



import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowercase:				Optional[Any]       =	logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")






def 					a(   A :					Tuple				,  A :					Tuple				,  A :					List[str]=0 )	-> Dict:
					"""simple docstring"""





					a        =     (size, size) if isinstance(A				,  A ) else size
					a        =     img.size
					a        =     size[1] - original_height if original_height < size[1] else 0
					a        =     size[0] - original_width if original_width < size[0] else 0
					a        =     functional.pad(A				,  (0, 0, pad_width, pad_height)				,  fill=A )
					return img



class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     transforms




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						for t in self.transforms:
											a        =     t(_A	, _A	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	):
						"""simple docstring"""

						pass




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     size




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     functional.resize(_A	, self.size	)
						a        =     functional.resize(_A	, self.size	, interpolation=transforms.InterpolationMode.NEAREST	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=None	):
						"""simple docstring"""

						a        =     min_size
						if max_size is None:
											a        =     min_size
						a        =     max_size




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     random.randint(self.min_size	, self.max_size	)
						a        =     functional.resize(_A	, _A	)
						a        =     functional.resize(_A	, _A	, interpolation=transforms.InterpolationMode.NEAREST	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     size if isinstance(_A	, _A	) else (size, size)




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     pad_if_smaller(_A	, self.size	)
						a        =     pad_if_smaller(_A	, self.size	, fill=255	)
						a        =     transforms.RandomCrop.get_params(_A	, self.size	)
						a        =     functional.crop(_A	, *_A	)
						a        =     functional.crop(_A	, *_A	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     flip_prob




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						if random.random() < self.flip_prob:
											a        =     functional.hflip(_A	)
											a        =     functional.hflip(_A	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     functional.pil_to_tensor(_A	)
						a        =     torch.as_tensor(np.array(_A	)	, dtype=torch.intaa	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     dtype




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     functional.convert_image_dtype(_A	, self.dtype	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     mean
						a        =     std




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     functional.normalize(_A	, mean=self.mean	, std=self.std	)
						return image, target




class 			_lowercase							:





	"""simple docstring"""




	def __call__(self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						if not isinstance(_A	, np.ndarray	):
											a        =     np.array(_A	).astype(np.uinta	)
						# avoid using underflow conversion
						a        =     255
						a        =     target - 1
						a        =     255

						a        =     Image.fromarray(_A	)
						return image, target




@dataclass
class 			_lowercase							:





	"""simple docstring"""
	__A					=							field(
	    default="segments/sidewalk-semantic",   metadata={
	        "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
	    },   )
	__A					=							field(
	    default=lowercase__,   metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
	__A					=							field(
	    default=0.15,   metadata={"help": "Percent to split off of train for validation."}			)
	__A					=							field(
	    default=lowercase__,   metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    },   )
	__A					=							field(
	    default=lowercase__,   metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    },   )
	__A					=							field(
	    default=lowercase__,   metadata={"help": "Whether or not to reduce all labels by 1 and replace background by 255."},   )




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
											raise ValueError(
											    "You must specify either a dataset name from the hub or a train and/or validation directory."	)




@dataclass
class 			_lowercase							:





	"""simple docstring"""
	__A					=							field(
	    default="nvidia/mit-b0",   metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},   )
	__A					=							field(
	    default=lowercase__,   metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
	__A					=							field(
	    default=lowercase__,   metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}			)
	__A					=							field(
	    default="main",   metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},   )
	__A					=							field(default=lowercase__,   metadata={"help": "Name or path of preprocessor config."}			)
	__A					=							field(
	    default=lowercase__,   metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    },   )






def 					a(   )	-> str:
					"""simple docstring"""





					a        =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
					if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										a        =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
					else:
										a        =     parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry("run_semantic_segmentation"				,  A				,  A )

					# Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,  datefmt="%m/%d/%Y %H:%M:%S"				,  handlers=[logging.StreamHandler(sys.stdout )]				,  )

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					a        =     training_args.get_process_log_level()
					logger.setLevel(A )
					transformers.utils.logging.set_verbosity(A )
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
					    + f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
					logger.info(f'''Training/evaluation parameters {training_args}''' )

					# Detecting last checkpoint.
					a        =     None
					if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
										a        =     get_last_checkpoint(training_args.output_dir )
										if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
															raise ValueError(
															    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
															    "Use --overwrite_output_dir to overcome." )
										elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
															logger.info(
															    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
															    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
					a        =     load_dataset(data_args.dataset_name				,  cache_dir=model_args.cache_dir )

					# Rename column names to standardized names (only "image" and "label" need to be present)
					if "pixel_values" in dataset["train"].column_names:
										a        =     dataset.rename_columns({"pixel_values": "image"} )
					if "annotation" in dataset["train"].column_names:
										a        =     dataset.rename_columns({"annotation": "label"} )

					# If we don't have a validation split, split off a percentage of train as validation.
					a        =     None if 'validation' in dataset.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split				,  A ) and data_args.train_val_split > 0.0:
										a        =     dataset['train'].train_test_split(data_args.train_val_split )
										a        =     split['train']
										a        =     split['test']

					# Prepare label mappings.
					# We'll include these in the model's config to get human readable labels in the Inference API.
					if data_args.dataset_name == "scene_parse_150":
										a        =     'huggingface/label-files'
										a        =     'ade20k-id2label.json'
					else:
										a        =     data_args.dataset_name
										a        =     'id2label.json'
					a        =     json.load(open(hf_hub_download(A				,  A				,  repo_type="dataset" )				,  "r" ) )
					a        =     {int(A ): v for k, v in idalabel.items()}
					a        =     {v: str(A ) for k, v in idalabel.items()}

					# Load the mean IoU metric from the datasets package
					a        =     evaluate.load("mean_iou" )

					# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
					# predictions and label_ids field) and has to return a dictionary string to float.
					@torch.no_grad()
					def compute_metrics(A :					int ):
										a        =     eval_pred
										a        =     torch.from_numpy(A )
										# scale the logits to the size of the label
										a        =     nn.functional.interpolate(
										    A				,  size=labels.shape[-2:]				,  mode="bilinear"				,  align_corners=A				,  ).argmax(dim=1 )

										a        =     logits_tensor.detach().cpu().numpy()
										a        =     metric.compute(
										    predictions=A				,  references=A				,  num_labels=len(A )				,  ignore_index=0				,  reduce_labels=image_processor.do_reduce_labels				,  )
										# add per category metrics as individual key-value pairs
										a        =     metrics.pop("per_category_accuracy" ).tolist()
										a        =     metrics.pop("per_category_iou" ).tolist()

										metrics.update({f'''accuracy_{idalabel[i]}''': v for i, v in enumerate(A )} )
										metrics.update({f'''iou_{idalabel[i]}''': v for i, v in enumerate(A )} )

										return metrics

					a        =     AutoConfig.from_pretrained(
					    model_args.config_name or model_args.model_name_or_path				,  labelaid=A				,  idalabel=A				,  cache_dir=model_args.cache_dir				,  revision=model_args.model_revision				,  use_auth_token=True if model_args.use_auth_token else None				,  )
					a        =     AutoModelForSemanticSegmentation.from_pretrained(
					    model_args.model_name_or_path				,  from_tf=bool(".ckpt" in model_args.model_name_or_path )				,  config=A				,  cache_dir=model_args.cache_dir				,  revision=model_args.model_revision				,  use_auth_token=True if model_args.use_auth_token else None				,  )
					a        =     AutoImageProcessor.from_pretrained(
					    model_args.image_processor_name or model_args.model_name_or_path				,  cache_dir=model_args.cache_dir				,  revision=model_args.model_revision				,  use_auth_token=True if model_args.use_auth_token else None				,  )

					# Define torchvision transforms to be applied to each image + target.
					# Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
					# Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
					if "shortest_edge" in image_processor.size:
										# We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
										a        =     (image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
					else:
										a        =     (image_processor.size['height'], image_processor.size['width'])
					a        =     Compose(
					    [
					        ReduceLabels() if data_args.reduce_labels else Identity(),
					        RandomCrop(size=A ),
					        RandomHorizontalFlip(flip_prob=0.5 ),
					        PILToTensor(),
					        ConvertImageDtype(torch.float ),
					        Normalize(mean=image_processor.image_mean				,  std=image_processor.image_std ),
					    ] )
					# Define torchvision transform to be applied to each image.
					# jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
					a        =     Compose(
					    [
					        ReduceLabels() if data_args.reduce_labels else Identity(),
					        Resize(size=A ),
					        PILToTensor(),
					        ConvertImageDtype(torch.float ),
					        Normalize(mean=image_processor.image_mean				,  std=image_processor.image_std ),
					    ] )

					def preprocess_train(A :					str ):
										a        =     []
										a        =     []
										for image, target in zip(example_batch["image"]				,  example_batch["label"] ):
															a        =     train_transforms(image.convert("RGB" )				,  A )
															pixel_values.append(A )
															labels.append(A )

										a        =     {}
										a        =     torch.stack(A )
										a        =     torch.stack(A )

										return encoding

					def preprocess_val(A :					Dict ):
										a        =     []
										a        =     []
										for image, target in zip(example_batch["image"]				,  example_batch["label"] ):
															a        =     val_transforms(image.convert("RGB" )				,  A )
															pixel_values.append(A )
															labels.append(A )

										a        =     {}
										a        =     torch.stack(A )
										a        =     torch.stack(A )

										return encoding

					if training_args.do_train:
										if "train" not in dataset:
															raise ValueError("--do_train requires a train dataset" )
										if data_args.max_train_samples is not None:
															a        =     (
															    dataset['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
															)
										# Set the training transforms
										dataset["train"].set_transform(A )

					if training_args.do_eval:
										if "validation" not in dataset:
															raise ValueError("--do_eval requires a validation dataset" )
										if data_args.max_eval_samples is not None:
															a        =     (
															    dataset['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
															)
										# Set the validation transforms
										dataset["validation"].set_transform(A )

					# Initalize our trainer
					a        =     Trainer(
					    model=A				,  args=A				,  train_dataset=dataset["train"] if training_args.do_train else None				,  eval_dataset=dataset["validation"] if training_args.do_eval else None				,  compute_metrics=A				,  tokenizer=A				,  data_collator=A				,  )

					# Training
					if training_args.do_train:
										a        =     None
										if training_args.resume_from_checkpoint is not None:
															a        =     training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															a        =     last_checkpoint
										a        =     trainer.train(resume_from_checkpoint=A )
										trainer.save_model()
										trainer.log_metrics("train"				,  train_result.metrics )
										trainer.save_metrics("train"				,  train_result.metrics )
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										a        =     trainer.evaluate()
										trainer.log_metrics("eval"				,  A )
										trainer.save_metrics("eval"				,  A )

					# Write model card and (optionally) push to hub
					a        =     {
					    'finetuned_from': model_args.model_name_or_path,
					    'dataset': data_args.dataset_name,
					    'tags': ['image-segmentation', 'vision'],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**A )
					else:
										trainer.create_model_card(**A )


if __name__ == "__main__":
			main()





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)
_lowercase:				Optional[Any]       =	"\\n"

_lowercase:				List[Any]       =	"\\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"

_lowercase:				Dict       =	"\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"

_lowercase:				List[Any]       =	["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
_lowercase:				Tuple       =	""

try:
				from jiwer import transforms as tr

				_lowercase:				Any       =	True
except ImportError:
				_lowercase:				Optional[int]       =	False

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



				class 			_lowercase							(     tr.AbstractTransform			):





					"""simple docstring"""




					def __init__(self	, lowerCamelCase_ = " "	):
										"""simple docstring"""

										a        =     sentence_delimiter




					def   UpperCamelCase_ (self	, lowerCamelCase_	):
										"""simple docstring"""

										return list(lowerCamelCase_	)




					def   UpperCamelCase_ (self	, lowerCamelCase_	):
										"""simple docstring"""

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




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






def 					a(   A :					List[Any]				,  A :					int )	-> Union[str, Any]:
					"""simple docstring"""





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






def 					a(   A :					int				,  A :					List[str] )	-> str:
					"""simple docstring"""





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






def 					a(   A :					Optional[Any]				,  A :					Dict				,  A :					int="exp"				,  A :					int=None				,  A :					List[Any]=False				,  A :					Tuple=False				,  A :					Tuple=None				,  A :					Tuple=False				,  )	-> Tuple:
					"""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(   A :					str				,  A :					Tuple				,  A :					Optional[Any]				,  A :					str )	-> int:
					"""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(A :					List[Any]				,  A :					Any				,  A :					Union[str, Any]="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 			_lowercase							(     datasets.Metric			):





	"""simple docstring"""




	def   UpperCamelCase_ (self	):
						"""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(lowerCamelCase_	), "references": datasets.Value(lowerCamelCase_	)}	)	, codebase_urls=[]	, reference_urls=[]	, format="numpy"	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_=None	, lowerCamelCase_=None	):
						"""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(lowerCamelCase_	, lowerCamelCase_	)}
						elif self.config_name == "minds14":
											return fa_and_simple_accuracy(lowerCamelCase_	, lowerCamelCase_	)
						elif self.config_name == "covost2":
											a        =     bleu_kwargs.pop("smooth_method"	, "exp"	)
											a        =     bleu_kwargs.pop("smooth_value"	, lowerCamelCase_	)
											a        =     bleu_kwargs.pop("force"	, lowerCamelCase_	)
											a        =     bleu_kwargs.pop("lowercase"	, lowerCamelCase_	)
											a        =     bleu_kwargs.pop("tokenize"	, lowerCamelCase_	)
											a        =     bleu_kwargs.pop("use_effective_order"	, lowerCamelCase_	)
											return bleu(
											    preds=lowerCamelCase_	, labels=lowerCamelCase_	, smooth_method=lowerCamelCase_	, smooth_value=lowerCamelCase_	, force=lowerCamelCase_	, lowercase=lowerCamelCase_	, tokenize=lowerCamelCase_	, use_effective_order=lowerCamelCase_	, )
						elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
											a        =     wer_kwargs.pop("concatenate_texts"	, lowerCamelCase_	)
											return wer_and_cer(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, self.config_name	)
						else:
											raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''	)
