


'''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      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  1
   lowerCamelCase_      =  3
   lowerCamelCase_      =  (32, 32)

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






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   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=UpperCamelCase			,  only_cross_attention=(True, True, False)			,  num_class_embeds=100			,  )
   return model






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   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     ):







   """simple docstring"""
   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(UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   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(UpperCamelCase     )     ).convert("RGB"     ).resize((64, 64)     )

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

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

   lowerCamelCase_      =  output.images

   lowerCamelCase_      =  torch.Generator(device=UpperCamelCase     ).manual_seed(0     )
   lowerCamelCase_      =  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]

   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([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]     )

   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     ):







   """simple docstring"""
   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(UpperCamelCase     )     ).convert("RGB"     ).resize((64, 64)     )

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

   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=UpperCamelCase     ).manual_seed(0     )
   lowerCamelCase_      =  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"			,  )
   lowerCamelCase_      =  output.images
   assert image.shape[0] == 2







  @unittest.skipIf(torch_device != "cuda"			,  "This test requires a GPU"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   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(UpperCamelCase     )     ).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=UpperCamelCase			,  low_res_scheduler=UpperCamelCase			,  scheduler=UpperCamelCase			,  vae=UpperCamelCase			,  text_encoder=UpperCamelCase			,  tokenizer=UpperCamelCase			,  max_noise_level=350			,  )
   lowerCamelCase_      =  sd_pipe.to(UpperCamelCase     )
   sd_pipe.set_progress_bar_config(disable=UpperCamelCase     )

   lowerCamelCase_      =  "A painting of a squirrel eating a burger"
   lowerCamelCase_      =  torch.manual_seed(0     )
   lowerCamelCase_      =  sd_pipe(
       [prompt]			,  image=UpperCamelCase			,  generator=UpperCamelCase			,  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      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def   snake_case		(    self     ):







   """simple docstring"""
   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(UpperCamelCase     )
   pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   pipe.enable_attention_slicing()

   lowerCamelCase_      =  "a cat sitting on a park bench"

   lowerCamelCase_      =  torch.manual_seed(0     )
   lowerCamelCase_      =  pipe(
       prompt=UpperCamelCase			,  image=UpperCamelCase			,  generator=UpperCamelCase			,  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     ):







   """simple docstring"""
   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(
       UpperCamelCase			,  torch_dtype=torch.floataa			,  )
   pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   pipe.enable_attention_slicing()

   lowerCamelCase_      =  "a cat sitting on a park bench"

   lowerCamelCase_      =  torch.manual_seed(0     )
   lowerCamelCase_      =  pipe(
       prompt=UpperCamelCase			,  image=UpperCamelCase			,  generator=UpperCamelCase			,  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     ):







   """simple docstring"""
   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(
       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()

   lowerCamelCase_      =  "a cat sitting on a park bench"

   lowerCamelCase_      =  torch.manual_seed(0     )
   lowerCamelCase_      =  pipe(
       prompt=UpperCamelCase			,  image=UpperCamelCase			,  generator=UpperCamelCase			,  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 absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_  :				str						= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION			)
class      snake_case    (    datasets.Metric			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   return datasets.MetricInfo(
       description=_DESCRIPTION			,  citation=_CITATION			,  inputs_description=_KWARGS_DESCRIPTION			,  features=datasets.Features(
           {
               "predictions": datasets.Value("string"			,  id="sequence"     ),
               "references": datasets.Value("string"			,  id="sequence"     ),
           }     )			,  codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]			,  reference_urls=[
           "https://en.wikipedia.org/wiki/ROUGE_(metric)",
           "https://github.com/google-research/google-research/tree/master/rouge",
       ]			,  )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

   lowerCamelCase_      =  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase			,  use_stemmer=UpperCamelCase     )
   if use_aggregator:
    lowerCamelCase_      =  scoring.BootstrapAggregator()
   else:
    lowerCamelCase_      =  []

   for ref, pred in zip(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  scorer.score(UpperCamelCase			,  UpperCamelCase     )
    if use_aggregator:
     aggregator.add_scores(UpperCamelCase     )
    else:
     scores.append(UpperCamelCase     )

   if use_aggregator:
    lowerCamelCase_      =  aggregator.aggregate()
   else:
    lowerCamelCase_      =  {}
    for key in scores[0]:
     lowerCamelCase_      =  [score[key] for score in scores]

   return result



