
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      self  ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					1
									_lowercase					       =					3
									_lowercase					       =					(32, 32)

									_lowercase					       =					floats_tensor((batch_size, num_channels) + sizes				,			rng=random.Random(0  )  ).to(__UpperCamelCase  )
									return image






			@property
			def    UpperCamelCase_				(      self  ):
									torch.manual_seed(0  )
									_lowercase					       =					UNetaDConditionModel(
									    block_out_channels=(32, 32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=7				,			out_channels=4				,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")				,			up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")				,			cross_attention_dim=32				,			attention_head_dim=8				,			use_linear_projection=__UpperCamelCase				,			only_cross_attention=(True, True, False)				,			num_class_embeds=100				,			)
									return model






			@property
			def    UpperCamelCase_				(      self  ):
									torch.manual_seed(0  )
									_lowercase					       =					AutoencoderKL(
									    block_out_channels=[32, 32, 64]				,			in_channels=3				,			out_channels=3				,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,			latent_channels=4				,			)
									return model






			@property
			def    UpperCamelCase_				(      self  ):
									torch.manual_seed(0  )
									_lowercase					       =					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=1_000				,			hidden_act="""gelu"""				,			projection_dim=512				,			)
									return CLIPTextModel(__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowercase					       =					self.dummy_cond_unet_upscale
									_lowercase					       =					DDPMScheduler()
									_lowercase					       =					DDIMScheduler(prediction_type="""v_prediction"""  )
									_lowercase					       =					self.dummy_vae
									_lowercase					       =					self.dummy_text_encoder
									_lowercase					       =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									_lowercase					       =					self.dummy_image.cpu().permute(0				,			2				,			3				,			1  )[0]
									_lowercase					       =					Image.fromarray(np.uinta(__UpperCamelCase  )  ).convert("""RGB"""  ).resize((64, 64)  )

									# make sure here that pndm scheduler skips prk
									_lowercase					       =					StableDiffusionUpscalePipeline(
									    unet=__UpperCamelCase				,			low_res_scheduler=__UpperCamelCase				,			scheduler=__UpperCamelCase				,			vae=__UpperCamelCase				,			text_encoder=__UpperCamelCase				,			tokenizer=__UpperCamelCase				,			max_noise_level=350				,			)
									_lowercase					       =					sd_pipe.to(__UpperCamelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

									_lowercase					       =					"""A painting of a squirrel eating a burger"""
									_lowercase					       =					torch.Generator(device=__UpperCamelCase  ).manual_seed(0  )
									_lowercase					       =					sd_pipe(
									    [prompt]				,			image=__UpperCamelCase				,			generator=__UpperCamelCase				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type="""np"""				,			)

									_lowercase					       =					output.images

									_lowercase					       =					torch.Generator(device=__UpperCamelCase  ).manual_seed(0  )
									_lowercase					       =					sd_pipe(
									    [prompt]				,			image=__UpperCamelCase				,			generator=__UpperCamelCase				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type="""np"""				,			return_dict=__UpperCamelCase				,			)[0]

									_lowercase					       =					image[0, -3:, -3:, -1]
									_lowercase					       =					image_from_tuple[0, -3:, -3:, -1]

									_lowercase					       =					low_res_image.size[0] * 4
									assert image.shape == (1, expected_height_width, expected_height_width, 3)
									_lowercase					       =					np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]  )

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowercase					       =					self.dummy_cond_unet_upscale
									_lowercase					       =					DDPMScheduler()
									_lowercase					       =					DDIMScheduler(prediction_type="""v_prediction"""  )
									_lowercase					       =					self.dummy_vae
									_lowercase					       =					self.dummy_text_encoder
									_lowercase					       =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									_lowercase					       =					self.dummy_image.cpu().permute(0				,			2				,			3				,			1  )[0]
									_lowercase					       =					Image.fromarray(np.uinta(__UpperCamelCase  )  ).convert("""RGB"""  ).resize((64, 64)  )

									# make sure here that pndm scheduler skips prk
									_lowercase					       =					StableDiffusionUpscalePipeline(
									    unet=__UpperCamelCase				,			low_res_scheduler=__UpperCamelCase				,			scheduler=__UpperCamelCase				,			vae=__UpperCamelCase				,			text_encoder=__UpperCamelCase				,			tokenizer=__UpperCamelCase				,			max_noise_level=350				,			)
									_lowercase					       =					sd_pipe.to(__UpperCamelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

									_lowercase					       =					"""A painting of a squirrel eating a burger"""
									_lowercase					       =					sd_pipe(
									    2 * [prompt]				,			image=2 * [low_res_image]				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type="""np"""				,			)
									_lowercase					       =					output.images
									assert image.shape[0] == 2

									_lowercase					       =					torch.Generator(device=__UpperCamelCase  ).manual_seed(0  )
									_lowercase					       =					sd_pipe(
									    [prompt]				,			image=__UpperCamelCase				,			generator=__UpperCamelCase				,			num_images_per_prompt=2				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type="""np"""				,			)
									_lowercase					       =					output.images
									assert image.shape[0] == 2






			@unittest.skipIf(torch_device != """cuda"""				,			"""This test requires a GPU"""  )
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.dummy_cond_unet_upscale
									_lowercase					       =					DDPMScheduler()
									_lowercase					       =					DDIMScheduler(prediction_type="""v_prediction"""  )
									_lowercase					       =					self.dummy_vae
									_lowercase					       =					self.dummy_text_encoder
									_lowercase					       =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									_lowercase					       =					self.dummy_image.cpu().permute(0				,			2				,			3				,			1  )[0]
									_lowercase					       =					Image.fromarray(np.uinta(__UpperCamelCase  )  ).convert("""RGB"""  ).resize((64, 64)  )

									# put models in fp16, except vae as it overflows in fp16
									_lowercase					       =					unet.half()
									_lowercase					       =					text_encoder.half()

									# make sure here that pndm scheduler skips prk
									_lowercase					       =					StableDiffusionUpscalePipeline(
									    unet=__UpperCamelCase				,			low_res_scheduler=__UpperCamelCase				,			scheduler=__UpperCamelCase				,			vae=__UpperCamelCase				,			text_encoder=__UpperCamelCase				,			tokenizer=__UpperCamelCase				,			max_noise_level=350				,			)
									_lowercase					       =					sd_pipe.to(__UpperCamelCase  )
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

									_lowercase					       =					"""A painting of a squirrel eating a burger"""
									_lowercase					       =					torch.manual_seed(0  )
									_lowercase					       =					sd_pipe(
									    [prompt]				,			image=__UpperCamelCase				,			generator=__UpperCamelCase				,			num_inference_steps=2				,			output_type="""np"""				,			).images

									_lowercase					       =					low_res_image.size[0] * 4
									assert image.shape == (1, expected_height_width, expected_height_width, 3)







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






			def    UpperCamelCase_				(      self  ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/sd2-upscale/low_res_cat.png"""  )
									_lowercase					       =					load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
									    """/upsampled_cat.npy"""  )

									_lowercase					       =					"""stabilityai/stable-diffusion-x4-upscaler"""
									_lowercase					       =					StableDiffusionUpscalePipeline.from_pretrained(__UpperCamelCase  )
									pipe.to(__UpperCamelCase  )
									pipe.set_progress_bar_config(disable=__UpperCamelCase  )
									pipe.enable_attention_slicing()

									_lowercase					       =					"""a cat sitting on a park bench"""

									_lowercase					       =					torch.manual_seed(0  )
									_lowercase					       =					pipe(
									    prompt=__UpperCamelCase				,			image=__UpperCamelCase				,			generator=__UpperCamelCase				,			output_type="""np"""				,			)
									_lowercase					       =					output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image  ).max() < 1E-3






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/sd2-upscale/low_res_cat.png"""  )
									_lowercase					       =					load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
									    """/upsampled_cat_fp16.npy"""  )

									_lowercase					       =					"""stabilityai/stable-diffusion-x4-upscaler"""
									_lowercase					       =					StableDiffusionUpscalePipeline.from_pretrained(
									    __UpperCamelCase				,			torch_dtype=torch.floataa				,			)
									pipe.to(__UpperCamelCase  )
									pipe.set_progress_bar_config(disable=__UpperCamelCase  )
									pipe.enable_attention_slicing()

									_lowercase					       =					"""a cat sitting on a park bench"""

									_lowercase					       =					torch.manual_seed(0  )
									_lowercase					       =					pipe(
									    prompt=__UpperCamelCase				,			image=__UpperCamelCase				,			generator=__UpperCamelCase				,			output_type="""np"""				,			)
									_lowercase					       =					output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image  ).max() < 5E-1






			def    UpperCamelCase_				(      self  ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_lowercase					       =					load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/sd2-upscale/low_res_cat.png"""  )

									_lowercase					       =					"""stabilityai/stable-diffusion-x4-upscaler"""
									_lowercase					       =					StableDiffusionUpscalePipeline.from_pretrained(
									    __UpperCamelCase				,			torch_dtype=torch.floataa				,			)
									pipe.to(__UpperCamelCase  )
									pipe.set_progress_bar_config(disable=__UpperCamelCase  )
									pipe.enable_attention_slicing(1  )
									pipe.enable_sequential_cpu_offload()

									_lowercase					       =					"""a cat sitting on a park bench"""

									_lowercase					       =					torch.manual_seed(0  )
									_lowercase					       =					pipe(
									    prompt=__UpperCamelCase				,			image=__UpperCamelCase				,			generator=__UpperCamelCase				,			num_inference_steps=5				,			output_type="""np"""				,			)

									_lowercase					       =					torch.cuda.max_memory_allocated()
									# make sure that less than 2.9 GB is allocated
									assert mem_bytes < 2.9 * 10**9

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A		: Dict								=     {
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		: Optional[Any]								=     [
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		A		: List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)