

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_    :		Union[str, Any]							     =  transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def        _snake_case   (			UpperCAmelCase_     :    str	):







     if isinstance(__UpperCamelCase    ,      torch.Tensor	):
          return image
     elif isinstance(__UpperCamelCase    ,      PIL.Image.Image	):
          A__								=     [image]

     A__								=     [trans(img.convert("""RGB"""	)	) for img in image]
     A__								=     torch.stack(__UpperCamelCase	)
     return image



class     a  (				_lowerCAmelCase ):



    """simple docstring"""



    def __init__(   self:       Tuple			,  UpperCamelCase:       Dict			,  UpperCamelCase:       Union[str, Any]				):






         """simple docstring"""


         super().__init__()

         # make sure scheduler can always be converted to DDIM
         A__								=     DDIMScheduler.from_config(scheduler.config				)

         self.register_modules(unet=_lowerCamelCase			,  scheduler=_lowerCamelCase				)

    def  UpperCamelCase		(   self:       Tuple			,  UpperCamelCase:       Dict				):






         """simple docstring"""


         if strength < 0 or strength > 1:
              raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""				)

    def  UpperCamelCase		(   self:       Dict			,  UpperCamelCase:       Any			,  UpperCamelCase:       Dict			,  UpperCamelCase:       Optional[Any]				):






         """simple docstring"""


         A__								=     min(int(num_inference_steps * strength				)			,  _lowerCamelCase				)

         A__								=     max(num_inference_steps - init_timestep			,  0				)
         A__								=     self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start

    def  UpperCamelCase		(   self:       Dict			,  UpperCamelCase:       Dict			,  UpperCamelCase:       Union[str, Any]			,  UpperCamelCase:       Dict			,  UpperCamelCase:       str			,  UpperCamelCase:       str			,  UpperCamelCase:       List[str]=None				):






         """simple docstring"""


         if not isinstance(_lowerCamelCase			,  (torch.Tensor, PIL.Image.Image, list)				):
              raise ValueError(
                  f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase				)}"""				)

         A__								=     image.to(device=_lowerCamelCase			,  dtype=_lowerCamelCase				)

         if isinstance(_lowerCamelCase			,  _lowerCamelCase				) and len(_lowerCamelCase				) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(_lowerCamelCase				)}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

         A__								=     init_latents.shape
         A__								=     randn_tensor(_lowerCamelCase			,  generator=_lowerCamelCase			,  device=_lowerCamelCase			,  dtype=_lowerCamelCase				)

         # get latents
         print("""add noise to latents at timestep"""			,  _lowerCamelCase				)
         A__								=     self.scheduler.add_noise(_lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase				)
         A__								=     init_latents

         return latents





    @torch.no_grad()
    def __call__(   self:       Dict			,  UpperCamelCase:       Union[torch.FloatTensor, PIL.Image.Image] = None			,  UpperCamelCase:       float = 0.8			,  UpperCamelCase:       int = 1			,  UpperCamelCase:       Optional[Union[torch.Generator, List[torch.Generator]]] = None			,  UpperCamelCase:       float = 0.0			,  UpperCamelCase:       int = 50			,  UpperCamelCase:       Optional[bool] = None			,  UpperCamelCase:       Optional[str] = "pil"			,  UpperCamelCase:       bool = True			,  ):






         """simple docstring"""


         self.check_inputs(_lowerCamelCase				)

         # 2. Preprocess image
         A__								=     preprocess(_lowerCamelCase				)

         # 3. set timesteps
         self.scheduler.set_timesteps(_lowerCamelCase			,  device=self.device				)
         A__	, A__								=     self.get_timesteps(_lowerCamelCase			,  _lowerCamelCase			,  self.device				)
         A__								=     timesteps[:1].repeat(_lowerCamelCase				)

         # 4. Prepare latent variables
         A__								=     self.prepare_latents(_lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase			,  self.unet.dtype			,  self.device			,  _lowerCamelCase				)
         A__								=     latents

         # 5. Denoising loop
         for t in self.progress_bar(_lowerCamelCase				):
              # 1. predict noise model_output
              A__								=     self.unet(_lowerCamelCase			,  _lowerCamelCase				).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              A__								=     self.scheduler.step(
                  _lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase			,  eta=_lowerCamelCase			,  use_clipped_model_output=_lowerCamelCase			,  generator=_lowerCamelCase			,  ).prev_sample

         A__								=     (image / 2 + 0.5).clamp(0			,  1				)
         A__								=     image.cpu().permute(0			,  2			,  3			,  1				).numpy()
         if output_type == "pil":
              A__								=     self.numpy_to_pil(_lowerCamelCase				)

         if not return_dict:
              return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_lowerCamelCase				)














"""simple docstring"""


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowercase_				     =     '\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

lowercase_				     =     '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

lowercase_				     =     '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'


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


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     return datasets.MetricInfo(
         description=_DESCRIPTION,       citation=_CITATION,       inputs_description=_KWARGS_DESCRIPTION,       features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''string''',       id='''token'''  ),       id='''sequence'''  ),
                 '''references''': datasets.Sequence(
                     datasets.Sequence(datasets.Value('''string''',       id='''token'''  ),       id='''sequence'''  ),       id='''references'''  ),
             }  ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     List[List[List[str]]],       _lowerCamelCase :     List[List[str]],       _lowerCamelCase :     int = 1,       _lowerCamelCase :     int = 4,       ):

     '''simple docstring'''
     return {
         "google_bleu": gleu_score.corpus_gleu(
             list_of_references=_lowerCamelCase,       hypotheses=_lowerCamelCase,       min_len=_lowerCamelCase,       max_len=_lowerCamelCase  )
     }




