





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 PoolFormerImageProcessor



class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  def __init__(							self					:						int   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						List[Any]=7   ,    lowerCAmelCase_					:						Optional[int]=3   ,    lowerCAmelCase_					:						List[Any]=3_0   ,    lowerCAmelCase_					:						List[str]=4_0_0   ,    lowerCAmelCase_					:						int=True   ,    lowerCAmelCase_					:						str=None   ,    lowerCAmelCase_					:						List[Any]=0.9   ,    lowerCAmelCase_					:						Any=None   ,    lowerCAmelCase_					:						Dict=True   ,    lowerCAmelCase_					:						Optional[Any]=[0.5, 0.5, 0.5]   ,    lowerCAmelCase_					:						List[str]=[0.5, 0.5, 0.5]   ,    )				->		List[str]:
     __lowerCAmelCase				      =      size if size is not None else {'shortest_edge': 3_0}
     __lowerCAmelCase				      =      crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      min_resolution
     __lowerCAmelCase				      =      max_resolution
     __lowerCAmelCase				      =      do_resize_and_center_crop
     __lowerCAmelCase				      =      size
     __lowerCAmelCase				      =      crop_pct
     __lowerCAmelCase				      =      crop_size
     __lowerCAmelCase				      =      do_normalize
     __lowerCAmelCase				      =      image_mean
     __lowerCAmelCase				      =      image_std






  def 							lowercase						(							self					:						str	)				->		Tuple:
     return {
         "size": self.size,
         "do_resize_and_center_crop": self.do_resize_and_center_crop,
         "crop_pct": self.crop_pct,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }





@require_torch
@require_vision
class 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					PoolFormerImageProcessor if is_vision_available() else None






  def 							lowercase						(							self					:						int	)				->		str:
     __lowerCAmelCase				      =      PoolFormerImageProcessingTester(self	)






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






  def 							lowercase						(							self					:						List[str]	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_resize_and_center_crop'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'size'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'crop_pct'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_normalize'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'image_mean'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'image_std'	)	)






  def 							lowercase						(							self					:						Optional[Any]	)				->		List[str]:
     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 3_0}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 3_0, 'width': 3_0}	)

     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict   ,    size=4_2   ,    crop_size=8_4	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 4_2}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 8_4, 'width': 8_4}	)






  def 							lowercase						(							self					:						List[Any]	)				->		str:
     pass






  def 							lowercase						(							self					:						Tuple	)				->		Tuple:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    Image.Image	)

     # Test not batched input
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    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 							lowercase						(							self					:						Tuple	)				->		Optional[int]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    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 							lowercase						(							self					:						Dict	)				->		Optional[Any]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      image_processing(lowerCAmelCase_   ,    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'],
         )   ,    )








import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				PriorTransformer
	_A													=				"hidden_states"

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      List[Any]      =		4
			lowercase__					:      Optional[int]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Optional[Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		floats_tensor((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      Any      =		floats_tensor((batch_size, num_embeddings, embedding_dim)			).to(lowerCamelCase_			)

			return {
			    "hidden_states": hidden_states,
			    "timestep": 2,
			    "proj_embedding": proj_embedding,
			    "encoder_hidden_states": encoder_hidden_states,
			}

	def    snake_case__(						self:						int,					lowerCamelCase_:						Dict=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Tuple      =		4
			lowercase__					:      List[Any]      =		8
			lowercase__					:      List[str]      =		7

			lowercase__					:      Union[str, Any]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.randn((batch_size, num_embeddings, embedding_dim)			).to(lowerCamelCase_			)

			return {
			    "hidden_states": hidden_states,
			    "timestep": 2,
			    "proj_embedding": proj_embedding,
			    "encoder_hidden_states": encoder_hidden_states,
			}

	@property
	def    snake_case__(						self:						str			):
			return (4, 8)

	@property
	def    snake_case__(						self:						List[str]			):
			return (4, 8)

	def    snake_case__(						self:						Dict			):
			lowercase__					:      int      =		{
			    'num_attention_heads': 2,
			    'attention_head_dim': 4,
			    'num_layers': 2,
			    'embedding_dim': 8,
			    'num_embeddings': 7,
			    'additional_embeddings': 4,
			}
			lowercase__					:      Optional[Any]      =		self.dummy_input
			return init_dict, inputs_dict

	def    snake_case__(						self:						int			):
			lowercase__   ,				lowercase__					:      Dict      =		PriorTransformer.from_pretrained(
			    'hf-internal-testing/prior-dummy',					output_loading_info=lowerCamelCase_			)
			self.assertIsNotNone(lowerCamelCase_			)
			self.assertEqual(len(loading_info['missing_keys']			),					0			)

			model.to(lowerCamelCase_			)
			lowercase__					:      Tuple      =		model(**self.dummy_input			)[0]

			assert hidden_states is not None, "Make sure output is not None"

	def    snake_case__(						self:						str			):
			lowercase__   ,				lowercase__					:      List[Any]      =		self.prepare_init_args_and_inputs_for_common()

			lowercase__					:      Optional[int]      =		self.model_class(**lowerCamelCase_			)
			lowercase__					:      int      =		inspect.signature(model.forward			)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			lowercase__					:      Dict      =		[*signature.parameters.keys()]

			lowercase__					:      List[str]      =		['hidden_states', 'timestep']
			self.assertListEqual(arg_names[:2],					lowerCamelCase_			)





	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      str      =		PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'			)
			lowercase__					:      Optional[int]      =		model.to(lowerCamelCase_			)

			if hasattr(lowerCamelCase_,					'set_default_attn_processor'			):
					model.set_default_attn_processor()

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

			with torch.no_grad():
					lowercase__					:      str      =		model(**lowerCamelCase_			)[0]

			lowercase__					:      int      =		output[0, :5].flatten().cpu()
			print(lowerCamelCase_			)

			# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
			# the expected output slices are not the same for CPU and GPU.
			lowercase__					:      List[str]      =		torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]			)
			self.assertTrue(torch_all_close(lowerCamelCase_,					lowerCamelCase_,					rtol=1E-2			)			)


@slow
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Union[str, Any]=1,					lowerCamelCase_:						Tuple=768,					lowerCamelCase_:						Dict=77,					lowerCamelCase_:						Union[str, Any]=0			):
			torch.manual_seed(lowerCamelCase_			)
			lowercase__					:      Dict      =		batch_size
			lowercase__					:      Dict      =		embedding_dim
			lowercase__					:      Dict      =		num_embeddings

			lowercase__					:      int      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)

			lowercase__					:      str      =		torch.randn((batch_size, embedding_dim)			).to(lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.randn((batch_size, num_embeddings, embedding_dim)			).to(lowerCamelCase_			)

			return {
			    "hidden_states": hidden_states,
			    "timestep": 2,
			    "proj_embedding": proj_embedding,
			    "encoder_hidden_states": encoder_hidden_states,
			}

	def    snake_case__(						self:						str			):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
	        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
	        # fmt: on
	    ]			)
	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						str			):
			lowercase__					:      List[Any]      =		PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior',					subfolder='prior'			)
			model.to(lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		self.get_dummy_seed_input(seed=lowerCamelCase_			)

			with torch.no_grad():
					lowercase__					:      List[str]      =		model(**lowerCamelCase_			)[0]

			assert list(sample.shape			) == [1, 768]

			lowercase__					:      Union[str, Any]      =		sample[0, :8].flatten().cpu()
			print(lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		torch.tensor(lowerCamelCase_			)

			assert torch_all_close(lowerCamelCase_,					lowerCamelCase_,					atol=1E-3			)


