
'''simple docstring'''

import random
import unittest

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

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 			UpperCAmelCase				(					a_  ,		unittest.TestCase ):





		"""simple docstring"""





		A__  :   str          =    KandinskyVaaPriorEmbaEmbPipeline
		A__  :   Dict          =    ['prompt', 'image']
		A__  :   List[Any]          =    ['prompt', 'image']
		A__  :   List[Any]          =    [
		    'num_images_per_prompt',
		    'strength',
		    'generator',
		    'num_inference_steps',
		    'latents',
		    'negative_prompt',
		    'guidance_scale',
		    'output_type',
		    'return_dict',
		]
		A__  :   List[str]          =    False
		@property
		def _lowercase  (    self     )				->    List[Any]:
				return 32
		@property
		def _lowercase  (    self     )				->    List[str]:
				return 32
		@property
		def _lowercase  (    self     )				->    Any:
				return self.time_input_dim
		@property
		def _lowercase  (    self     )				->    str:
				return self.time_input_dim * 4
		@property
		def _lowercase  (    self     )				->    List[Any]:
				return 100
		@property
		def _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : Any    =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )
				return tokenizer
		@property
		def _lowercase  (    self     )				->    List[Any]:
				torch.manual_seed(0     )
				_UpperCamelCase       : Optional[Any]    =      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=1000  ,					)
				return CLIPTextModelWithProjection(_snake_case     )
		@property
		def _lowercase  (    self     )				->    Optional[int]:
				torch.manual_seed(0     )

				_UpperCamelCase       : Any    =      {
				    '''num_attention_heads''': 2,
				    '''attention_head_dim''': 12,
				    '''embedding_dim''': self.text_embedder_hidden_size,
				    '''num_layers''': 1,
				}

				_UpperCamelCase       : Union[str, Any]    =      PriorTransformer(**_snake_case     )
				# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
				_UpperCamelCase       : Tuple    =      nn.Parameter(torch.ones(model.clip_std.shape     )     )
				return model
		@property
		def _lowercase  (    self     )				->    str:
				torch.manual_seed(0     )
				_UpperCamelCase       : str    =      CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size  ,					image_size=224  ,					projection_dim=self.text_embedder_hidden_size  ,					intermediate_size=37  ,					num_attention_heads=4  ,					num_channels=3  ,					num_hidden_layers=5  ,					patch_size=14  ,					)

				_UpperCamelCase       : str    =      CLIPVisionModelWithProjection(_snake_case     )
				return model
		@property
		def _lowercase  (    self     )				->    Union[str, Any]:
				_UpperCamelCase       : List[str]    =      CLIPImageProcessor(
				    crop_size=224  ,					do_center_crop=_snake_case  ,					do_normalize=_snake_case  ,					do_resize=_snake_case  ,					image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,					image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,					resample=3  ,					size=224  ,					)

				return image_processor
		def _lowercase  (    self     )				->    Union[str, Any]:
				_UpperCamelCase       : List[str]    =      self.dummy_prior
				_UpperCamelCase       : List[str]    =      self.dummy_image_encoder
				_UpperCamelCase       : List[Any]    =      self.dummy_text_encoder
				_UpperCamelCase       : Tuple    =      self.dummy_tokenizer
				_UpperCamelCase       : List[Any]    =      self.dummy_image_processor

				_UpperCamelCase       : Optional[Any]    =      UnCLIPScheduler(
				    variance_type='''fixed_small_log'''  ,					prediction_type='''sample'''  ,					num_train_timesteps=1000  ,					clip_sample=_snake_case  ,					clip_sample_range=10.0  ,					)

				_UpperCamelCase       : Union[str, Any]    =      {
				    '''prior''': prior,
				    '''image_encoder''': image_encoder,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''scheduler''': scheduler,
				    '''image_processor''': image_processor,
				}

				return components
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case=0     )				->    Any:
				if str(_snake_case     ).startswith('''mps'''     ):
						_UpperCamelCase       : str    =      torch.manual_seed(_snake_case     )
				else:
						_UpperCamelCase       : str    =      torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

				_UpperCamelCase       : List[str]    =      floats_tensor((1, 3, 64, 64)  ,					rng=random.Random(_snake_case     )     ).to(_snake_case     )
				_UpperCamelCase       : int    =      image.cpu().permute(0  ,					2  ,					3  ,					1     )[0]
				_UpperCamelCase       : List[str]    =      Image.fromarray(np.uinta(_snake_case     )     ).convert('''RGB'''     ).resize((256, 256)     )

				_UpperCamelCase       : List[str]    =      {
				    '''prompt''': '''horse''',
				    '''image''': init_image,
				    '''strength''': 0.5,
				    '''generator''': generator,
				    '''guidance_scale''': 4.0,
				    '''num_inference_steps''': 2,
				    '''output_type''': '''np''',
				}
				return inputs
		def _lowercase  (    self     )				->    List[str]:
				_UpperCamelCase       : Any    =      '''cpu'''

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

				_UpperCamelCase       : Optional[Any]    =      self.pipeline_class(**_snake_case     )
				_UpperCamelCase       : str    =      pipe.to(_snake_case     )

				pipe.set_progress_bar_config(disable=_snake_case     )

				_UpperCamelCase       : List[Any]    =      pipe(**self.get_dummy_inputs(_snake_case     )     )
				_UpperCamelCase       : Tuple    =      output.image_embeds

				_UpperCamelCase       : int    =      pipe(
				    **self.get_dummy_inputs(_snake_case     )  ,					return_dict=_snake_case  ,					)[0]

				_UpperCamelCase       : Union[str, Any]    =      image[0, -10:]
				_UpperCamelCase       : List[str]    =      image_from_tuple[0, -10:]

				assert image.shape == (1, 32)

				_UpperCamelCase       : int    =      np.array(
				    [
				        0.1_071_284,
				        1.3_330_271,
				        0.61_260_223,
				        -0.6_691_065,
				        -0.3_846_852,
				        -1.0_303_661,
				        0.22_716_111,
				        0.03_348_901,
				        0.30_040_675,
				        -0.24_805_029,
				    ]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
		@skip_mps
		def _lowercase  (    self     )				->    Any:
				_UpperCamelCase       : Union[str, Any]    =      torch_device == '''cpu'''
				_UpperCamelCase       : Any    =      True
				_UpperCamelCase       : Optional[Any]    =      False

				self._test_inference_batch_single_identical(
				    test_max_difference=_snake_case  ,					relax_max_difference=_snake_case  ,					test_mean_pixel_difference=_snake_case  ,					)




		@skip_mps
		def _lowercase  (    self     )				->    Any:
				_UpperCamelCase       : Any    =      torch_device == '''cpu'''
				_UpperCamelCase       : Union[str, Any]    =      False

				self._test_attention_slicing_forward_pass(
				    test_max_difference=_snake_case  ,					test_mean_pixel_difference=_snake_case  ,					)







'''simple docstring'''

_UpperCAmelCase   :      Optional[int] 						=  """
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_UpperCAmelCase   :      Union[str, Any] 						=  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_UpperCAmelCase   :      Union[str, Any] 						=  {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}





