from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

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


__lowerCAmelCase	:       str     =	""


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):




		class 					snake_case__							(tr.AbstractTransform     ):
						"""simple docstring"""



						def __init__(   self       :  Any      ,   __lowerCamelCase       :  str = " "							)      ->  Any:
									a					=    sentence_delimiter





						def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  str							)      ->  Any:
									return list(UpperCAmelCase__							)





						def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[str]							)      ->  List[str]:
									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	:       List[str]     =	tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
else:
		__lowerCAmelCase	:       Dict     =	tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)


__lowerCAmelCase	:       Union[str, Any]     =	"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

__lowerCAmelCase	:       Dict     =	"\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

__lowerCAmelCase	:       Optional[int]     =	"\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION     )
class 					snake_case__							(datasets.Metric     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  int:
							return datasets.MetricInfo(
							    description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"      ,   id="sequence"							),
							            "references": datasets.Value("string"      ,   id="sequence"							),
							        }							)      ,   codebase_urls=["https://github.com/jitsi/jiwer/"]      ,   reference_urls=[
							        "https://en.wikipedia.org/wiki/Word_error_rate",
							        "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
							    ]      ,   )





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str=False							)      ->  Optional[Any]:
							if concatenate_texts:
										return jiwer.compute_measures(
										    UpperCAmelCase__      ,   UpperCAmelCase__      ,   truth_transform=UpperCAmelCase__      ,   hypothesis_transform=UpperCAmelCase__      ,   )["wer"]

							a					=    0
							a					=    0
							for prediction, reference in zip(UpperCAmelCase__      ,   UpperCAmelCase__							):
										a					=    jiwer.compute_measures(
										    UpperCAmelCase__      ,   UpperCAmelCase__      ,   truth_transform=UpperCAmelCase__      ,   hypothesis_transform=UpperCAmelCase__      ,   )
										incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
										total += measures["substitutions"] + measures["deletions"] + measures["hits"]

							return incorrect / total












import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    fname.split(os.path.sep     )[-1]
			return re.search(R"^(.*)_\d+\.jpg$",						A     ).groups()[0]




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  str      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  Union[str, Any]=None							)      ->  Tuple:
							a					=    file_names
							a					=    image_transform
							a					=    label_to_id





				def __len__(   self       :  Any							)      ->  Tuple:
							return len(self.file_names							)





				def __getitem__(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  int:
							a					=    self.file_names[idx]
							a					=    PIL.Image.open(__lowerCamelCase							)
							a					=    raw_image.convert("RGB"							)
							if self.image_transform is not None:
										a					=    self.image_transform(__lowerCamelCase							)
							a					=    extract_label(__lowerCamelCase							)
							if self.label_to_id is not None:
										a					=    self.label_to_id[label]
							return {"image": image, "label": label}




def     __magic_name__       (				A	:      str,						A	:      int     ):






			'''simple docstring'''







			if args.with_tracking:
						a					=    Accelerator(
						    cpu=args.cpu,						mixed_precision=args.mixed_precision,						log_with="all",						project_dir=args.project_dir     )
			else:
						a					=    Accelerator(cpu=args.cpu,						mixed_precision=args.mixed_precision     )

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a					=    config["lr"]
			a					=    int(config["num_epochs"]     )
			a					=    int(config["seed"]     )
			a					=    int(config["batch_size"]     )
			a					=    config["image_size"]
			if not isinstance(A,						(list, tuple)     ):
						a					=    (image_size, image_size)

			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps,						"isdigit"     ):
						if args.checkpointing_steps == "epoch":
									a					=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									a					=    int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						a					=    None

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						a					=    os.path.split(A     )[-1].split("."     )[0]
						accelerator.init_trackers(A,						A     )

			# Grab all the image filenames
			a					=    [os.path.join(args.data_dir,						A     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(".jpg"     )]

			# Build the label correspondences
			a					=    [extract_label(A     ) for fname in file_names]
			a					=    list(set(A     )     )
			id_to_label.sort()
			a					=    {lbl: i for i, lbl in enumerate(A     )}

			# Set the seed before splitting the data.
			np.random.seed(A     )
			torch.manual_seed(A     )
			torch.cuda.manual_seed_all(A     )

			# Split our filenames between train and validation
			a					=    np.random.permutation(len(A     )     )
			a					=    int(0.8 * len(A     )     )
			a					=    random_perm[:cut]
			a					=    random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			a					=    Compose([RandomResizedCrop(A,						scale=(0.5, 1.0)     ), ToTensor()]     )
			a					=    PetsDataset(
			    [file_names[i] for i in train_split],						image_transform=A,						label_to_id=A     )

			# For evaluation, we use a deterministic Resize
			a					=    Compose([Resize(A     ), ToTensor()]     )
			a					=    PetsDataset([file_names[i] for i in eval_split],						image_transform=A,						label_to_id=A     )

			# Instantiate dataloaders.
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a					=    create_model("resnet50d",						pretrained=A,						num_classes=len(A     )     )

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			a					=    model.to(accelerator.device     )

			# Freezing the base model
			for param in model.parameters():
						a					=    False
			for param in model.get_classifier().parameters():
						a					=    True

			# We normalize the batches of images to be a bit faster.
			a					=    torch.tensor(model.default_cfg["mean"]     )[None, :, None, None].to(accelerator.device     )
			a					=    torch.tensor(model.default_cfg["std"]     )[None, :, None, None].to(accelerator.device     )

			# Instantiate optimizer
			a					=    torch.optim.Adam(params=model.parameters(),						lr=lr / 25     )

			# Instantiate learning rate scheduler
			a					=    OneCycleLR(optimizer=A,						max_lr=A,						epochs=A,						steps_per_epoch=len(A     )     )

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			a					,	a					,	a					,	a					,	a					=    accelerator.prepare(
			    A,						A,						A,						A,						A     )
			# We need to keep track of how many total steps we have iterated over
			a					=    0
			# We also need to keep track of the starting epoch so files are named properly
			a					=    0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""     )
									accelerator.load_state(args.resume_from_checkpoint     )
									a					=    os.path.basename(args.resume_from_checkpoint     )
						else:
									# Get the most recent checkpoint
									a					=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
									dirs.sort(key=os.path.getctime     )
									a					=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						a					=    os.path.splitext(A     )[0]

						if "epoch" in training_difference:
									a					=    int(training_difference.replace("epoch_",						""     )     ) + 1
									a					=    None
						else:
									a					=    int(training_difference.replace("step_",						""     )     )
									a					=    resume_step // len(A     )
									resume_step -= starting_epoch * len(A     )

    # Now we train the model
			for epoch in range(A,						A     ):
						model.train()
						if args.with_tracking:
									a					=    0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									a					=    accelerator.skip_first_batches(A,						A     )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									a					=    train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									a					=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
									a					=    (batch["image"] - mean) / std
									a					=    model(A     )
									a					=    torch.nn.functional.cross_entropy(A,						batch["label"]     )
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(A     )
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(A,						A     ):
												a					=    F"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		a					=    os.path.join(args.output_dir,						A     )
															accelerator.save_state(A     )
						model.eval()
						a					=    0
						a					=    0
						for step, batch in enumerate(A     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									a					=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
									a					=    (batch["image"] - mean) / std
									with torch.no_grad():
												a					=    model(A     )
									a					=    outputs.argmax(dim=-1     )
									a					,	a					=    accelerator.gather_for_metrics((predictions, batch["label"])     )
									a					=    predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						a					=    accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""     )
						if args.with_tracking:
									accelerator.log(
									    {
									        "accuracy": 100 * eval_metric,
									        "train_loss": total_loss.item() / len(A     ),
									        "epoch": epoch,
									    },						step=A,						)
						if checkpointing_steps == "epoch":
									a					=    F"""epoch_{epoch}"""
									if args.output_dir is not None:
												a					=    os.path.join(args.output_dir,						A     )
									accelerator.save_state(A     )

			if args.with_tracking:
						accelerator.end_training()




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    argparse.ArgumentParser(description="Simple example of training script."     )
			parser.add_argument("--data_dir",						required=A,						help="The data folder on disk."     )
			parser.add_argument("--fp16",						action="store_true",						help="If passed, will use FP16 training."     )
			parser.add_argument(
			    "--mixed_precision",						type=A,						default=A,						choices=["no", "fp16", "bf16", "fp8"],						help="Whether to use mixed precision. Choose"
			    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
			    "and an Nvidia Ampere GPU.",						)
			parser.add_argument("--cpu",						action="store_true",						help="If passed, will train on the CPU."     )
			parser.add_argument(
			    "--checkpointing_steps",						type=A,						default=A,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",						)
			parser.add_argument(
			    "--output_dir",						type=A,						default=".",						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",						)
			parser.add_argument(
			    "--resume_from_checkpoint",						type=A,						default=A,						help="If the training should continue from a checkpoint folder.",						)
			parser.add_argument(
			    "--with_tracking",						action="store_true",						help="Whether to load in all available experiment trackers from the environment and use them for logging.",						)
			parser.add_argument(
			    "--project_dir",						type=A,						default="logs",						help="Location on where to store experiment tracking logs` and relevent project information",						)
			a					=    parser.parse_args()
			a					=    {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
			training_function(A,						A     )


if __name__ == "__main__":
		main()






