


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     _lowercase				(							lowercase__):


       """simple docstring"""




       def __init__(     self							:    Tuple      ,   __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
             lowerCamelCase__	:       Dict					=						{}
             if idalabel is not None:
                   for key, value in idalabel.items():
                         for label in value.split("," ):
                               lowerCamelCase__	:       Dict					=						int(__lowerCamelCase )
                   lowerCamelCase__	:       str					=						dict(sorted(self.labels.items() ) )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, List[str]] ):


             '''simple docstring'''



             if not isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       Optional[int]					=						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							:    List[Any]      ,   __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'''



             lowerCamelCase__	:       str					=						len(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						self.transformer.config.sample_size
             lowerCamelCase__	:       Tuple					=						self.transformer.config.in_channels

             lowerCamelCase__	:       Union[str, Any]					=						randn_tensor(
                 shape=(batch_size, latent_channels, latent_size, latent_size)      ,   generator=__lowerCamelCase      ,   device=self.device      ,   dtype=self.transformer.dtype      ,   )
             lowerCamelCase__	:       Union[str, Any]					=						torch.cat([latents] * 2 ) if guidance_scale > 1 else latents

             lowerCamelCase__	:       List[Any]					=						torch.tensor(__lowerCamelCase      ,   device=self.device ).reshape(-1 )
             lowerCamelCase__	:       str					=						torch.tensor([1000] * batch_size      ,   device=self.device )
             lowerCamelCase__	:       List[Any]					=						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:
                         lowerCamelCase__	:       int					=						latent_model_input[: len(__lowerCamelCase ) // 2]
                         lowerCamelCase__	:       Tuple					=						torch.cat([half, half]      ,   dim=0 )
                   lowerCamelCase__	:       Union[str, Any]					=						self.scheduler.scale_model_input(__lowerCamelCase      ,   __lowerCamelCase )

                   lowerCamelCase__	:       Optional[int]					=						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+)
                         lowerCamelCase__	:       List[Any]					=						latent_model_input.device.type == "mps"
                         if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                               lowerCamelCase__	:       List[str]					=						torch.floataa if is_mps else torch.floataa
                         else:
                               lowerCamelCase__	:       Any					=						torch.intaa if is_mps else torch.intaa
                         lowerCamelCase__	:       Dict					=						torch.tensor([timesteps]      ,   dtype=__lowerCamelCase      ,   device=latent_model_input.device )
                   elif len(timesteps.shape ) == 0:
                         lowerCamelCase__	:       Any					=						timesteps[None].to(latent_model_input.device )
                   # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                   lowerCamelCase__	:       Any					=						timesteps.expand(latent_model_input.shape[0] )
                   # predict noise model_output
                   lowerCamelCase__	:       List[str]					=						self.transformer(
                       __lowerCamelCase      ,   timestep=__lowerCamelCase      ,   class_labels=__lowerCamelCase ).sample

                   # perform guidance
                   if guidance_scale > 1:
                         lowerCamelCase__	:       Optional[int]					=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                         lowerCamelCase__	:       Dict					=						torch.split(__lowerCamelCase      ,   len(__lowerCamelCase ) // 2      ,   dim=0 )

                         lowerCamelCase__	:       Any					=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                         lowerCamelCase__	:       Optional[Any]					=						torch.cat([half_eps, half_eps]      ,   dim=0 )

                         lowerCamelCase__	:       Tuple					=						torch.cat([eps, rest]      ,   dim=1 )

                   # learned sigma
                   if self.transformer.config.out_channels // 2 == latent_channels:
                         lowerCamelCase__	:       Union[str, Any]					=						torch.split(__lowerCamelCase      ,   __lowerCamelCase      ,   dim=1 )
                   else:
                         lowerCamelCase__	:       str					=						noise_pred

                   # compute previous image: x_t -> x_t-1
                   lowerCamelCase__	:       Dict					=						self.scheduler.step(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase ).prev_sample

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

             lowerCamelCase__	:       Optional[int]					=						1 / self.vae.config.scaling_factor * latents
             lowerCamelCase__	:       Optional[int]					=						self.vae.decode(__lowerCamelCase ).sample

             lowerCamelCase__	:       List[str]					=						(samples / 2 + 0.5).clamp(0      ,   1 )

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

             if output_type == "pil":
                   lowerCamelCase__	:       List[Any]					=						self.numpy_to_pil(__lowerCamelCase )

             if not return_dict:
                   return (samples,)

             return ImagePipelineOutput(images=__lowerCamelCase )






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


A   :					Tuple	=      "\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

A   :					Optional[int]	=      "\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

A   :					str	=      "\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"







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


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''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" ),
                     } )      ,   codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]      ,   reference_urls=[
                     "https://en.wikipedia.org/wiki/BLEU",
                     "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
                 ]      ,   )





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[Any]=4      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						compute_bleu(
                 reference_corpus=__lowerCamelCase      ,   translation_corpus=__lowerCamelCase      ,   max_order=__lowerCamelCase      ,   smooth=__lowerCamelCase )
             ((lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__))	:       List[str]					=						score
             return {
                 "bleu": bleu,
                 "precisions": precisions,
                 "brevity_penalty": bp,
                 "length_ratio": ratio,
                 "translation_length": translation_length,
                 "reference_length": reference_length,
             }


