





'''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__			(			lowercase_		,			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					KandinskyVaaPriorEmbaEmbPipeline
					SCREAMING_SNAKE_CASE__						=					['''prompt''', '''image''']
					SCREAMING_SNAKE_CASE__						=					['''prompt''', '''image''']
					SCREAMING_SNAKE_CASE__						=					[
					    '''num_images_per_prompt''',
					    '''strength''',
					    '''generator''',
					    '''num_inference_steps''',
					    '''latents''',
					    '''negative_prompt''',
					    '''guidance_scale''',
					    '''output_type''',
					    '''return_dict''',
					]
					SCREAMING_SNAKE_CASE__						=					False


					@property
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							return 32


					@property
					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							return 32


					@property
					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							return self.time_input_dim


					@property
					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							return self.time_input_dim * 4


					@property
					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							return 1_00


					@property
					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)
							return tokenizer


					@property
					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							torch.manual_seed(0		)
							SCREAMING_SNAKE_CASE				:							int						=	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(lowerCamelCase_		)


					@property
					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							torch.manual_seed(0		)

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

							SCREAMING_SNAKE_CASE				:							Dict						=	PriorTransformer(**lowerCamelCase_		)
							# 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
							SCREAMING_SNAKE_CASE				:							List[Any]						=	nn.Parameter(torch.ones(model.clip_std.shape		)		)
							return model


					@property
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							torch.manual_seed(0		)
							SCREAMING_SNAKE_CASE				:							Dict						=	CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size     ,		image_size=2_24     ,		projection_dim=self.text_embedder_hidden_size     ,		intermediate_size=37     ,		num_attention_heads=4     ,		num_channels=3     ,		num_hidden_layers=5     ,		patch_size=14     ,		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	CLIPVisionModelWithProjection(lowerCamelCase_		)
							return model


					@property
					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	CLIPImageProcessor(
							    crop_size=2_24     ,		do_center_crop=lowerCamelCase_     ,		do_normalize=lowerCamelCase_     ,		do_resize=lowerCamelCase_     ,		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=2_24     ,		)

							return image_processor


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE				:							Any						=	UnCLIPScheduler(
							    variance_type="""fixed_small_log"""     ,		prediction_type="""sample"""     ,		num_train_timesteps=10_00     ,		clip_sample=lowerCamelCase_     ,		clip_sample_range=10.0     ,		)

							SCREAMING_SNAKE_CASE				:							str						=	{
							    """prior""": prior,
							    """image_encoder""": image_encoder,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """scheduler""": scheduler,
							    """image_processor""": image_processor,
							}

							return components


					def lowerCamelCase_	(		self							:	int     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	List[str]=0		):

							'''simple docstring'''



							if str(lowerCamelCase_		).startswith("""mps"""		):
									SCREAMING_SNAKE_CASE				:							str						=	torch.manual_seed(lowerCamelCase_		)
							else:
									SCREAMING_SNAKE_CASE				:							Tuple						=	torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							int						=	floats_tensor((1, 3, 64, 64)     ,		rng=random.Random(lowerCamelCase_		)		).to(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	image.cpu().permute(0     ,		2     ,		3     ,		1		)[0]
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	Image.fromarray(np.uinta(lowerCamelCase_		)		).convert("""RGB"""		).resize((2_56, 2_56)		)

							SCREAMING_SNAKE_CASE				:							Dict						=	{
							    """prompt""": """horse""",
							    """image""": init_image,
							    """strength""": 0.5,
							    """generator""": generator,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	"""cpu"""

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

							SCREAMING_SNAKE_CASE				:							Dict						=	self.pipeline_class(**lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							List[str]						=	pipe.to(lowerCamelCase_		)

							pipe.set_progress_bar_config(disable=lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	pipe(**self.get_dummy_inputs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Any						=	output.image_embeds

							SCREAMING_SNAKE_CASE				:							Dict						=	pipe(
							    **self.get_dummy_inputs(lowerCamelCase_		)     ,		return_dict=lowerCamelCase_     ,		)[0]

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

							assert image.shape == (1, 32)

							SCREAMING_SNAKE_CASE				:							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 lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	torch_device == """cpu"""
							SCREAMING_SNAKE_CASE				:							str						=	True
							SCREAMING_SNAKE_CASE				:							Dict						=	False

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



					@skip_mps
					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	torch_device == """cpu"""
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False

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










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__UpperCAmelCase         =   logging.get_logger(__name__)




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					def __init__(		self							:	Dict     ,		*lowerCamelCase_							:	Optional[int]     ,		**lowerCamelCase_							:	Optional[int]		):

							'''simple docstring'''



							warnings.warn(
							    """The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use DetrImageProcessor instead."""     ,		lowerCamelCase_     ,		)
							super().__init__(*lowerCamelCase_     ,		**lowerCamelCase_		)



