




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_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


	'''simple docstring'''







	lowercase_       =   PriorTransformer
	lowercase_       =   "hidden_states"



	@property
	def 				lowerCAmelCase_  (							self  :		int						):
							SCREAMING_SNAKE_CASE_     						=      4
							SCREAMING_SNAKE_CASE_     						=      8
							SCREAMING_SNAKE_CASE_     						=      7

							SCREAMING_SNAKE_CASE_     						=      floats_tensor((batch_size, embedding_dim)						).to(_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      floats_tensor((batch_size, embedding_dim)						).to(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Dict=0						):
							torch.manual_seed(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      4
							SCREAMING_SNAKE_CASE_     						=      8
							SCREAMING_SNAKE_CASE_     						=      7

							SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Tuple						):
							return (4, 8)



	@property
	def 				lowerCAmelCase_  (							self  :		int						):
							return (4, 8)



	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_     						=      {
							    'num_attention_heads': 2,
							    'attention_head_dim': 4,
							    'num_layers': 2,
							    'embedding_dim': 8,
							    'num_embeddings': 7,
							    'additional_embeddings': 4,
							}
							SCREAMING_SNAKE_CASE_     						=      self.dummy_input
							return init_dict, inputs_dict



	def 				lowerCAmelCase_  (							self  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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						)
							SCREAMING_SNAKE_CASE_     						=      model(**self.dummy_input						)[0]

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



	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.prepare_init_args_and_inputs_for_common()

							SCREAMING_SNAKE_CASE_     						=      self.model_class(**_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							SCREAMING_SNAKE_CASE_     						=      [*signature.parameters.keys()]

							SCREAMING_SNAKE_CASE_     						=      ['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]    ,    _lowerCAmelCase						)




	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'						)
							SCREAMING_SNAKE_CASE_     						=      model.to(_lowerCAmelCase						)

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

							SCREAMING_SNAKE_CASE_     						=      self.get_dummy_seed_input()

							with torch.no_grad():
													SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)[0]

							SCREAMING_SNAKE_CASE_     						=      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.
							SCREAMING_SNAKE_CASE_     						=      torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]						)
							self.assertTrue(torch_all_close(_lowerCAmelCase    ,    _lowerCAmelCase    ,    rtol=1E-2						)						)





@slow
class 	lowerCamelCase_							(				unittest.TestCase ):


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		Any=1    ,    _lowerCAmelCase  :		Optional[int]=768    ,    _lowerCAmelCase  :		Tuple=77    ,    _lowerCAmelCase  :		Optional[Any]=0						):
							torch.manual_seed(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      batch_size
							SCREAMING_SNAKE_CASE_     						=      embedding_dim
							SCREAMING_SNAKE_CASE_     						=      num_embeddings

							SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      torch.randn((batch_size, embedding_dim)						).to(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
	        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
	        # fmt: on
	    ]						)
	def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Dict						):
							SCREAMING_SNAKE_CASE_     						=      PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'    ,    subfolder='prior'						)
							model.to(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      self.get_dummy_seed_input(seed=_lowerCAmelCase						)

							with torch.no_grad():
													SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)[0]

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

							SCREAMING_SNAKE_CASE_     						=      sample[0, :8].flatten().cpu()
							print(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      torch.tensor(_lowerCAmelCase						)

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





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 LevitImageProcessor





class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def __init__(							self  :		str    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any=7    ,    _lowerCAmelCase  :		Tuple=3    ,    _lowerCAmelCase  :		List[str]=18    ,    _lowerCAmelCase  :		Any=30    ,    _lowerCAmelCase  :		List[Any]=400    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		Tuple=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[Any]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 18}
       SCREAMING_SNAKE_CASE_     						=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      min_resolution
       SCREAMING_SNAKE_CASE_     						=      max_resolution
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std




 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }





@require_torch
@require_vision
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   LevitImageProcessor if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      LevitImageProcessingTester(self						)



 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_mean'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_std'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_normalize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_resize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_center_crop'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'size'						)						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 18}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 18, 'width': 18}						)

       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict    ,    size=42    ,    crop_size=84						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 42}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 84, 'width': 84}						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       pass



 def 				lowerCAmelCase_  (							self  :		int						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    Image.Image						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		str						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Tuple						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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'],
           )    ,    )