






'''simple docstring'''
from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
__lowerCAmelCase							='\\n'

__lowerCAmelCase							='\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

__lowerCAmelCase							='\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions 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    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

__lowerCAmelCase							=['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
__lowerCAmelCase							=''

try:
						from jiwer import transforms as tr

						__lowerCAmelCase							=True
except ImportError:
						__lowerCAmelCase							=False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):


						class    _snake_case   (				tr.AbstractTransform					):

											"""simple docstring"""





											def __init__(     self   , UpperCAmelCase__ = " "     )      ->	Dict:

																'''simple docstring'''





																a_									=   sentence_delimiter




											def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:

																'''simple docstring'''





																return list(UpperCAmelCase__     )




											def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	str:

																'''simple docstring'''





																a_									=   []
																for sent_idx, sentence in enumerate(UpperCAmelCase__     ):
																					chars.extend(self.process_string(UpperCAmelCase__     )     )
																					if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase__     ) - 1:
																										chars.append(self.sentence_delimiter     )
																return chars



						__lowerCAmelCase							=tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
elif _jiwer_available:
						__lowerCAmelCase							=tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)
else:
						__lowerCAmelCase							=None

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[Any]:






					"""simple docstring"""







					return float((preds == labels).mean() )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Dict:






					"""simple docstring"""







					return {
					    "f1": float(fa_score(y_true=_A					,  y_pred=_A					,  average='macro' ) ),
					    "accuracy": simple_accuracy(_A					,  _A ),
					}

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase="exp"					,  _UpperCAmelCase=None					,  _UpperCAmelCase=False					,  _UpperCAmelCase=False					,  _UpperCAmelCase=None					,  _UpperCAmelCase=False					,  )				->				List[Any]:






					"""simple docstring"""







					a_									=   [[label] for label in labels]
					a_									=   list(_A )
					try:
										import sacrebleu as scb
					except ImportError:
										raise ValueError(
										    'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
										    'You can install it via `pip install sacrebleu`.' )

					if version.parse(scb.__version__ ) < version.parse('1.4.12' ):
										raise ImportWarning(
										    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
										    'You can install it with `pip install \"sacrebleu>=1.4.12\"`.' )

					a_									=   len(labels[0] )
					if any(len(_A ) != references_per_prediction for refs in labels ):
										raise ValueError('Sacrebleu requires the same number of references for each prediction' )
					a_									=   [[refs[i] for refs in labels] for i in range(_A )]
					a_									=   scb.corpus_bleu(
					    _A					,  _A					,  smooth_method=_A					,  smooth_value=_A					,  force=_A					,  lowercase=_A					,  use_effective_order=_A					,  **({'tokenize': tokenize} if tokenize else {})					,  )
					return {"bleu": output.score}

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[Any]:






					"""simple docstring"""







					try:
										from jiwer import compute_measures
					except ImportError:
										raise ValueError(
										    F'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
										    'You can install it via `pip install jiwer`.' )

					if concatenate_texts:
										a_									=   compute_measures(_A					,  _A )['wer']

										a_									=   compute_measures(_A					,  _A					,  truth_transform=_A					,  hypothesis_transform=_A )['wer']
										return {"wer": wer, "cer": cer}
					else:

										def compute_score(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase="wer" ):
															a_									=   0
															a_									=   0
															for prediction, reference in zip(_A					,  _A ):
																				if score_type == "wer":
																									a_									=   compute_measures(_A					,  _A )
																				elif score_type == "cer":
																									a_									=   compute_measures(
																									    _A					,  _A					,  truth_transform=_A					,  hypothesis_transform=_A )
																				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																				total += measures["substitutions"] + measures["deletions"] + measures["hits"]
															return incorrect / total

										return {"wer": compute_score(_A					,  _A					,  'wer' ), "cer": compute_score(_A					,  _A					,  'cer' )}





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

					"""simple docstring"""





					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:

										'''simple docstring'''





										if self.config_name not in _CONFIG_NAMES:
															raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''     )

										a_									=   'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

										return datasets.MetricInfo(
										    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
										        {'predictions': datasets.Value(UpperCAmelCase__     ), 'references': datasets.Value(UpperCAmelCase__     )}     )   , codebase_urls=[]   , reference_urls=[]   , format='numpy'   , )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__=None   , UpperCAmelCase__=None     )      ->	str:

										'''simple docstring'''





										a_									=   bleu_kwargs if bleu_kwargs is not None else {}
										a_									=   wer_kwargs if wer_kwargs is not None else {}

										if self.config_name == "fleurs-lang_id":
															return {"accuracy": simple_accuracy(UpperCAmelCase__   , UpperCAmelCase__     )}
										elif self.config_name == "minds14":
															return fa_and_simple_accuracy(UpperCAmelCase__   , UpperCAmelCase__     )
										elif self.config_name == "covost2":
															a_									=   bleu_kwargs.pop('smooth_method'   , 'exp'     )
															a_									=   bleu_kwargs.pop('smooth_value'   , UpperCAmelCase__     )
															a_									=   bleu_kwargs.pop('force'   , UpperCAmelCase__     )
															a_									=   bleu_kwargs.pop('lowercase'   , UpperCAmelCase__     )
															a_									=   bleu_kwargs.pop('tokenize'   , UpperCAmelCase__     )
															a_									=   bleu_kwargs.pop('use_effective_order'   , UpperCAmelCase__     )
															return bleu(
															    preds=UpperCAmelCase__   , labels=UpperCAmelCase__   , smooth_method=UpperCAmelCase__   , smooth_value=UpperCAmelCase__   , force=UpperCAmelCase__   , lowercase=UpperCAmelCase__   , tokenize=UpperCAmelCase__   , use_effective_order=UpperCAmelCase__   , )
										elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
															a_									=   wer_kwargs.pop('concatenate_texts'   , UpperCAmelCase__     )
															return wer_and_cer(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , self.config_name     )
										else:
															raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''     )









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

