
"""simple docstring"""






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 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :int					)							->			List[Any]:







			'''simple docstring'''




			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	@property
	def   __lowerCAmelCase (						self    :Any					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= 1
			SCREAMING_SNAKE_CASE       :     Any				= 3
			SCREAMING_SNAKE_CASE       :     str				= (32, 32)

			SCREAMING_SNAKE_CASE       :     List[Any]				= floats_tensor((batch_size, num_channels) + sizes		, rng=random.Random(0					)					).to(lowerCamelCase_					)
			return image





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''simple docstring'''




			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Any				= 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=lowerCamelCase_		, only_cross_attention=(True, True, False)		, num_class_embeds=1_00		, )
			return model





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			str:







			'''simple docstring'''




			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Dict				= 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   __lowerCAmelCase (						self    :Tuple					)							->			Optional[Any]:







			'''simple docstring'''




			torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Optional[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=5_12		, )
			return CLIPTextModel(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Tuple					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
			SCREAMING_SNAKE_CASE       :     Tuple				= self.dummy_cond_unet_upscale
			SCREAMING_SNAKE_CASE       :     int				= DDPMScheduler()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= DDIMScheduler(prediction_type='''v_prediction'''					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.dummy_vae
			SCREAMING_SNAKE_CASE       :     Tuple				= self.dummy_text_encoder
			SCREAMING_SNAKE_CASE       :     List[Any]				= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= self.dummy_image.cpu().permute(0		, 2		, 3		, 1					)[0]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= Image.fromarray(np.uinta(lowerCamelCase_					)					).convert('''RGB'''					).resize((64, 64)					)

			# make sure here that pndm scheduler skips prk
			SCREAMING_SNAKE_CASE       :     int				= StableDiffusionUpscalePipeline(
			    unet=lowerCamelCase_		, low_res_scheduler=lowerCamelCase_		, scheduler=lowerCamelCase_		, vae=lowerCamelCase_		, text_encoder=lowerCamelCase_		, tokenizer=lowerCamelCase_		, max_noise_level=3_50		, )
			SCREAMING_SNAKE_CASE       :     str				= sd_pipe.to(lowerCamelCase_					)
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''A painting of a squirrel eating a burger'''
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= sd_pipe(
			    [prompt]		, image=lowerCamelCase_		, generator=lowerCamelCase_		, guidance_scale=6.0		, noise_level=20		, num_inference_steps=2		, output_type='''np'''		, )

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= output.images

			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Tuple				= sd_pipe(
			    [prompt]		, image=lowerCamelCase_		, generator=lowerCamelCase_		, guidance_scale=6.0		, noise_level=20		, num_inference_steps=2		, output_type='''np'''		, return_dict=lowerCamelCase_		, )[0]

			SCREAMING_SNAKE_CASE       :     str				= image[0, -3:, -3:, -1]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= image_from_tuple[0, -3:, -3:, -1]

			SCREAMING_SNAKE_CASE       :     List[str]				= low_res_image.size[0] * 4
			assert image.shape == (1, expected_height_width, expected_height_width, 3)
			SCREAMING_SNAKE_CASE       :     List[Any]				= np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]					)

			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   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.dummy_cond_unet_upscale
			SCREAMING_SNAKE_CASE       :     Any				= DDPMScheduler()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= DDIMScheduler(prediction_type='''v_prediction'''					)
			SCREAMING_SNAKE_CASE       :     List[str]				= self.dummy_vae
			SCREAMING_SNAKE_CASE       :     int				= self.dummy_text_encoder
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.dummy_image.cpu().permute(0		, 2		, 3		, 1					)[0]
			SCREAMING_SNAKE_CASE       :     List[Any]				= Image.fromarray(np.uinta(lowerCamelCase_					)					).convert('''RGB'''					).resize((64, 64)					)

			# make sure here that pndm scheduler skips prk
			SCREAMING_SNAKE_CASE       :     int				= StableDiffusionUpscalePipeline(
			    unet=lowerCamelCase_		, low_res_scheduler=lowerCamelCase_		, scheduler=lowerCamelCase_		, vae=lowerCamelCase_		, text_encoder=lowerCamelCase_		, tokenizer=lowerCamelCase_		, max_noise_level=3_50		, )
			SCREAMING_SNAKE_CASE       :     Any				= sd_pipe.to(lowerCamelCase_					)
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= '''A painting of a squirrel eating a burger'''
			SCREAMING_SNAKE_CASE       :     List[str]				= sd_pipe(
			    2 * [prompt]		, image=2 * [low_res_image]		, guidance_scale=6.0		, noise_level=20		, num_inference_steps=2		, output_type='''np'''		, )
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= output.images
			assert image.shape[0] == 2

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= sd_pipe(
			    [prompt]		, image=lowerCamelCase_		, generator=lowerCamelCase_		, num_images_per_prompt=2		, guidance_scale=6.0		, noise_level=20		, num_inference_steps=2		, output_type='''np'''		, )
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= output.images
			assert image.shape[0] == 2







	@unittest.skipIf(torch_device != '''cuda'''		, '''This test requires a GPU'''					)
	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.dummy_cond_unet_upscale
			SCREAMING_SNAKE_CASE       :     Dict				= DDPMScheduler()
			SCREAMING_SNAKE_CASE       :     str				= DDIMScheduler(prediction_type='''v_prediction'''					)
			SCREAMING_SNAKE_CASE       :     int				= self.dummy_vae
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.dummy_text_encoder
			SCREAMING_SNAKE_CASE       :     Any				= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

			SCREAMING_SNAKE_CASE       :     str				= self.dummy_image.cpu().permute(0		, 2		, 3		, 1					)[0]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= Image.fromarray(np.uinta(lowerCamelCase_					)					).convert('''RGB'''					).resize((64, 64)					)

			# put models in fp16, except vae as it overflows in fp16
			SCREAMING_SNAKE_CASE       :     List[str]				= unet.half()
			SCREAMING_SNAKE_CASE       :     List[Any]				= text_encoder.half()

			# make sure here that pndm scheduler skips prk
			SCREAMING_SNAKE_CASE       :     int				= StableDiffusionUpscalePipeline(
			    unet=lowerCamelCase_		, low_res_scheduler=lowerCamelCase_		, scheduler=lowerCamelCase_		, vae=lowerCamelCase_		, text_encoder=lowerCamelCase_		, tokenizer=lowerCamelCase_		, max_noise_level=3_50		, )
			SCREAMING_SNAKE_CASE       :     Any				= sd_pipe.to(lowerCamelCase_					)
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= '''A painting of a squirrel eating a burger'''
			SCREAMING_SNAKE_CASE       :     Any				= torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Dict				= sd_pipe(
			    [prompt]		, image=lowerCamelCase_		, generator=lowerCamelCase_		, num_inference_steps=2		, output_type='''np'''		, ).images

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





@slow
@require_torch_gpu
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[Any]:







			'''simple docstring'''




			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def   __lowerCAmelCase (						self    :List[Any]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
			    '''/sd2-upscale/low_res_cat.png'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
			    '''/upsampled_cat.npy'''					)

			SCREAMING_SNAKE_CASE       :     Dict				= '''stabilityai/stable-diffusion-x4-upscaler'''
			SCREAMING_SNAKE_CASE       :     Tuple				= StableDiffusionUpscalePipeline.from_pretrained(lowerCamelCase_					)
			pipe.to(lowerCamelCase_					)
			pipe.set_progress_bar_config(disable=lowerCamelCase_					)
			pipe.enable_attention_slicing()

			SCREAMING_SNAKE_CASE       :     Dict				= '''a cat sitting on a park bench'''

			SCREAMING_SNAKE_CASE       :     str				= torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Any				= pipe(
			    prompt=lowerCamelCase_		, image=lowerCamelCase_		, generator=lowerCamelCase_		, output_type='''np'''		, )
			SCREAMING_SNAKE_CASE       :     Dict				= output.images[0]

			assert image.shape == (5_12, 5_12, 3)
			assert np.abs(expected_image - image					).max() < 1E-3





	def   __lowerCAmelCase (						self    :Any					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
			    '''/sd2-upscale/low_res_cat.png'''					)
			SCREAMING_SNAKE_CASE       :     int				= load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
			    '''/upsampled_cat_fp16.npy'''					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= '''stabilityai/stable-diffusion-x4-upscaler'''
			SCREAMING_SNAKE_CASE       :     Any				= StableDiffusionUpscalePipeline.from_pretrained(
			    lowerCamelCase_		, torch_dtype=torch.floataa		, )
			pipe.to(lowerCamelCase_					)
			pipe.set_progress_bar_config(disable=lowerCamelCase_					)
			pipe.enable_attention_slicing()

			SCREAMING_SNAKE_CASE       :     Tuple				= '''a cat sitting on a park bench'''

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pipe(
			    prompt=lowerCamelCase_		, image=lowerCamelCase_		, generator=lowerCamelCase_		, output_type='''np'''		, )
			SCREAMING_SNAKE_CASE       :     Tuple				= output.images[0]

			assert image.shape == (5_12, 5_12, 3)
			assert np.abs(expected_image - image					).max() < 5E-1







	def   __lowerCAmelCase (						self    :Tuple					)							->			Dict:







			'''simple docstring'''




			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

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

			SCREAMING_SNAKE_CASE       :     List[str]				= '''stabilityai/stable-diffusion-x4-upscaler'''
			SCREAMING_SNAKE_CASE       :     List[Any]				= StableDiffusionUpscalePipeline.from_pretrained(
			    lowerCamelCase_		, torch_dtype=torch.floataa		, )
			pipe.to(lowerCamelCase_					)
			pipe.set_progress_bar_config(disable=lowerCamelCase_					)
			pipe.enable_attention_slicing(1					)
			pipe.enable_sequential_cpu_offload()

			SCREAMING_SNAKE_CASE       :     Optional[int]				= '''a cat sitting on a park bench'''

			SCREAMING_SNAKE_CASE       :     str				= torch.manual_seed(0					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= pipe(
			    prompt=lowerCamelCase_		, image=lowerCamelCase_		, generator=lowerCamelCase_		, num_inference_steps=5		, output_type='''np'''		, )

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







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__ :       Optional[Any]						     =			{
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Optional[Any]						     =			["FunnelTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Dict						     =			[
					    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FunnelBaseModel",
					    "FunnelForMaskedLM",
					    "FunnelForMultipleChoice",
					    "FunnelForPreTraining",
					    "FunnelForQuestionAnswering",
					    "FunnelForSequenceClassification",
					    "FunnelForTokenClassification",
					    "FunnelModel",
					    "FunnelPreTrainedModel",
					    "load_tf_weights_in_funnel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Tuple						     =			[
					    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFFunnelBaseModel",
					    "TFFunnelForMaskedLM",
					    "TFFunnelForMultipleChoice",
					    "TFFunnelForPreTraining",
					    "TFFunnelForQuestionAnswering",
					    "TFFunnelForSequenceClassification",
					    "TFFunnelForTokenClassification",
					    "TFFunnelModel",
					    "TFFunnelPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
					from .tokenization_funnel import FunnelTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_funnel_fast import FunnelTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_funnel import (
										    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FunnelBaseModel,
										    FunnelForMaskedLM,
										    FunnelForMultipleChoice,
										    FunnelForPreTraining,
										    FunnelForQuestionAnswering,
										    FunnelForSequenceClassification,
										    FunnelForTokenClassification,
										    FunnelModel,
										    FunnelPreTrainedModel,
										    load_tf_weights_in_funnel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_funnel import (
										    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFunnelBaseModel,
										    TFFunnelForMaskedLM,
										    TFFunnelForMultipleChoice,
										    TFFunnelForPreTraining,
										    TFFunnelForQuestionAnswering,
										    TFFunnelForSequenceClassification,
										    TFFunnelForTokenClassification,
										    TFFunnelModel,
										    TFFunnelPreTrainedModel,
										)

else:
					import sys

					lowerCamelCase__ :       Optional[int]						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





