



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class    __lowercase     (					__a					,    __a					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		StableDiffusionXLImgaImgPipeline
						_A		:							Union[str, Any]							       =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
						_A		:							Optional[int]							       =		PipelineTesterMixin.required_optional_params - {"""latents"""}
						_A		:							Dict							       =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						_A		:							Optional[Any]							       =		IMAGE_TO_IMAGE_IMAGE_PARAMS
						_A		:							Optional[int]							       =		IMAGE_TO_IMAGE_IMAGE_PARAMS







						def 						__UpperCamelCase							(self       ):
											torch.manual_seed(0       )
											snake_case_					:		Any 					=					UNetaDConditionModel(
											    block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=4   ,		out_channels=4   ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,		attention_head_dim=(2, 4)   ,		use_linear_projection=a_   ,		addition_embed_type="""text_time"""   ,		addition_time_embed_dim=8   ,		transformer_layers_per_block=(1, 2)   ,		projection_class_embeddings_input_dim=80   ,		cross_attention_dim=64   ,		)
											snake_case_					:		List[str] 					=					EulerDiscreteScheduler(
											    beta_start=0.00085   ,		beta_end=0.012   ,		steps_offset=1   ,		beta_schedule="""scaled_linear"""   ,		timestep_spacing="""leading"""   ,		)
											torch.manual_seed(0       )
											snake_case_					:		Optional[Any] 					=					AutoencoderKL(
											    block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,		latent_channels=4   ,		sample_size=1_28   ,		)
											torch.manual_seed(0       )
											snake_case_					:		Union[str, Any] 					=					CLIPTextConfig(
											    bos_token_id=0   ,		eos_token_id=2   ,		hidden_size=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		pad_token_id=1   ,		vocab_size=10_00   ,		hidden_act="""gelu"""   ,		projection_dim=32   ,		)
											snake_case_					:		Dict 					=					CLIPTextModel(a_       )
											snake_case_					:		Dict 					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,		local_files_only=a_       )

											snake_case_					:		Tuple 					=					CLIPTextModelWithProjection(a_       )
											snake_case_					:		int 					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,		local_files_only=a_       )

											snake_case_					:		Union[str, Any] 					=					{
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """text_encoder_2""": text_encoder_a,
											    """tokenizer_2""": tokenizer_a,
											    # "safety_checker": None,
											    # "feature_extractor": None,
											}
											return components







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__=0       ):
											snake_case_					:		int 					=					floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(a_       )       ).to(a_       )
											snake_case_					:		Optional[int] 					=					image / 2 + 0.5
											if str(a_       ).startswith("""mps"""       ):
																snake_case_					:		Optional[int] 					=					torch.manual_seed(a_       )
											else:
																snake_case_					:		str 					=					torch.Generator(device=a_       ).manual_seed(a_       )
											snake_case_					:		str 					=					{
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 5.0,
											    """output_type""": """numpy""",
											    """strength""": 0.75,
											}
											return inputs







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											snake_case_					:		Tuple 					=					self.get_dummy_components()
											snake_case_					:		List[Any] 					=					StableDiffusionXLImgaImgPipeline(**a_       )
											snake_case_					:		Optional[Any] 					=					sd_pipe.to(a_       )
											sd_pipe.set_progress_bar_config(disable=a_       )

											snake_case_					:		str 					=					self.get_dummy_inputs(a_       )
											snake_case_					:		List[Any] 					=					sd_pipe(**a_       ).images
											snake_case_					:		int 					=					image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)

											snake_case_					:		List[Any] 					=					np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2







						def 						__UpperCamelCase							(self       ):
											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )







						def 						__UpperCamelCase							(self       ):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					self.get_dummy_components()
											snake_case_					:		Tuple 					=					StableDiffusionXLImgaImgPipeline(**a_       )
											snake_case_					:		Union[str, Any] 					=					sd_pipe.to(a_       )
											snake_case_					:		int 					=					sd_pipe.to(a_       )
											sd_pipe.set_progress_bar_config(disable=a_       )

											# forward without prompt embeds
											snake_case_					:		str 					=					self.get_dummy_inputs(a_       )
											snake_case_					:		Any 					=					3 * ["""this is a negative prompt"""]
											snake_case_					:		Optional[Any] 					=					negative_prompt
											snake_case_					:		Optional[Any] 					=					3 * [inputs["""prompt"""]]

											snake_case_					:		Any 					=					sd_pipe(**a_       )
											snake_case_					:		List[str] 					=					output.images[0, -3:, -3:, -1]

											# forward with prompt embeds
											snake_case_					:		Optional[int] 					=					self.get_dummy_inputs(a_       )
											snake_case_					:		Any 					=					3 * ["""this is a negative prompt"""]
											snake_case_					:		List[str] 					=					3 * [inputs.pop("""prompt"""       )]

											(
											    snake_case_      
											)					:		str 					=					sd_pipe.encode_prompt(a_   ,		negative_prompt=a_       )

											snake_case_					:		Any 					=					sd_pipe(
											    **a_   ,		prompt_embeds=a_   ,		negative_prompt_embeds=a_   ,		pooled_prompt_embeds=a_   ,		negative_pooled_prompt_embeds=a_   ,		)
											snake_case_					:		Optional[Any] 					=					output.images[0, -3:, -3:, -1]

											# make sure that it's equal
											assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1e-4







@slow
@require_torch_gpu
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self       ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__="cpu"   ,		lowercase__=torch.floataa   ,		lowercase__=0       ):
											snake_case_					:		str 					=					torch.Generator(device=a_       ).manual_seed(a_       )
											snake_case_					:		Any 					=					np.random.RandomState(a_       ).standard_normal((1, 4, 64, 64)       )
											snake_case_					:		str 					=					torch.from_numpy(a_       ).to(device=a_   ,		dtype=a_       )
											snake_case_					:		int 					=					{
											    """prompt""": """a photograph of an astronaut riding a horse""",
											    """latents""": latents,
											    """generator""": generator,
											    """num_inference_steps""": 3,
											    """guidance_scale""": 7.5,
											    """output_type""": """numpy""",
											}
											return inputs







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""       )
											pipe.to(a_       )
											pipe.set_progress_bar_config(disable=a_       )

											snake_case_					:		int 					=					self.get_inputs(a_       )
											snake_case_					:		Dict 					=					pipe(**a_       ).images
											snake_case_					:		Optional[int] 					=					image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case_					:		Optional[int] 					=					np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]       )
											assert np.abs(image_slice - expected_slice       ).max() < 7e-3





"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		Any 					=					None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																    """ instead."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("""You need to specify an `image_processor`."""       )
											if tokenizer is None:
																raise ValueError("""You need to specify a `tokenizer`."""       )

											super().__init__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):

											if text is None and images is None:
																raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

											if text is not None:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







						@property
						def 						__UpperCamelCase							(self       ):
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,		lowercase__   ,		)
											return self.image_processor_class




