
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Union[str, Any]	   =      UnCLIPImageVariationPipeline
 __lowerCAmelCase	:  List[Any]	   =      IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
 __lowerCAmelCase	:  Optional[int]	   =      IMAGE_VARIATION_BATCH_PARAMS

 __lowerCAmelCase	:  Tuple	   =      [
     'generator',
     'return_dict',
     'decoder_num_inference_steps',
     'super_res_num_inference_steps',
 ]
 __lowerCAmelCase	:  int	   =      False


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return 32


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return 32


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.time_input_dim


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.time_input_dim * 4


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return 1_00


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
   return tokenizer


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__	: List[str]     		=   CLIPTextConfig(
       bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=self.text_embedder_hidden_size       ,	projection_dim=self.text_embedder_hidden_size       ,	intermediate_size=37       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)
   return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__	: Any     		=   CLIPVisionConfig(
       hidden_size=self.text_embedder_hidden_size       ,	projection_dim=self.text_embedder_hidden_size       ,	num_hidden_layers=5       ,	num_attention_heads=4       ,	image_size=32       ,	intermediate_size=37       ,	patch_size=1       ,	)
   return CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)

   lowercase__	: List[Any]     		=   {
       """clip_embeddings_dim""": self.text_embedder_hidden_size,
       """time_embed_dim""": self.time_embed_dim,
       """cross_attention_dim""": self.cross_attention_dim,
   }

   lowercase__	: List[str]     		=   UnCLIPTextProjModel(**SCREAMING_SNAKE_CASE_)
   return model


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)

   lowercase__	: List[str]     		=   {
       """sample_size""": 32,
       # RGB in channels
       """in_channels""": 3,
       # Out channels is double in channels because predicts mean and variance
       """out_channels""": 6,
       """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
       """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
       """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
       """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
       """layers_per_block""": 1,
       """cross_attention_dim""": self.cross_attention_dim,
       """attention_head_dim""": 4,
       """resnet_time_scale_shift""": """scale_shift""",
       """class_embed_type""": """identity""",
   }

   lowercase__	: Optional[int]     		=   UNetaDConditionModel(**SCREAMING_SNAKE_CASE_)
   return model


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return {
       "sample_size": 64,
       "layers_per_block": 1,
       "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
       "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "in_channels": 6,
       "out_channels": 3,
   }


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)

   lowercase__	: Any     		=   UNetaDModel(**self.dummy_super_res_kwargs)
   return model


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(1)

   lowercase__	: Any     		=   UNetaDModel(**self.dummy_super_res_kwargs)
   return model


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.dummy_decoder
   lowercase__	: Any     		=   self.dummy_text_proj
   lowercase__	: Optional[int]     		=   self.dummy_text_encoder
   lowercase__	: Optional[int]     		=   self.dummy_tokenizer
   lowercase__	: Tuple     		=   self.dummy_super_res_first
   lowercase__	: List[Any]     		=   self.dummy_super_res_last

   lowercase__	: Optional[Any]     		=   UnCLIPScheduler(
       variance_type="""learned_range"""       ,	prediction_type="""epsilon"""       ,	num_train_timesteps=10_00       ,	)

   lowercase__	: Dict     		=   UnCLIPScheduler(
       variance_type="""fixed_small_log"""       ,	prediction_type="""epsilon"""       ,	num_train_timesteps=10_00       ,	)

   lowercase__	: Tuple     		=   CLIPImageProcessor(crop_size=32       ,	size=32)

   lowercase__	: List[str]     		=   self.dummy_image_encoder

   return {
       "decoder": decoder,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "text_proj": text_proj,
       "feature_extractor": feature_extractor,
       "image_encoder": image_encoder,
       "super_res_first": super_res_first,
       "super_res_last": super_res_last,
       "decoder_scheduler": decoder_scheduler,
       "super_res_scheduler": super_res_scheduler,
   }


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=True):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
   if str(SCREAMING_SNAKE_CASE_).startswith("""mps"""):
     lowercase__	: List[Any]     		=   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__	: int     		=   torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(SCREAMING_SNAKE_CASE_)

   if pil_image:
     lowercase__	: Any     		=   input_image * 0.5 + 0.5
     lowercase__	: Any     		=   input_image.clamp(0       ,	1)
     lowercase__	: Tuple     		=   input_image.cpu().permute(0       ,	2       ,	3       ,	1).float().numpy()
     lowercase__	: Union[str, Any]     		=   DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE_)[0]

   return {
       "image": input_image,
       "generator": generator,
       "decoder_num_inference_steps": 2,
       "super_res_num_inference_steps": 2,
       "output_type": "np",
   }


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   """cpu"""

   lowercase__	: List[str]     		=   self.get_dummy_components()

   lowercase__	: int     		=   self.pipeline_class(**SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   pipe.to(SCREAMING_SNAKE_CASE_)

   pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   pipe(**SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   output.images

   lowercase__	: Union[str, Any]     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   pipe(
       **SCREAMING_SNAKE_CASE_       ,	return_dict=SCREAMING_SNAKE_CASE_       ,	)[0]

   lowercase__	: Any     		=   image[0, -3:, -3:, -1]
   lowercase__	: List[str]     		=   image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   lowercase__	: str     		=   np.array(
       [
           0.9_9_9_7,
           0.0_0_0_2,
           0.9_9_9_7,
           0.9_9_9_7,
           0.9_9_6_9,
           0.0_0_2_3,
           0.9_9_9_7,
           0.9_9_6_9,
           0.9_9_7_0,
       ])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   """cpu"""

   lowercase__	: Optional[int]     		=   self.get_dummy_components()

   lowercase__	: List[Any]     		=   self.pipeline_class(**SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   pipe.to(SCREAMING_SNAKE_CASE_)

   pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[int]     		=   pipe(**SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   output.images

   lowercase__	: List[str]     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   pipe(
       **SCREAMING_SNAKE_CASE_       ,	return_dict=SCREAMING_SNAKE_CASE_       ,	)[0]

   lowercase__	: Optional[Any]     		=   image[0, -3:, -3:, -1]
   lowercase__	: Optional[int]     		=   image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   lowercase__	: str     		=   np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   """cpu"""

   lowercase__	: Union[str, Any]     		=   self.get_dummy_components()

   lowercase__	: str     		=   self.pipeline_class(**SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   pipe.to(SCREAMING_SNAKE_CASE_)

   pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   [
       pipeline_inputs["""image"""],
       pipeline_inputs["""image"""],
   ]

   lowercase__	: List[str]     		=   pipe(**SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   output.images

   lowercase__	: int     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   [
       tuple_pipeline_inputs["""image"""],
       tuple_pipeline_inputs["""image"""],
   ]

   lowercase__	: Optional[int]     		=   pipe(
       **SCREAMING_SNAKE_CASE_       ,	return_dict=SCREAMING_SNAKE_CASE_       ,	)[0]

   lowercase__	: Optional[Any]     		=   image[0, -3:, -3:, -1]
   lowercase__	: Any     		=   image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (2, 64, 64, 3)

   lowercase__	: List[str]     		=   np.array(
       [
           0.9_9_9_7,
           0.9_9_8_9,
           0.0_0_0_8,
           0.0_0_2_1,
           0.9_9_6_0,
           0.0_0_1_8,
           0.0_0_1_4,
           0.0_0_0_2,
           0.9_9_3_3,
       ])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   torch.device("""cpu""")



   class    _snake_case :
    __lowerCAmelCase	:  Optional[Any]	   =      1

   lowercase__	: int     		=   self.get_dummy_components()

   lowercase__	: Tuple     		=   self.pipeline_class(**SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   pipe.to(SCREAMING_SNAKE_CASE_)

   pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[int]     		=   torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(0)
   lowercase__	: List[str]     		=   pipe.decoder.dtype
   lowercase__	: int     		=   1

   lowercase__	: Dict     		=   (
       batch_size,
       pipe.decoder.config.in_channels,
       pipe.decoder.config.sample_size,
       pipe.decoder.config.sample_size,
   )
   lowercase__	: Tuple     		=   pipe.prepare_latents(
       SCREAMING_SNAKE_CASE_       ,	dtype=SCREAMING_SNAKE_CASE_       ,	device=SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_       ,	latents=SCREAMING_SNAKE_CASE_       ,	scheduler=DummyScheduler())

   lowercase__	: Tuple     		=   (
       batch_size,
       pipe.super_res_first.config.in_channels // 2,
       pipe.super_res_first.config.sample_size,
       pipe.super_res_first.config.sample_size,
   )
   lowercase__	: str     		=   pipe.prepare_latents(
       SCREAMING_SNAKE_CASE_       ,	dtype=SCREAMING_SNAKE_CASE_       ,	device=SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_       ,	latents=SCREAMING_SNAKE_CASE_       ,	scheduler=DummyScheduler())

   lowercase__	: List[Any]     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   pipe(
       **SCREAMING_SNAKE_CASE_       ,	decoder_latents=SCREAMING_SNAKE_CASE_       ,	super_res_latents=SCREAMING_SNAKE_CASE_).images

   lowercase__	: List[Any]     		=   self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ,	pil_image=SCREAMING_SNAKE_CASE_)
   # Don't pass image, instead pass embedding
   lowercase__	: List[str]     		=   pipeline_inputs.pop("""image""")
   lowercase__	: Optional[int]     		=   pipe.image_encoder(SCREAMING_SNAKE_CASE_).image_embeds

   lowercase__	: Union[str, Any]     		=   pipe(
       **SCREAMING_SNAKE_CASE_       ,	decoder_latents=SCREAMING_SNAKE_CASE_       ,	super_res_latents=SCREAMING_SNAKE_CASE_       ,	image_embeddings=SCREAMING_SNAKE_CASE_       ,	).images

   # make sure passing text embeddings manually is identical
   assert np.abs(img_out_a - img_out_a).max() < 1E-4


 @skip_mps
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   torch_device == """cpu"""

   # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
   lowercase__	: str     		=   1E-2

   self._test_attention_slicing_forward_pass(
       test_max_difference=SCREAMING_SNAKE_CASE_       ,	expected_max_diff=SCREAMING_SNAKE_CASE_)


 @skip_mps
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch_device == """cpu"""
   lowercase__	: Optional[int]     		=   True
   lowercase__	: Tuple     		=   [
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]

   self._test_inference_batch_single_identical(
       test_max_difference=SCREAMING_SNAKE_CASE_       ,	relax_max_difference=SCREAMING_SNAKE_CASE_       ,	additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   [
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]

   if torch_device == "mps":
     # TODO: MPS errors with larger batch sizes
     lowercase__	: Optional[int]     		=   [2, 3]
     self._test_inference_batch_consistent(
         batch_sizes=SCREAMING_SNAKE_CASE_       ,	additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_       ,	)
   else:
     self._test_inference_batch_consistent(
         additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_)


 @skip_mps
 def   lowercase__  (       self):


   '''simple docstring'''




   return super().test_dict_tuple_outputs_equivalent()


 @skip_mps
 def   lowercase__  (       self):


   '''simple docstring'''




   return super().test_save_load_local()



 @skip_mps
 def   lowercase__  (       self):


   '''simple docstring'''




   return super().test_save_load_optional_components()



@slow
@require_torch_gpu
class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""")
   lowercase__	: Tuple     		=   load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/unclip/karlo_v1_alpha_cat_variation_fp16.npy""")

   lowercase__	: str     		=   UnCLIPImageVariationPipeline.from_pretrained(
       """kakaobrain/karlo-v1-alpha-image-variations"""       ,	torch_dtype=torch.floataa)
   lowercase__	: Optional[int]     		=   pipeline.to(SCREAMING_SNAKE_CASE_)
   pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   torch.Generator(device="""cpu""").manual_seed(0)
   lowercase__	: int     		=   pipeline(
       SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_       ,	output_type="""np"""       ,	)

   lowercase__	: str     		=   output.images[0]

   assert image.shape == (2_56, 2_56, 3)

   assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	15)








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 ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       self):


   '''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,
   }



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	np.ndarray)

   # Test not batched input
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)






