


'''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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Dict					    =  fname.split(os.path.sep   )[-1]
					return re.search(r'^(.*)_\d+\.jpg$'						,      _SCREAMING_SNAKE_CASE   ).groups()[0]





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def __init__(   self						,				__a						,				__a=None						,				__a=None							):

						'''simple docstring'''





						__a			:       Any					    =  file_names
						__a			:       List[str]					    =  image_transform
						__a			:       List[str]					    =  label_to_id




	def __len__(   self							):

						'''simple docstring'''





						return len(self.file_names							)





	def __getitem__(   self						,				__a							):

						'''simple docstring'''





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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					# Initialize accelerator
					if args.with_tracking:
										__a			:       Optional[int]					    =  Accelerator(
										    cpu=args.cpu						,      mixed_precision=args.mixed_precision						,      log_with='all'						,      project_dir=args.project_dir   )
					else:
										__a			:       Tuple					    =  Accelerator(cpu=args.cpu						,      mixed_precision=args.mixed_precision   )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a			:       Optional[int]					    =  config['lr']
					__a			:       Optional[Any]					    =  int(config['num_epochs']   )
					__a			:       Tuple					    =  int(config['seed']   )
					__a			:       List[str]					    =  int(config['batch_size']   )
					__a			:       Union[str, Any]					    =  config['image_size']
					if not isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										__a			:       Optional[int]					    =  (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			:       Optional[int]					    =  args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__a			:       Dict					    =  int(args.checkpointing_steps   )
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
					else:
										__a			:       Optional[Any]					    =  None

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

					# Grab all the image filenames
					__a			:       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
					__a			:       List[str]					    =  [extract_label(_SCREAMING_SNAKE_CASE   ) for fname in file_names]
					__a			:       Tuple					    =  list(set(_SCREAMING_SNAKE_CASE   )   )
					id_to_label.sort()
					__a			:       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
					__a			:       int					    =  np.random.permutation(len(_SCREAMING_SNAKE_CASE   )   )
					__a			:       str					    =  int(0.8 * len(_SCREAMING_SNAKE_CASE   )   )
					__a			:       List[Any]					    =  random_perm[:cut]
					__a			:       int					    =  random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					__a			:       str					    =  Compose([RandomResizedCrop(_SCREAMING_SNAKE_CASE						,      scale=(0.5, 1.0)   ), ToTensor()]   )
					__a			:       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
					__a			:       Optional[int]					    =  Compose([Resize(_SCREAMING_SNAKE_CASE   ), ToTensor()]   )
					__a			:       Tuple					    =  PetsDataset([file_names[i] for i in eval_split]						,      image_transform=_SCREAMING_SNAKE_CASE						,      label_to_id=_SCREAMING_SNAKE_CASE   )

					# Instantiate dataloaders.
					__a			:       Dict					    =  DataLoader(_SCREAMING_SNAKE_CASE						,      shuffle=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE						,      num_workers=4   )
					__a			:       Optional[Any]					    =  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)
					__a			:       List[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).
					__a			:       Optional[int]					    =  model.to(accelerator.device   )

					# Freezing the base model
					for param in model.parameters():
										__a			:       str					    =  False
					for param in model.get_classifier().parameters():
										__a			:       List[Any]					    =  True

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

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

					# Instantiate learning rate scheduler
					__a			:       Union[str, Any]					    =  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.
					__a    ,				__a    ,				__a    ,				__a    ,				__a			:       List[str]					    =  accelerator.prepare(
					    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
					# We need to keep track of how many total steps we have iterated over
					__a			:       List[Any]					    =  0
					# We also need to keep track of the starting epoch so files are named properly
					__a			:       Tuple					    =  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			:       List[Any]					    =  os.path.basename(args.resume_from_checkpoint   )
										else:
															# Get the most recent checkpoint
															__a			:       Any					    =  [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
															dirs.sort(key=os.path.getctime   )
															__a			:       Optional[Any]					    =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										__a			:       Optional[int]					    =  os.path.splitext(_SCREAMING_SNAKE_CASE   )[0]

										if "epoch" in training_difference:
															__a			:       Optional[int]					    =  int(training_difference.replace('epoch_'						,      ''   )   ) + 1
															__a			:       str					    =  None
										else:
															__a			:       str					    =  int(training_difference.replace('step_'						,      ''   )   )
															__a			:       Union[str, Any]					    =  resume_step // len(_SCREAMING_SNAKE_CASE   )
															resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE   )

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

										__a			:       int					    =  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(_SCREAMING_SNAKE_CASE   ),
															        'epoch': epoch,
															    }						,      step=_SCREAMING_SNAKE_CASE						,      )
										if checkpointing_steps == "epoch":
															__a			:       str					    =  F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				__a			:       Tuple					    =  os.path.join(args.output_dir						,      _SCREAMING_SNAKE_CASE   )
															accelerator.save_state(_SCREAMING_SNAKE_CASE   )

					if args.with_tracking:
										accelerator.end_training()






def   lowerCamelCase							():
					__a			:       Optional[int]					    =  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('--fp16'						,      action='store_true'						,      help='If passed, will use FP16 training.'   )
					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('--cpu'						,      action='store_true'						,      help='If passed, will train on the CPU.'   )
					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(
					    '--output_dir'						,      type=_SCREAMING_SNAKE_CASE						,      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=_SCREAMING_SNAKE_CASE						,      default=_SCREAMING_SNAKE_CASE						,      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=_SCREAMING_SNAKE_CASE						,      default='logs'						,      help='Location on where to store experiment tracking logs` and relevent project information'						,      )
					__a			:       Tuple					    =  parser.parse_args()
					__a			:       int					    =  {'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()




import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin



@require_phonemizer
class 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Dict    			=		WavaVecaPhonemeCTCTokenizer
 lowerCamelCase	:Optional[int]    			=		False



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        super().setUp()

        _A        =  (
            """<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː """
            """ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː """
            """ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 """
            """oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ """
            """pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ """
            """yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ """
            """əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ """
            """ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ """
            """ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ """
            """uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ """
            """ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ """
            """ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ """
            """ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"""
        ).split(""" """							)
        _A        =  dict(zip(lowerCAmelCase_		,				range(len(lowerCAmelCase_							)							)							)							)

        _A        =  {"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

        _A        =  os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES["""vocab_file"""]							)
        with open(self.vocab_file		,				"""w"""		,				encoding="""utf-8"""							) as fp:
               fp.write(json.dumps(lowerCAmelCase_							) + """\n"""							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=False		,				lowerCAmelCase_=20		,				lowerCAmelCase_=5							)  ->					Tuple[str, list]:
        _A        =  [(i, tokenizer.decode([i]		,				clean_up_tokenization_spaces=lowerCAmelCase_							)) for i in range(len(lowerCAmelCase_							)							)]
        _A        =  list(filter(lambda lowerCAmelCase_							: [t[0]] == tokenizer.encode(t[1]		,				do_phonemize=lowerCAmelCase_							)		,				lowerCAmelCase_							)							)
        if max_length is not None and len(lowerCAmelCase_							) > max_length:
               _A        =  toks[:max_length]
        if min_length is not None and len(lowerCAmelCase_							) < min_length and len(lowerCAmelCase_							) > 0:
               while len(lowerCAmelCase_							) < min_length:
                      _A        =  toks + toks
        # toks_str = [t[1] for t in toks]
        _A        =  [t[0] for t in toks]

        # Ensure consistency
        _A        =  tokenizer.decode(lowerCAmelCase_		,				clean_up_tokenization_spaces=lowerCAmelCase_							)
        if " " not in output_txt and len(lowerCAmelCase_							) > 1:
               _A        =  (
                   tokenizer.decode([toks_ids[0]]		,				clean_up_tokenization_spaces=lowerCAmelCase_							)
                   + """ """
                   + tokenizer.decode(toks_ids[1:]		,				clean_up_tokenization_spaces=lowerCAmelCase_							)
               )
        if with_prefix_space:
               _A        =  """ """ + output_txt
        _A        =  tokenizer.encode(lowerCAmelCase_		,				add_special_tokens=lowerCAmelCase_							)
        return output_txt, output_ids



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        kwargs.update(self.special_tokens_map							)
        return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""							)

        # check adding a single token
        tokenizer.add_tokens("""xxx"""							)
        _A        =  tokenizer("""m xxx ɪ"""		,				do_phonemize=lowerCAmelCase_							).input_ids
        self.assertEqual(lowerCAmelCase_		,				[13, 3_92, 17]							)  # xxx should be last token

        tokenizer.add_tokens(["""aaa""", """bbb""", """ccc"""]							)
        _A        =  tokenizer("""m aaa ɪ ccc"""		,				do_phonemize=lowerCAmelCase_							).input_ids
        self.assertEqual(lowerCAmelCase_		,				[13, 3_93, 17, 3_95]							)  # aaa and ccc should be after xxx and 2 after aaa

        _A        =  tokenizer("""maɪ c"""		,				do_phonemize=lowerCAmelCase_							).input_ids
        self.assertEqual(lowerCAmelCase_		,				[3, 2_00]							)  # mai should be <unk> (=3)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""							)

        _A        =  """Hello how are you"""
        _A        =  tokenizer.phonemize(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							)
        self.assertEqual(lowerCAmelCase_		,				"""h ə l oʊ h aʊ ɑːɹ j uː"""							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""							)

        _A        =  """Hello how are you"""
        _A        =  tokenizer.phonemize(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							)
        self.assertEqual(tokenizer(lowerCAmelCase_							).input_ids		,				tokenizer(lowerCAmelCase_		,				do_phonemize=lowerCAmelCase_							).input_ids							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""							)
        _A        =  """Hello how are you"""
        _A        =  tokenizer.phonemize(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							)

        _A        =  tokenizer.decode(tokenizer(lowerCAmelCase_							).input_ids							)

        self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""							)

        _A        =  [
            [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
            [24, 22, 5, 24, 22, 5, 77],
        ]
        _A        =  tokenizer.decode(sample_ids[0]							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)
        self.assertEqual(lowerCAmelCase_		,				batch_tokens[0]							)
        self.assertEqual(lowerCAmelCase_		,				["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]							)



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  self.tokenizer_class.from_pretrained(
            """facebook/wav2vec2-lv-60-espeak-cv-ft"""		,				word_delimiter_token="""|"""							)
        tokenizer.add_tokens("""|"""							)

        _A        =  """Hello how are you"""
        _A        =  tokenizer.phonemize(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							)
        self.assertEqual(lowerCAmelCase_		,				"""h ə l oʊ | h aʊ | ɑːɹ | j uː |"""							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.tokenizer_class.from_pretrained(
            """facebook/wav2vec2-lv-60-espeak-cv-ft"""		,				word_delimiter_token="""|"""							)
        tokenizer.add_tokens("""|"""							)

        _A        =  """Hello how are you"""
        _A        =  tokenizer.phonemize(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							)
        self.assertEqual(tokenizer(lowerCAmelCase_							).input_ids		,				tokenizer(lowerCAmelCase_		,				do_phonemize=lowerCAmelCase_							).input_ids							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.tokenizer_class.from_pretrained(
            """facebook/wav2vec2-lv-60-espeak-cv-ft"""		,				word_delimiter_token="""|"""							)
        tokenizer.add_tokens("""|"""							)

        # fmt: off
        _A        =  [
            [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
            [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
        ]
        # fmt: on

        # decode with word_del_token filter
        _A        =  tokenizer.decode(sample_ids[0]							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)
        self.assertEqual(lowerCAmelCase_		,				batch_tokens[0]							)
        self.assertEqual(lowerCAmelCase_		,				["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]							)

        # decode with no word_del_token filter
        _A        =  tokenizer.decode(sample_ids[0]		,				filter_word_delimiter_token=lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_		,				filter_word_delimiter_token=lowerCAmelCase_							)
        self.assertEqual(lowerCAmelCase_		,				batch_tokens[0]							)
        self.assertEqual(lowerCAmelCase_		,				["""k s ɾ | ɾ l | ɭʲ""", """| j ð | s j ð s oːɹ"""]							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.tokenizer_class.from_pretrained(
            """facebook/wav2vec2-lv-60-espeak-cv-ft"""		,				word_delimiter_token="""|"""							)
        tokenizer.add_tokens("""|"""							)

        _A        =  """Hello how are you"""
        _A        =  tokenizer.phonemize(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							)

        _A        =  tokenizer.decode(tokenizer(lowerCAmelCase_							).input_ids		,				filter_word_delimiter_token=lowerCAmelCase_							)

        self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.tokenizer_class.from_pretrained(
            """facebook/wav2vec2-lv-60-espeak-cv-ft"""		,				word_delimiter_token="""|"""							)
        tokenizer.add_tokens("""|"""							)

        _A        =  """Hello how are you"""
        _A        =  tokenizer.phonemize(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							)

        _A        =  tokenizer.decode(tokenizer(lowerCAmelCase_							).input_ids		,				filter_word_delimiter_token=lowerCAmelCase_							)

        self.assertEqual(""" """.join([p.strip() for p in phonemes.split(""" |"""							)]							).strip()		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.tokenizer_class.from_pretrained(
            """facebook/wav2vec2-lv-60-espeak-cv-ft"""		,				word_delimiter_token=lowerCAmelCase_							)
        _A        =  """Hello how are you"""

        _A        =  tokenizer(lowerCAmelCase_		,				phonemizer_lang="""en-us"""							).input_ids
        _A        =  tokenizer(lowerCAmelCase_		,				phonemizer_lang="""fr-fr"""							).input_ids

        self.assertNotEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  tokenizer.decode(lowerCAmelCase_							)
        _A        =  tokenizer.decode(lowerCAmelCase_							)

        self.assertEqual(lowerCAmelCase_		,				"""h ə l oʊ h aʊ ɑːɹ j uː"""							)
        self.assertEqual(lowerCAmelCase_		,				"""ɛ l o h aʊ a ʁ j u"""							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""							)
        _A        =  """Hello how Are you"""
        _A        =  """hello how are you"""

        _A        =  tokenizer(lowerCAmelCase_							).input_ids
        _A        =  tokenizer(lowerCAmelCase_							).input_ids

        self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""							)
        tokenizer.add_tokens(["""!""", """?"""]							)
        tokenizer.add_special_tokens({"""cls_token""": """$$$"""}							)

        # fmt: off
        _A        =  [
            [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
            [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
        ]
        # fmt: on

        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)
        self.assertEqual(lowerCAmelCase_		,				["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""]							)



 @staticmethod
 def      UpperCAmelCase							(					lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[str]:
        _A        =  [d[key] for d in offsets]
        return retrieved_list



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.get_tokenizer(word_delimiter_token="""|"""							)
        tokenizer.add_tokens("""|"""							)

        # fmt: off
        # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
        _A        =  [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
        # fmt: on

        _A        =  tokenizer.decode(lowerCAmelCase_		,				output_char_offsets=lowerCAmelCase_		,				filter_word_delimiter_token=lowerCAmelCase_							)
        # check Wav2Vec2CTCTokenizerOutput keys for char
        self.assertEqual(len(outputs.keys()							)		,				2							)
        self.assertTrue("""text""" in outputs							)
        self.assertTrue("""char_offsets""" in outputs							)
        self.assertTrue(isinstance(lowerCAmelCase_		,				lowerCAmelCase_							)							)

        # check that order of chars is correct and identical for both outputs
        self.assertEqual(""" """.join(self.get_from_offsets(outputs["""char_offsets"""]		,				"""char"""							)							)		,				outputs.text							)
        self.assertListEqual(
            self.get_from_offsets(outputs["""char_offsets"""]		,				"""char"""							)		,				["""k""", """s""", """ɾ""", """ɾ""", """|""", """ɾ""", """l""", """|""", """ɭʲ"""]							)

        # check that offsets are actually correct for char
        # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
        # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
        self.assertListEqual(
            self.get_from_offsets(outputs["""char_offsets"""]		,				"""start_offset"""							)		,				[0, 1, 4, 7, 9, 11, 12, 15, 16]							)
        self.assertListEqual(
            self.get_from_offsets(outputs["""char_offsets"""]		,				"""end_offset"""							)		,				[1, 4, 6, 9, 10, 12, 15, 16, 17]							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  self.get_tokenizer(word_delimiter_token="""|"""							)

        def check_list_tuples_equal(lowerCAmelCase_		,				lowerCAmelCase_							):
               self.assertTrue(isinstance(lowerCAmelCase_		,				lowerCAmelCase_							)							)
               self.assertTrue(isinstance(outputs_list[0]		,				lowerCAmelCase_							)							)

               # transform list to ModelOutput
               _A        =  WavaVecaPhonemeCTCTokenizerOutput(
                   {k: [d[k] for d in outputs_list] for k in outputs_list[0]}							)

               self.assertListEqual(outputs_batch["""text"""]		,				outputs_batch_a["""text"""]							)

               def recursive_check(lowerCAmelCase_		,				lowerCAmelCase_							):
                      if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							):
                             [recursive_check(lowerCAmelCase_		,				lowerCAmelCase_							) for la, la in zip(lowerCAmelCase_		,				lowerCAmelCase_							)]
                      self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

               if "char_offsets" in outputs_batch:
                      recursive_check(outputs_batch["""char_offsets"""]		,				outputs_batch_a["""char_offsets"""]							)

        # fmt: off
        _A        =  [
            [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
            [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
        ]
        # fmt: on

        # We assume that `decode` works as expected. All we will check now is
        # the output type is correct and the output is identical to `decode`

        # char
        _A        =  tokenizer.batch_decode(lowerCAmelCase_		,				output_char_offsets=lowerCAmelCase_							)
        _A        =  [tokenizer.decode(lowerCAmelCase_		,				output_char_offsets=lowerCAmelCase_							) for ids in sample_ids]
        check_list_tuples_equal(lowerCAmelCase_		,				lowerCAmelCase_							)



 @unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"""							)
 def      UpperCAmelCase							(					self							)  ->					int:
        pass



 @unittest.skip("""Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"""							)
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        pass



 @unittest.skip("""encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"""							)
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        pass



 @unittest.skip("""Wav2Vec2PhonemeModel has no max model length => no testing"""							)
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        pass



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.get_tokenizers(do_lower_case=lowerCAmelCase_							)
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
                      _A        =  tokenizer.vocab_size
                      _A        =  len(lowerCAmelCase_							)

                      self.assertNotEqual(lowerCAmelCase_		,				0							)

                      # We usually have added tokens from the start in tests because our vocab fixtures are
                      # smaller than the original vocabs - let's not assert this
                      # self.assertEqual(vocab_size, all_size)

                      _A        =  ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
                      _A        =  tokenizer.add_tokens(lowerCAmelCase_							)
                      _A        =  tokenizer.vocab_size
                      _A        =  len(lowerCAmelCase_							)

                      self.assertNotEqual(lowerCAmelCase_		,				0							)
                      self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)
                      self.assertEqual(lowerCAmelCase_		,				len(lowerCAmelCase_							)							)
                      self.assertEqual(lowerCAmelCase_		,				all_size + len(lowerCAmelCase_							)							)

                      _A        =  tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""		,				add_special_tokens=lowerCAmelCase_							)

                      self.assertGreaterEqual(len(lowerCAmelCase_							)		,				4							)
                      self.assertGreater(tokens[0]		,				tokenizer.vocab_size - 1							)
                      self.assertGreater(tokens[-3]		,				tokenizer.vocab_size - 1							)

                      _A        =  {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
                      _A        =  tokenizer.add_special_tokens(lowerCAmelCase_							)
                      _A        =  tokenizer.vocab_size
                      _A        =  len(lowerCAmelCase_							)

                      self.assertNotEqual(lowerCAmelCase_		,				0							)
                      self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)
                      self.assertEqual(lowerCAmelCase_		,				len(lowerCAmelCase_							)							)
                      self.assertEqual(lowerCAmelCase_		,				all_size_a + len(lowerCAmelCase_							)							)

                      _A        =  tokenizer.encode(
                          """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""		,				add_special_tokens=lowerCAmelCase_							)

                      self.assertGreaterEqual(len(lowerCAmelCase_							)		,				6							)
                      self.assertGreater(tokens[0]		,				tokenizer.vocab_size - 1							)
                      self.assertGreater(tokens[0]		,				tokens[1]							)
                      self.assertGreater(tokens[-3]		,				tokenizer.vocab_size - 1							)
                      self.assertGreater(tokens[-3]		,				tokens[-4]							)
                      self.assertEqual(tokens[0]		,				tokenizer.eos_token_id							)
                      self.assertEqual(tokens[-3]		,				tokenizer.pad_token_id							)



 @unittest.skip("""The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."""							)
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        pass



 @unittest.skip("""The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."""							)
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        pass



 def      UpperCAmelCase							(					self							)  ->					str:
        # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
        # is not the case for Wav2Vec2PhonemeCTCTokenizer.
        _A        =  self.get_tokenizers(fast=lowerCAmelCase_		,				do_lower_case=lowerCAmelCase_							)
        for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
                      _A        =  ["""ð""", """ɪ""", """s""", """ɪ""", """z""", """ɐ""", """t""", """ɛ""", """k""", """s""", """t"""]
                      _A        =  tokenizer.convert_tokens_to_string(lowerCAmelCase_							)

                      self.assertIsInstance(output["""text"""]		,				lowerCAmelCase_							)




