






"""simple docstring"""


from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Any,       _lowerCamelCase :     TransformeraDModel,       _lowerCamelCase :     AutoencoderKL,       _lowerCamelCase :     KarrasDiffusionSchedulers,       _lowerCamelCase :     Optional[Dict[int, str]] = None,       ):

     '''simple docstring'''
     super().__init__()
     self.register_modules(transformer=_lowerCamelCase,       vae=_lowerCamelCase,       scheduler=_lowerCamelCase  )

     # create a imagenet -> id dictionary for easier use
     __A									= {}
     if idalabel is not None:
       for key, value in idalabel.items():
         for label in value.split(''','''  ):
           __A									= int(_lowerCamelCase  )
       __A									= dict(sorted(self.labels.items()  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     Union[str, List[str]]  ):

     '''simple docstring'''

     if not isinstance(_lowerCamelCase,       _lowerCamelCase  ):
       __A									= list(_lowerCamelCase  )

     for l in label:
       if l not in self.labels:
         raise ValueError(
             f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'  )

     return [self.labels[l] for l in label]






   @torch.no_grad()
   def __call__(	self :     Optional[int],       _lowerCamelCase :     List[int],       _lowerCamelCase :     float = 4.0,       _lowerCamelCase :     Optional[Union[torch.Generator, List[torch.Generator]]] = None,       _lowerCamelCase :     int = 50,       _lowerCamelCase :     Optional[str] = "pil",       _lowerCamelCase :     bool = True,       ):

     '''simple docstring'''
     __A									= len(_lowerCamelCase  )
     __A									= self.transformer.config.sample_size
     __A									= self.transformer.config.in_channels

     __A									= randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size),       generator=_lowerCamelCase,       device=self.device,       dtype=self.transformer.dtype,       )
     __A									= torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

     __A									= torch.tensor(_lowerCamelCase,       device=self.device  ).reshape(-1  )
     __A									= torch.tensor([10_00] * batch_size,       device=self.device  )
     __A									= torch.cat([class_labels, class_null],       0  ) if guidance_scale > 1 else class_labels

     # set step values
     self.scheduler.set_timesteps(_lowerCamelCase  )

     for t in self.progress_bar(self.scheduler.timesteps  ):
       if guidance_scale > 1:
         __A									= latent_model_input[: len(_lowerCamelCase  ) // 2]
         __A									= torch.cat([half, half],       dim=0  )
       __A									= self.scheduler.scale_model_input(_lowerCamelCase,       _lowerCamelCase  )

       __A									= t
       if not torch.is_tensor(_lowerCamelCase  ):
         # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
         # This would be a good case for the `match` statement (Python 3.10+)
         __A									= latent_model_input.device.type == '''mps'''
         if isinstance(_lowerCamelCase,       _lowerCamelCase  ):
           __A									= torch.floataa if is_mps else torch.floataa
         else:
           __A									= torch.intaa if is_mps else torch.intaa
         __A									= torch.tensor([timesteps],       dtype=_lowerCamelCase,       device=latent_model_input.device  )
       elif len(timesteps.shape  ) == 0:
         __A									= timesteps[None].to(latent_model_input.device  )
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       __A									= timesteps.expand(latent_model_input.shape[0]  )
       # predict noise model_output
       __A									= self.transformer(
           _lowerCamelCase,       timestep=_lowerCamelCase,       class_labels=_lowerCamelCase  ).sample

       # perform guidance
       if guidance_scale > 1:
         __A			,    __A									= noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
         __A			,    __A									= torch.split(_lowerCamelCase,       len(_lowerCamelCase  ) // 2,       dim=0  )

         __A									= uncond_eps + guidance_scale * (cond_eps - uncond_eps)
         __A									= torch.cat([half_eps, half_eps],       dim=0  )

         __A									= torch.cat([eps, rest],       dim=1  )

       # learned sigma
       if self.transformer.config.out_channels // 2 == latent_channels:
         __A			,    __A									= torch.split(_lowerCamelCase,       _lowerCamelCase,       dim=1  )
       else:
         __A									= noise_pred

       # compute previous image: x_t -> x_t-1
       __A									= self.scheduler.step(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase  ).prev_sample

     if guidance_scale > 1:
       __A			,    __A									= latent_model_input.chunk(2,       dim=0  )
     else:
       __A									= latent_model_input

     __A									= 1 / self.vae.config.scaling_factor * latents
     __A									= self.vae.decode(_lowerCamelCase  ).sample

     __A									= (samples / 2 + 0.5).clamp(0,       1  )

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     __A									= samples.cpu().permute(0,       2,       3,       1  ).float().numpy()

     if output_type == "pil":
       __A									= self.numpy_to_pil(_lowerCamelCase  )

     if not return_dict:
       return (samples,)

     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  )
     }




