
'''simple docstring'''
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 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Optional[int]:


		'''simple docstring'''


		snake_case   :    int			       =		fname.split(os.path.sep       )[-1]
		return re.search(R'''^(.*)_\d+\.jpg$'''      ,    SCREAMING_SNAKE_CASE__       ).groups()[0]






class      snake_case__    (	__SCREAMING_SNAKE_CASE ):







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       str   ,     UpperCamelCase__							:       str=None   ,     UpperCamelCase__							:       Optional[Any]=None	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		file_names
				snake_case   :    Tuple			       =		image_transform
				snake_case   :    List[str]			       =		label_to_id



		def __len__(	self							:       Union[str, Any]	)		->       Tuple:






				"""simple docstring"""







				return len(self.file_names	)



		def __getitem__(	self							:       List[Any]   ,     UpperCamelCase__							:       Optional[int]	)		->       str:






				"""simple docstring"""







				snake_case   :    Any			       =		self.file_names[idx]
				snake_case   :    Dict			       =		PIL.Image.open(UpperCamelCase__	)
				snake_case   :    List[Any]			       =		raw_image.convert('''RGB'''	)
				if self.image_transform is not None:
						snake_case   :    Optional[Any]			       =		self.image_transform(UpperCamelCase__	)
				snake_case   :    List[Any]			       =		extract_label(UpperCamelCase__	)
				if self.label_to_id is not None:
						snake_case   :    Tuple			       =		self.label_to_id[label]
				return {"image": image, "label": label}







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Union[str, Any]:


		'''simple docstring'''


		snake_case   :    int			       =		Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision       )

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		snake_case   :    Optional[int]			       =		config['''lr''']
		snake_case   :    Tuple			       =		int(config['''num_epochs''']       )
		snake_case   :    Dict			       =		int(config['''seed''']       )
		snake_case   :    str			       =		int(config['''batch_size''']       )
		snake_case   :    Any			       =		config['''image_size''']
		if not isinstance(SCREAMING_SNAKE_CASE__      ,    (list, tuple)       ):
				snake_case   :    List[Any]			       =		(image_size, image_size)

		# Grab all the image filenames
		snake_case   :    Optional[int]			       =		[os.path.join(args.data_dir      ,    SCREAMING_SNAKE_CASE__       ) for fname in os.listdir(args.data_dir       ) if fname.endswith('''.jpg'''       )]

		# Build the label correspondences
		snake_case   :    Union[str, Any]			       =		[extract_label(SCREAMING_SNAKE_CASE__       ) for fname in file_names]
		snake_case   :    Any			       =		list(set(SCREAMING_SNAKE_CASE__       )       )
		id_to_label.sort()
		snake_case   :    Optional[Any]			       =		{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__       )}

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

		# Split our filenames between train and validation
		snake_case   :    int			       =		np.random.permutation(len(SCREAMING_SNAKE_CASE__       )       )
		snake_case   :    List[str]			       =		int(0.8 * len(SCREAMING_SNAKE_CASE__       )       )
		snake_case   :    Any			       =		random_perm[:cut]
		snake_case   :    str			       =		random_perm[cut:]

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

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

		# Instantiate dataloaders.
		snake_case   :    Tuple			       =		DataLoader(SCREAMING_SNAKE_CASE__      ,    shuffle=SCREAMING_SNAKE_CASE__      ,    batch_size=SCREAMING_SNAKE_CASE__      ,    num_workers=4       )
		snake_case   :    Tuple			       =		DataLoader(SCREAMING_SNAKE_CASE__      ,    shuffle=SCREAMING_SNAKE_CASE__      ,    batch_size=SCREAMING_SNAKE_CASE__      ,    num_workers=4       )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		snake_case   :    Optional[Any]			       =		create_model('''resnet50d'''      ,    pretrained=SCREAMING_SNAKE_CASE__      ,    num_classes=len(SCREAMING_SNAKE_CASE__       )       )

		# 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).
		snake_case   :    List[Any]			       =		model.to(accelerator.device       )

		# Freezing the base model
		for param in model.parameters():
				snake_case   :    Dict			       =		False
		for param in model.get_classifier().parameters():
				snake_case   :    Optional[Any]			       =		True

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

		# Instantiate optimizer
		snake_case   :    List[str]			       =		torch.optim.Adam(params=model.parameters()      ,    lr=lr / 25       )

		# Instantiate learning rate scheduler
		snake_case   :    int			       =		OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__      ,    max_lr=SCREAMING_SNAKE_CASE__      ,    epochs=SCREAMING_SNAKE_CASE__      ,    steps_per_epoch=len(SCREAMING_SNAKE_CASE__       )       )

		# 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.
		snake_case       ,snake_case       ,snake_case       ,snake_case       ,snake_case   :    Dict			       =		accelerator.prepare(
		    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )

		# Now we train the model
		for epoch in range(SCREAMING_SNAKE_CASE__       ):
				model.train()
				for step, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						snake_case   :    Tuple			       =		{k: v.to(accelerator.device       ) for k, v in batch.items()}
						snake_case   :    Any			       =		(batch['''image'''] - mean) / std
						snake_case   :    Tuple			       =		model(SCREAMING_SNAKE_CASE__       )
						snake_case   :    str			       =		torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE__      ,    batch['''label''']       )
						accelerator.backward(SCREAMING_SNAKE_CASE__       )
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()

				model.eval()
				snake_case   :    List[Any]			       =		0
				snake_case   :    List[Any]			       =		0
				for _, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						snake_case   :    Optional[Any]			       =		{k: v.to(accelerator.device       ) for k, v in batch.items()}
						snake_case   :    List[str]			       =		(batch['''image'''] - mean) / std
						with torch.no_grad():
								snake_case   :    Optional[Any]			       =		model(SCREAMING_SNAKE_CASE__       )
						snake_case   :    Tuple			       =		outputs.argmax(dim=-1       )
						snake_case       ,snake_case   :    str			       =		accelerator.gather_for_metrics((predictions, batch['''label'''])       )
						snake_case   :    str			       =		predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				snake_case   :    Any			       =		accurate.item() / num_elems
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'       )




def 					_UpperCamelCase		(    )   ->				int:


		'''simple docstring'''


		snake_case   :    Tuple			       =		argparse.ArgumentParser(description='''Simple example of training script.'''       )
		parser.add_argument('''--data_dir'''      ,    required=SCREAMING_SNAKE_CASE__      ,    help='''The data folder on disk.'''       )
		parser.add_argument(
		    '''--mixed_precision'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default=SCREAMING_SNAKE_CASE__      ,    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(
		    '''--checkpointing_steps'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default=SCREAMING_SNAKE_CASE__      ,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''      ,    )
		parser.add_argument('''--cpu'''      ,    action='''store_true'''      ,    help='''If passed, will train on the CPU.'''       )
		snake_case   :    Optional[Any]			       =		parser.parse_args()
		snake_case   :    Tuple			       =		{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
		training_function(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
						main()




'''simple docstring'''
import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor






class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       Dict	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		cuda
				snake_case   :    int			       =		AutoConfig.from_pretrained(UpperCamelCase__	)
				snake_case   :    int			       =		AutoModelForCTC.from_pretrained(UpperCamelCase__	)
				self.model.eval()
				if self.cuda:
						self.model.to(device='''cuda'''	)
				snake_case   :    Tuple			       =		AutoProcessor.from_pretrained(UpperCamelCase__	)
				snake_case   :    str			       =		torchaudio.transforms.Resample(UpperCamelCase__   ,     1_6000	)
				snake_case   :    Any			       =		0
				snake_case   :    int			       =		list(self.processor.tokenizer.get_vocab().keys()	)
				for i in range(len(UpperCamelCase__	)	):
						if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
								snake_case   :    Union[str, Any]			       =		i
				print('''Blank Token id [PAD]/<pad>'''   ,     UpperCamelCase__	)
				snake_case   :    List[Any]			       =		blank_id



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     UpperCamelCase__							:       str	)		->       List[str]:






				"""simple docstring"""







				snake_case       ,snake_case   :    Tuple			       =		torchaudio.load(UpperCamelCase__	)
				snake_case   :    Optional[Any]			       =		self.resampler(UpperCamelCase__	).squeeze().numpy()
				return speech



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       int	)		->       Dict:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.blank_id
				snake_case   :    Union[str, Any]			       =		'''|'''.join(item['''sent'''].split(''' '''	)	)
				if not os.path.isfile(item['''wav_path''']	):
						print(item['''wav_path''']   ,     '''not found in wavs directory'''	)

				snake_case   :    List[Any]			       =		self.speech_file_to_array_fn(item['''wav_path''']	)
				snake_case   :    List[str]			       =		self.processor(UpperCamelCase__   ,     sampling_rate=1_6000   ,     return_tensors='''pt'''   ,     padding=UpperCamelCase__	)
				if self.cuda:
						snake_case   :    str			       =		inputs.to(device='''cuda'''	)

				with torch.no_grad():
						snake_case   :    List[Any]			       =		self.model(inputs.input_values	).logits

				# get the emission probability at frame level
				snake_case   :    str			       =		torch.log_softmax(UpperCamelCase__   ,     dim=-1	)
				snake_case   :    Optional[Any]			       =		emissions[0].cpu().detach()

				# get labels from vocab
				snake_case   :    Dict			       =		([''''''] + list(self.processor.tokenizer.get_vocab().keys()	))[
				    :-1
				]  # logits don't align with the tokenizer's vocab

				snake_case   :    List[str]			       =		{c: i for i, c in enumerate(UpperCamelCase__	)}
				snake_case   :    Any			       =		[]
				for c in transcript:
						if c in dictionary:
								tokens.append(dictionary[c]	)

				def get_trellis(UpperCamelCase__							:       str   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       str=0	):
						snake_case   :    List[str]			       =		emission.size(0	)
						snake_case   :    List[str]			       =		len(UpperCamelCase__	)

						# Trellis has extra diemsions for both time axis and tokens.
						# The extra dim for tokens represents <SoS> (start-of-sentence)
						# The extra dim for time axis is for simplification of the code.
						snake_case   :    List[Any]			       =		torch.full((num_frames + 1, num_tokens + 1)   ,     -float('''inf'''	)	)
						snake_case   :    Optional[int]			       =		0
						for t in range(UpperCamelCase__	):
								snake_case   :    Any			       =		torch.maximum(
								    # Score for staying at the same token
								    trellis[t, 1:] + emission[t, blank_id]   ,     trellis[t, :-1] + emission[t, tokens]   ,     )
						return trellis

				snake_case   :    Optional[Any]			       =		get_trellis(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42

				def backtrack(UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Optional[Any]   ,     UpperCamelCase__							:       Optional[Any]   ,     UpperCamelCase__							:       Union[str, Any]=0	):
						snake_case   :    List[str]			       =		trellis.size(1	) - 1
						snake_case   :    Any			       =		torch.argmax(trellis[:, j]	).item()

						snake_case   :    Dict			       =		[]
						for t in range(UpperCamelCase__   ,     0   ,     -1	):
								# 1. Figure out if the current position was stay or change
								# Note (again):
								# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
								# Score for token staying the same from time frame J-1 to T.
								snake_case   :    Union[str, Any]			       =		trellis[t - 1, j] + emission[t - 1, blank_id]
								# Score for token changing from C-1 at T-1 to J at T.
								snake_case   :    Optional[int]			       =		trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

								# 2. Store the path with frame-wise probability.
								snake_case   :    Optional[Any]			       =		emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
								# Return token index and time index in non-trellis coordinate.
								path.append(Point(j - 1   ,     t - 1   ,     UpperCamelCase__	)	)

								# 3. Update the token
								if changed > stayed:
										j -= 1
										if j == 0:
												break
						else:
								raise ValueError('''Failed to align'''	)
						return path[::-1]

				snake_case   :    Any			       =		backtrack(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42



						def __repr__(	self							:       Dict	)		->       int:






								"""simple docstring"""







								return f'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'



						@property
						def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Any:






								"""simple docstring"""







								return self.end - self.start

				def merge_repeats(UpperCamelCase__							:       str	):
						snake_case       ,snake_case   :    Union[str, Any]			       =		0, 0
						snake_case   :    List[Any]			       =		[]
						while ia < len(UpperCamelCase__	):
								while ia < len(UpperCamelCase__	) and path[ia].token_index == path[ia].token_index:
										ia += 1
								snake_case   :    Dict			       =		sum(path[k].score for k in range(UpperCamelCase__   ,     UpperCamelCase__	)	) / (ia - ia)
								segments.append(
								    Segment(
								        transcript[path[ia].token_index]   ,     path[ia].time_index   ,     path[ia - 1].time_index + 1   ,     UpperCamelCase__   ,     )	)
								snake_case   :    int			       =		ia
						return segments

				snake_case   :    Any			       =		merge_repeats(UpperCamelCase__	)
				with open(item['''out_path''']   ,     '''w'''	) as out_align:
						for seg in segments:
								out_align.write(str(UpperCamelCase__	) + '''\n'''	)



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       List[str]	)		->       List[str]:






				"""simple docstring"""







				if not os.path.exists(UpperCamelCase__	):
						os.makedirs(UpperCamelCase__	)

				# load text file
				snake_case   :    List[Any]			       =		open(UpperCamelCase__   ,     encoding='''utf8'''	).readlines()

				snake_case   :    Optional[int]			       =		[]
				for line in lines:
						if len(line.strip().split('''\t'''	)	) != 2:
								print('''Script must be in format: 00001  this is my sentence'''	)
								exit()

						snake_case       ,snake_case   :    Dict			       =		line.strip().split('''\t'''	)
						snake_case   :    Tuple			       =		os.path.join(UpperCamelCase__   ,     wav_name + '''.wav'''	)
						snake_case   :    str			       =		os.path.join(UpperCamelCase__   ,     wav_name + '''.txt'''	)

						items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path}	)
				print('''Number of samples found in script file'''   ,     len(UpperCamelCase__	)	)

				for item in tqdm(UpperCamelCase__	):
						self.align_single_sample(UpperCamelCase__	)







def 					_UpperCamelCase		(    )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''arijitx/wav2vec2-xls-r-300m-bengali'''      ,    help='''wav2vec model name'''       )
		parser.add_argument('''--wav_dir'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''./wavs'''      ,    help='''directory containing wavs'''       )
		parser.add_argument('''--text_file'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''script.txt'''      ,    help='''file containing text'''       )
		parser.add_argument('''--input_wavs_sr'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default=1_6000      ,    help='''sampling rate of input audios'''       )
		parser.add_argument(
		    '''--output_dir'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''./out_alignment'''      ,    help='''output directory containing the alignment files'''       )
		parser.add_argument('''--cuda'''      ,    action='''store_true'''       )

		snake_case   :    Tuple			       =		parser.parse_args()

		snake_case   :    Tuple			       =		WavaVecaAligner(args.model_name      ,    args.input_wavs_sr      ,    args.cuda       )
		aligner.align_data(args.wav_dir      ,    args.text_file      ,    args.output_dir       )


if __name__ == "__main__":
						main()


