



"""simple docstring"""

import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					KandinskyVaaPriorPipeline
						_lowerCAmelCase							=					["prompt"]
						_lowerCAmelCase							=					["prompt", "negative_prompt"]
						_lowerCAmelCase							=					[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						_lowerCAmelCase							=					False



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim * 4



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 100



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
													return tokenizer



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		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=1000						,     )
													return CLIPTextModelWithProjection(_lowercase							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)

													__a		:		Dict									=       {
													    """num_attention_heads""": 2,
													    """attention_head_dim""": 12,
													    """embedding_dim""": self.text_embedder_hidden_size,
													    """num_layers""": 1,
													}

													__a		:		Tuple									=       PriorTransformer(**_lowercase							)
													# 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
													__a		:		int									=       nn.Parameter(torch.ones(model.clip_std.shape							)							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		List[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						,     )

													__a		:		Optional[Any]									=       CLIPVisionModelWithProjection(_lowercase							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       CLIPImageProcessor(
													    crop_size=224						,     do_center_crop=_lowercase						,     do_normalize=_lowercase						,     do_resize=_lowercase						,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,     resample=3						,     size=224						,     )

													return image_processor



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       self.dummy_prior
													__a		:		int									=       self.dummy_image_encoder
													__a		:		Any									=       self.dummy_text_encoder
													__a		:		int									=       self.dummy_tokenizer
													__a		:		Optional[Any]									=       self.dummy_image_processor

													__a		:		List[Any]									=       UnCLIPScheduler(
													    variance_type="""fixed_small_log"""						,     prediction_type="""sample"""						,     num_train_timesteps=1000						,     clip_sample=_lowercase						,     clip_sample_range=10.0						,     )

													__a		:		List[Any]									=       {
													    """prior""": prior,
													    """image_encoder""": image_encoder,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """scheduler""": scheduler,
													    """image_processor""": image_processor,
													}

													return components



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=0							):





													'''simple docstring'''

													if str(_lowercase							).startswith("""mps"""							):
																				__a		:		Dict									=       torch.manual_seed(_lowercase							)
													else:
																				__a		:		Union[str, Any]									=       torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
													__a		:		Union[str, Any]									=       {
													    """prompt""": """horse""",
													    """generator""": generator,
													    """guidance_scale""": 4.0,
													    """num_inference_steps""": 2,
													    """output_type""": """np""",
													}
													return inputs



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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

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

													__a		:		Dict									=       self.pipeline_class(**_lowercase							)
													__a		:		Tuple									=       pipe.to(_lowercase							)

													pipe.set_progress_bar_config(disable=_lowercase							)

													__a		:		Optional[int]									=       pipe(**self.get_dummy_inputs(_lowercase							)							)
													__a		:		str									=       output.image_embeds

													__a		:		Any									=       pipe(
													    **self.get_dummy_inputs(_lowercase							)						,     return_dict=_lowercase						,     )[0]

													__a		:		List[Any]									=       image[0, -10:]
													__a		:		List[Any]									=       image_from_tuple[0, -10:]

													assert image.shape == (1, 32)

													__a		:		Optional[Any]									=       np.array(
													    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]							)

													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     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       torch_device == """cpu"""
													__a		:		Any									=       True
													__a		:		Any									=       False

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





						@skip_mps
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       torch_device == """cpu"""
													__a		:		Union[str, Any]									=       False

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






"""simple docstring"""

import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					KandinskyVaaPriorPipeline
						_lowerCAmelCase							=					["prompt"]
						_lowerCAmelCase							=					["prompt", "negative_prompt"]
						_lowerCAmelCase							=					[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						_lowerCAmelCase							=					False



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim * 4



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 100



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
													return tokenizer



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		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=1000						,     )
													return CLIPTextModelWithProjection(_lowercase							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)

													__a		:		Dict									=       {
													    """num_attention_heads""": 2,
													    """attention_head_dim""": 12,
													    """embedding_dim""": self.text_embedder_hidden_size,
													    """num_layers""": 1,
													}

													__a		:		Tuple									=       PriorTransformer(**_lowercase							)
													# 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
													__a		:		int									=       nn.Parameter(torch.ones(model.clip_std.shape							)							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		List[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						,     )

													__a		:		Optional[Any]									=       CLIPVisionModelWithProjection(_lowercase							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       CLIPImageProcessor(
													    crop_size=224						,     do_center_crop=_lowercase						,     do_normalize=_lowercase						,     do_resize=_lowercase						,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,     resample=3						,     size=224						,     )

													return image_processor



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       self.dummy_prior
													__a		:		int									=       self.dummy_image_encoder
													__a		:		Any									=       self.dummy_text_encoder
													__a		:		int									=       self.dummy_tokenizer
													__a		:		Optional[Any]									=       self.dummy_image_processor

													__a		:		List[Any]									=       UnCLIPScheduler(
													    variance_type="""fixed_small_log"""						,     prediction_type="""sample"""						,     num_train_timesteps=1000						,     clip_sample=_lowercase						,     clip_sample_range=10.0						,     )

													__a		:		List[Any]									=       {
													    """prior""": prior,
													    """image_encoder""": image_encoder,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """scheduler""": scheduler,
													    """image_processor""": image_processor,
													}

													return components



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=0							):





													'''simple docstring'''

													if str(_lowercase							).startswith("""mps"""							):
																				__a		:		Dict									=       torch.manual_seed(_lowercase							)
													else:
																				__a		:		Union[str, Any]									=       torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
													__a		:		Union[str, Any]									=       {
													    """prompt""": """horse""",
													    """generator""": generator,
													    """guidance_scale""": 4.0,
													    """num_inference_steps""": 2,
													    """output_type""": """np""",
													}
													return inputs



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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

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

													__a		:		Dict									=       self.pipeline_class(**_lowercase							)
													__a		:		Tuple									=       pipe.to(_lowercase							)

													pipe.set_progress_bar_config(disable=_lowercase							)

													__a		:		Optional[int]									=       pipe(**self.get_dummy_inputs(_lowercase							)							)
													__a		:		str									=       output.image_embeds

													__a		:		Any									=       pipe(
													    **self.get_dummy_inputs(_lowercase							)						,     return_dict=_lowercase						,     )[0]

													__a		:		List[Any]									=       image[0, -10:]
													__a		:		List[Any]									=       image_from_tuple[0, -10:]

													assert image.shape == (1, 32)

													__a		:		Optional[Any]									=       np.array(
													    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]							)

													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     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       torch_device == """cpu"""
													__a		:		Any									=       True
													__a		:		Any									=       False

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





						@skip_mps
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       torch_device == """cpu"""
													__a		:		Union[str, Any]									=       False

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

