





'''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  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 @property
 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   3
     _lowerCAmelCase						      =   (32, 32)

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




 @property
 def 				_snake_case (						self		)     ->				Optional[int]:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   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=100      ,  )
     return model




 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				str:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   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=1000      ,  hidden_act="gelu"      ,  projection_dim=512      ,  )
     return CLIPTextModel(_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
     _lowerCAmelCase						      =   self.dummy_cond_unet_upscale
     _lowerCAmelCase						      =   DDPMScheduler()
     _lowerCAmelCase						      =   DDIMScheduler(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   self.dummy_vae
     _lowerCAmelCase						      =   self.dummy_text_encoder
     _lowerCAmelCase						      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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

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

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

     _lowerCAmelCase						      =   output.images

     _lowerCAmelCase						      =   torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
     _lowerCAmelCase						      =   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]

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

     _lowerCAmelCase						      =   low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     _lowerCAmelCase						      =   np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]		)

     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 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
     _lowerCAmelCase						      =   self.dummy_cond_unet_upscale
     _lowerCAmelCase						      =   DDPMScheduler()
     _lowerCAmelCase						      =   DDIMScheduler(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   self.dummy_vae
     _lowerCAmelCase						      =   self.dummy_text_encoder
     _lowerCAmelCase						      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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

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

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

     _lowerCAmelCase						      =   torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
     _lowerCAmelCase						      =   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"      ,  )
     _lowerCAmelCase						      =   output.images
     assert image.shape[0] == 2





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

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

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

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

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

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







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




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




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/sd2-upscale/low_res_cat.png"		)
     _lowerCAmelCase						      =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
         "/upsampled_cat.npy"		)

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

     _lowerCAmelCase						      =   "a cat sitting on a park bench"

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

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




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/sd2-upscale/low_res_cat.png"		)
     _lowerCAmelCase						      =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
         "/upsampled_cat_fp16.npy"		)

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

     _lowerCAmelCase						      =   "a cat sitting on a park bench"

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

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





 def 				_snake_case (						self		)     ->				int:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

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

     _lowerCAmelCase						      =   "stabilityai/stable-diffusion-x4-upscaler"
     _lowerCAmelCase						      =   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()

     _lowerCAmelCase						      =   "a cat sitting on a park bench"

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

     _lowerCAmelCase						      =   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 unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers import AutoModelForCausalLM

@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()
     _lowerCAmelCase						      =   ""
     for new_text in streamer:
         streamer_text += new_text

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   greedy_ids[:, input_ids.shape[1] :]
     _lowerCAmelCase						      =   tokenizer.decode(new_greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_prompt=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
     # with actual models -- the dummy models' tokenizers are not aligned with their models, and
     # `skip_special_tokens=True` has no effect on them
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("distilgpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   torch.ones((1, 5)      ,  device=_lowerCAmelCase		).long() * model.config.bos_token_id
     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_special_tokens=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=1      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)

     # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
     # re-tokenized, must only contain one token
     _lowerCAmelCase						      =   cs.out[:-1]  # Remove the final "\n"
     _lowerCAmelCase						      =   tokenizer(_lowerCAmelCase      ,  return_tensors="pt"		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape      ,  (1, 1)		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase      ,  timeout=0.001		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()

     # The streamer will timeout after 0.001 seconds, so an exception will be raised
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase						      =   ""
         for new_text in streamer:
             streamer_text += new_text


