






'''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     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 @property
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							1
       UpperCamelCase_				:   List[str]					    =							3
       UpperCamelCase_				:   List[Any]					    =							(32, 32)

       UpperCamelCase_				:   Union[str, Any]					    =							floats_tensor((batch_size, num_channels) + sizes			,     rng=random.Random(0 ) ).to(__lowerCAmelCase )
       return image
 @property
 def   _UpperCAmelCase       (							self ):
       torch.manual_seed(0 )
       UpperCamelCase_				:   List[str]					    =							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   _UpperCAmelCase       (							self ):
       torch.manual_seed(0 )
       UpperCamelCase_				:   Optional[Any]					    =							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 )
       UpperCamelCase_				:   List[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   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase_				:   List[str]					    =							self.dummy_cond_unet_upscale
       UpperCamelCase_				:   str					    =							DDPMScheduler()
       UpperCamelCase_				:   Dict					    =							DDIMScheduler(prediction_type="""v_prediction""" )
       UpperCamelCase_				:   Optional[int]					    =							self.dummy_vae
       UpperCamelCase_				:   Dict					    =							self.dummy_text_encoder
       UpperCamelCase_				:   Dict					    =							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

       UpperCamelCase_				:   List[Any]					    =							self.dummy_image.cpu().permute(0			,     2			,     3			,     1 )[0]
       UpperCamelCase_				:   List[str]					    =							Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert("""RGB""" ).resize((64, 64) )

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

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

       UpperCamelCase_				:   int					    =							output.images

       UpperCamelCase_				:   Tuple					    =							torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )
       UpperCamelCase_				:   List[str]					    =							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]

       UpperCamelCase_				:   List[Any]					    =							image[0, -3:, -3:, -1]
       UpperCamelCase_				:   int					    =							image_from_tuple[0, -3:, -3:, -1]

       UpperCamelCase_				:   Optional[Any]					    =							low_res_image.size[0] * 4
       assert image.shape == (1, expected_height_width, expected_height_width, 3)
       UpperCamelCase_				:   Union[str, Any]					    =							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 ):
       UpperCamelCase_				:   Any					    =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase_				:   str					    =							self.dummy_cond_unet_upscale
       UpperCamelCase_				:   List[Any]					    =							DDPMScheduler()
       UpperCamelCase_				:   Tuple					    =							DDIMScheduler(prediction_type="""v_prediction""" )
       UpperCamelCase_				:   str					    =							self.dummy_vae
       UpperCamelCase_				:   List[str]					    =							self.dummy_text_encoder
       UpperCamelCase_				:   Optional[int]					    =							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

       UpperCamelCase_				:   int					    =							self.dummy_image.cpu().permute(0			,     2			,     3			,     1 )[0]
       UpperCamelCase_				:   Tuple					    =							Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert("""RGB""" ).resize((64, 64) )

       # make sure here that pndm scheduler skips prk
       UpperCamelCase_				:   Tuple					    =							StableDiffusionUpscalePipeline(
           unet=__lowerCAmelCase			,     low_res_scheduler=__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     vae=__lowerCAmelCase			,     text_encoder=__lowerCAmelCase			,     tokenizer=__lowerCAmelCase			,     max_noise_level=3_50			,     )
       UpperCamelCase_				:   List[str]					    =							sd_pipe.to(__lowerCAmelCase )
       sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

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

       UpperCamelCase_				:   Any					    =							torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )
       UpperCamelCase_				:   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"""			,     )
       UpperCamelCase_				:   str					    =							output.images
       assert image.shape[0] == 2





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

       UpperCamelCase_				:   Tuple					    =							self.dummy_image.cpu().permute(0			,     2			,     3			,     1 )[0]
       UpperCamelCase_				:   Optional[int]					    =							Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert("""RGB""" ).resize((64, 64) )

       # put models in fp16, except vae as it overflows in fp16
       UpperCamelCase_				:   Any					    =							unet.half()
       UpperCamelCase_				:   Optional[int]					    =							text_encoder.half()

       # make sure here that pndm scheduler skips prk
       UpperCamelCase_				:   Optional[Any]					    =							StableDiffusionUpscalePipeline(
           unet=__lowerCAmelCase			,     low_res_scheduler=__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     vae=__lowerCAmelCase			,     text_encoder=__lowerCAmelCase			,     tokenizer=__lowerCAmelCase			,     max_noise_level=3_50			,     )
       UpperCamelCase_				:   List[str]					    =							sd_pipe.to(__lowerCAmelCase )
       sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   Any					    =							"""A painting of a squirrel eating a burger"""
       UpperCamelCase_				:   Optional[int]					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   str					    =							sd_pipe(
           [prompt]			,     image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     num_inference_steps=2			,     output_type="""np"""			,     ).images

       UpperCamelCase_				:   Optional[Any]					    =							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						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-upscale/low_res_cat.png""" )
       UpperCamelCase_				:   Optional[Any]					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
           """/upsampled_cat.npy""" )

       UpperCamelCase_				:   int					    =							"""stabilityai/stable-diffusion-x4-upscaler"""
       UpperCamelCase_				:   List[Any]					    =							StableDiffusionUpscalePipeline.from_pretrained(__lowerCAmelCase )
       pipe.to(__lowerCAmelCase )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )
       pipe.enable_attention_slicing()

       UpperCamelCase_				:   Union[str, Any]					    =							"""a cat sitting on a park bench"""

       UpperCamelCase_				:   int					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   int					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )
       UpperCamelCase_				:   Optional[Any]					    =							output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 1E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-upscale/low_res_cat.png""" )
       UpperCamelCase_				:   Tuple					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
           """/upsampled_cat_fp16.npy""" )

       UpperCamelCase_				:   str					    =							"""stabilityai/stable-diffusion-x4-upscaler"""
       UpperCamelCase_				:   Tuple					    =							StableDiffusionUpscalePipeline.from_pretrained(
           __lowerCAmelCase			,     torch_dtype=torch.floataa			,     )
       pipe.to(__lowerCAmelCase )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )
       pipe.enable_attention_slicing()

       UpperCamelCase_				:   Union[str, Any]					    =							"""a cat sitting on a park bench"""

       UpperCamelCase_				:   str					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   Optional[Any]					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )
       UpperCamelCase_				:   Union[str, Any]					    =							output.images[0]

       assert image.shape == (5_12, 5_12, 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()

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

       UpperCamelCase_				:   Union[str, Any]					    =							"""stabilityai/stable-diffusion-x4-upscaler"""
       UpperCamelCase_				:   Union[str, 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()

       UpperCamelCase_				:   List[Any]					    =							"""a cat sitting on a park bench"""

       UpperCamelCase_				:   int					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   Any					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     num_inference_steps=5			,     output_type="""np"""			,     )

       UpperCamelCase_				:   List[str]					    =							torch.cuda.max_memory_allocated()
       # make sure that less than 2.9 GB is allocated
       assert mem_bytes < 2.9 * 10**9














'''simple docstring'''

import warnings

from .generation import TFGenerationMixin




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 warnings.warn(
     '''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''',   SCREAMING_SNAKE_CASE__,   )






