual(image_processor.size ,{"""height""": 2_0, """width""": 2_0}    )
          self.assertEqual(image_processor.crop_size ,{"""height""": 1_8, """width""": 1_8}    )
          self.assertEqual(image_processor.do_reduce_labels ,__a    )

          A				  =		self.image_processing_class.from_dict(
              self.image_processor_dict ,size=4_2 ,crop_size=8_4 ,reduce_labels=__a    )
          self.assertEqual(image_processor.size ,{"""height""": 4_2, """width""": 4_2}    )
          self.assertEqual(image_processor.crop_size ,{"""height""": 8_4, """width""": 8_4}    )
          self.assertEqual(image_processor.do_reduce_labels ,__a    )




    def UpperCamelCase__		( self    )    ->      Any:
          pass




    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          A				  =		prepare_image_inputs(self.image_processor_tester ,equal_resolution=__a    )
          for image in image_inputs:
                self.assertIsInstance(__a ,Image.Image    )

          # Test not batched input
          A				  =		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
          A				  =		image_processing(__a ,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 UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          A				  =		prepare_image_inputs(self.image_processor_tester ,equal_resolution=__a ,numpify=__a    )
          for image in image_inputs:
                self.assertIsInstance(__a ,np.ndarray    )

          # Test not batched input
          A				  =		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
          A				  =		image_processing(__a ,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 UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          A				  =		prepare_image_inputs(self.image_processor_tester ,equal_resolution=__a ,torchify=__a    )
          for image in image_inputs:
                self.assertIsInstance(__a ,torch.Tensor    )

          # Test not batched input
          A				  =		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
          A				  =		image_processing(__a ,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 UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          A				  =		prepare_image_inputs(self.image_processor_tester ,equal_resolution=__a ,torchify=__a    )
          A				  =		[]
          for image in image_inputs:
                self.assertIsInstance(__a ,torch.Tensor    )
                maps.append(torch.zeros(image.shape[-2:]    ).long()    )

          # Test not batched input
          A				  =		image_processing(image_inputs[0] ,maps[0] ,return_tensors="""pt"""    )
          self.assertEqual(
              encoding["""pixel_values"""].shape ,(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              ) ,)
          self.assertEqual(
              encoding["""labels"""].shape ,(
                  1,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              ) ,)
          self.assertEqual(encoding["""labels"""].dtype ,torch.long    )
          self.assertTrue(encoding["""labels"""].min().item() >= 0    )
          self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5    )

          # Test batched
          A				  =		image_processing(__a ,__a ,return_tensors="""pt"""    )
          self.assertEqual(
              encoding["""pixel_values"""].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"""],
              ) ,)
          self.assertEqual(
              encoding["""labels"""].shape ,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              ) ,)
          self.assertEqual(encoding["""labels"""].dtype ,torch.long    )
          self.assertTrue(encoding["""labels"""].min().item() >= 0    )
          self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5    )

          # Test not batched input (PIL images)
          A				  =		prepare_semantic_single_inputs()

          A				  =		image_processing(__a ,__a ,return_tensors="""pt"""    )
          self.assertEqual(
              encoding["""pixel_values"""].shape ,(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              ) ,)
          self.assertEqual(
              encoding["""labels"""].shape ,(
                  1,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              ) ,)
          self.assertEqual(encoding["""labels"""].dtype ,torch.long    )
          self.assertTrue(encoding["""labels"""].min().item() >= 0    )
          self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5    )

          # Test batched input (PIL images)
          A				  =		prepare_semantic_batch_inputs()

          A				  =		image_processing(__a ,__a ,return_tensors="""pt"""    )
          self.assertEqual(
              encoding["""pixel_values"""].shape ,(
                  2,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              ) ,)
          self.assertEqual(
              encoding["""labels"""].shape ,(
                  2,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              ) ,)
          self.assertEqual(encoding["""labels"""].dtype ,torch.long    )
          self.assertTrue(encoding["""labels"""].min().item() >= 0    )
          self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5    )




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )

          # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
          A				  =		prepare_semantic_single_inputs()
          A				  =		image_processing(__a ,__a ,return_tensors="""pt"""    )
          self.assertTrue(encoding["""labels"""].min().item() >= 0    )
          self.assertTrue(encoding["""labels"""].max().item() <= 1_5_0    )

          A				  =		True
          A				  =		image_processing(__a ,__a ,return_tensors="""pt"""    )
          self.assertTrue(encoding["""labels"""].min().item() >= 0    )
          self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5    )









'''simple docstring'''





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
    import jax
    import jax.numpy as jnp



@slow
@require_flax
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							int      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       return f'''gaussian_noise_s={seed}_shape={"_".join([str(__a  ) for s in shape]  )}.npy'''
 def A_   (   self						:							Any  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
 def A_   (   self						:							List[Any]      ,     __a						:							Union[str, Any]=0      ,     __a						:							List[str]=(4, 4, 64, 64)      ,     __a						:							Optional[Any]=False  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   List[Any]													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return image
 def A_   (   self						:							Any      ,     __a						:							Any=False      ,     __a						:							Dict="CompVis/stable-diffusion-v1-4"  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   int													=    'bf16' if fpaa else None

       __snake_case							,     __snake_case :   Union[str, Any]													=    FlaxUNetaDConditionModel.from_pretrained(
           __a      ,     subfolder='unet'      ,     dtype=__a      ,     revision=__a  )
       return model, params
 def A_   (   self						:							Any      ,     __a						:							Dict=0      ,     __a						:							Dict=(4, 77, 768)      ,     __a						:							List[str]=False  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   Any													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return hidden_states
 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
         [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
         [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
         [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
         # fmt: on
     ]  )
 def A_   (   self						:							List[Any]      ,     __a						:							List[str]      ,     __a						:							Union[str, Any]      ,     __a						:							Any  )      ->   Dict:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,     fpaa=__a  )
       __snake_case :   Tuple													=    self.get_latents(__a      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_encoder_hidden_states(__a      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   str													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )





 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
         [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
         [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
         [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
         # fmt: on
     ]  )
 def A_   (   self						:							str      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   int													=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_latents(__a      ,     shape=(4, 4, 96, 96)      ,     fpaa=__a  )
       __snake_case :   Optional[Any]													=    self.get_encoder_hidden_states(__a      ,     shape=(4, 77, 1024)      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   int													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )



