

from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__snake_case							:     str 						=	getLogger(__name__)


__snake_case							:     int 						=	4
__snake_case							:     int 						=	7

__snake_case							:     Optional[Any] 						=	{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if isinstance(a__       ,					torch.Tensor):
						return image
			elif isinstance(a__       ,					PIL.Image.Image):
						a_		:		Any				    =				[image]

			if isinstance(image[0]       ,					PIL.Image.Image):
						a_		:		Union[str, Any]				    =				image[0].size
						a_		:		Tuple				    =				(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

						a_		:		int				    =				[np.array(i.resize((w, h)))[None, :] for i in image]
						a_		:		List[str]				    =				np.concatenate(a__       ,					axis=0)
						a_		:		Union[str, Any]				    =				np.array(a__).astype(np.floataa) / 255.0
						a_		:		int				    =				image.transpose(0       ,					3       ,					1       ,					2)
						a_		:		Union[str, Any]				    =				2.0 * image - 1.0
						a_		:		int				    =				torch.from_numpy(a__)
			elif isinstance(image[0]       ,					torch.Tensor):
						a_		:		List[str]				    =				torch.cat(a__       ,					dim=0)

			return image



class    A__(a_    ):
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase = 350       ,     )      ->				int:
									super().__init__(
									    vae=_lowercase       ,     text_encoder=_lowercase       ,     tokenizer=_lowercase       ,     unet=_lowercase       ,     low_res_scheduler=_lowercase       ,     scheduler=_lowercase       ,     safety_checker=_lowercase       ,     feature_extractor=_lowercase       ,     watermarker=_lowercase       ,     max_noise_level=_lowercase       ,     )





						def __call__(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase = 75       ,     _lowercase = 9.0       ,     _lowercase = 20       ,     _lowercase = None       ,     _lowercase = 1       ,     _lowercase = 0.0       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = "pil"       ,     _lowercase = True       ,     _lowercase = None       ,     _lowercase = 1       ,     )      ->				int:
									self.check_inputs(_lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )

									# 2. Define call parameters
									if prompt is not None and isinstance(_lowercase       ,     _lowercase  ):
												a_		:		str				    =				1
									elif prompt is not None and isinstance(_lowercase       ,     _lowercase  ):
												a_		:		Dict				    =				len(_lowercase  )
									else:
												a_		:		int				    =				prompt_embeds.shape[0]

									a_		:		List[str]				    =				self._execution_device
									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									a_		:		Union[str, Any]				    =				guidance_scale > 1.0

									# 3. Encode input prompt
									a_		:		Tuple				    =				self._encode_prompt(
									    _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     prompt_embeds=_lowercase       ,     negative_prompt_embeds=_lowercase       ,     )

									a_		:		List[str]				    =				ORT_TO_PT_TYPE[str(text_embeddings.dtype  )]

									# 4. Preprocess image
									a_		:		Union[str, Any]				    =				preprocess(_lowercase  )
									a_		:		str				    =				image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(_lowercase       ,     device=_lowercase  )
									a_		:		str				    =				self.scheduler.timesteps

									# 5. Add noise to image
									a_		:		int				    =				torch.tensor([noise_level]       ,     dtype=torch.long       ,     device=_lowercase  )
									a_		:		Any				    =				torch.randn(image.shape       ,     generator=_lowercase       ,     device=_lowercase       ,     dtype=_lowercase  )
									a_		:		str				    =				self.low_res_scheduler.add_noise(_lowercase       ,     _lowercase       ,     _lowercase  )

									a_		:		List[Any]				    =				2 if do_classifier_free_guidance else 1
									a_		:		Tuple				    =				np.concatenate([image] * batch_multiplier * num_images_per_prompt  )
									a_		:		Optional[int]				    =				np.concatenate([noise_level] * image.shape[0]  )

									# 6. Prepare latent variables
									a_		:		Union[str, Any]				    =				image.shape[2:]
									a_		:		Any				    =				self.prepare_latents(
									    batch_size * num_images_per_prompt       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )

									# 7. Check that sizes of image and latents match
									a_		:		Dict				    =				image.shape[1]
									if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
												raise ValueError(
												    """Incorrect configuration settings! The config of `pipeline.unet` expects"""
												    F''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
												    F''' `num_channels_image`: {num_channels_image} '''
												    F''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
												    """ `pipeline.unet` or your `image` input."""  )

									# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
									a_		:		Union[str, Any]				    =				self.prepare_extra_step_kwargs(_lowercase       ,     _lowercase  )

									a_		:		int				    =				next(
									    (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")       ,     """tensor(float)"""  )
									a_		:		Dict				    =				ORT_TO_NP_TYPE[timestep_dtype]

									# 9. Denoising loop
									a_		:		Optional[Any]				    =				len(_lowercase  ) - num_inference_steps * self.scheduler.order
									with self.progress_bar(total=_lowercase  ) as progress_bar:
												for i, t in enumerate(_lowercase  ):
															# expand the latents if we are doing classifier free guidance
															a_		:		List[Any]				    =				np.concatenate([latents] * 2  ) if do_classifier_free_guidance else latents

															# concat latents, mask, masked_image_latents in the channel dimension
															a_		:		str				    =				self.scheduler.scale_model_input(_lowercase       ,     _lowercase  )
															a_		:		Any				    =				np.concatenate([latent_model_input, image]       ,     axis=1  )

															# timestep to tensor
															a_		:		str				    =				np.array([t]       ,     dtype=_lowercase  )

															# predict the noise residual
															a_		:		List[Any]				    =				self.unet(
															    sample=_lowercase       ,     timestep=_lowercase       ,     encoder_hidden_states=_lowercase       ,     class_labels=noise_level.astype(np.intaa  )       ,     )[0]

															# perform guidance
															if do_classifier_free_guidance:
																		a_		:		Optional[int]				    =				np.split(_lowercase       ,     2  )
															a_		:		str				    =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															a_		:		List[Any]				    =				self.scheduler.step(
															    torch.from_numpy(_lowercase  )       ,     _lowercase       ,     _lowercase       ,     **_lowercase  ).prev_sample

															# call the callback, if provided
															if i == len(_lowercase  ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																		progress_bar.update()
																		if callback is not None and i % callback_steps == 0:
																					callback(_lowercase       ,     _lowercase       ,     _lowercase  )

        # 10. Post-processing
									a_		:		Optional[Any]				    =				self.decode_latents(latents.float()  )

									# 11. Convert to PIL
									if output_type == "pil":
												a_		:		Dict				    =				self.numpy_to_pil(_lowercase  )

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Any:
									a_		:		Dict				    =				1 / 0.0_8_3_3_3 * latents
									a_		:		int				    =				self.vae(latent_sample=_lowercase  )[0]
									a_		:		Any				    =				np.clip(image / 2 + 0.5       ,     0       ,     1  )
									a_		:		int				    =				image.transpose((0, 2, 3, 1)  )
									return image





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = None       ,     )      ->				Tuple:
									if prompt is not None and isinstance(_lowercase       ,     _lowercase  ):
												a_		:		int				    =				1
									elif prompt is not None and isinstance(_lowercase       ,     _lowercase  ):
												a_		:		Dict				    =				len(_lowercase  )
									else:
												a_		:		Any				    =				prompt_embeds.shape[0]

									if prompt_embeds is None:
												a_		:		int				    =				self.tokenizer(
												    _lowercase       ,     padding="""max_length"""       ,     max_length=self.tokenizer.model_max_length       ,     truncation=_lowercase       ,     return_tensors="""pt"""       ,     )
												a_		:		int				    =				text_inputs.input_ids
												a_		:		Optional[int]				    =				self.tokenizer(_lowercase       ,     padding="""longest"""       ,     return_tensors="""pt"""  ).input_ids

												if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
												    _lowercase       ,     _lowercase  ):
															a_		:		Tuple				    =				self.tokenizer.batch_decode(
															    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
															logger.warning(
															    """The following part of your input was truncated because CLIP can only handle sequences up to"""
															    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''  )

												# no positional arguments to text_encoder
												a_		:		List[Any]				    =				self.text_encoder(
												    input_ids=text_input_ids.int().to(_lowercase  )       ,     )
												a_		:		str				    =				prompt_embeds[0]

									a_		:		Optional[int]				    =				prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									a_		:		Optional[Any]				    =				prompt_embeds.repeat(1       ,     _lowercase  )
									a_		:		List[Any]				    =				prompt_embeds.reshape(bs_embed * num_images_per_prompt       ,     _lowercase       ,     -1  )

									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance and negative_prompt_embeds is None:
												a_		:		List[str]
												if negative_prompt is None:
															a_		:		Tuple				    =				[""""""] * batch_size
												elif type(_lowercase  ) is not type(_lowercase  ):
															raise TypeError(
															    F'''`negative_prompt` should be the same type to `prompt`, but got {type(_lowercase  )} !='''
															    F''' {type(_lowercase  )}.'''  )
												elif isinstance(_lowercase       ,     _lowercase  ):
															a_		:		Optional[int]				    =				[negative_prompt]
												elif batch_size != len(_lowercase  ):
															raise ValueError(
															    F'''`negative_prompt`: {negative_prompt} has batch size {len(_lowercase  )}, but `prompt`:'''
															    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
															    """ the batch size of `prompt`."""  )
												else:
															a_		:		Optional[int]				    =				negative_prompt

												a_		:		int				    =				text_input_ids.shape[-1]
												a_		:		Dict				    =				self.tokenizer(
												    _lowercase       ,     padding="""max_length"""       ,     max_length=_lowercase       ,     truncation=_lowercase       ,     return_tensors="""pt"""       ,     )

												# if hasattr(uncond_input, "attention_mask"):
												#     attention_mask = uncond_input.attention_mask.to(device)
												# else:
												#     attention_mask = None

												a_		:		Any				    =				self.text_encoder(
												    input_ids=uncond_input.input_ids.int().to(_lowercase  )       ,     )
												a_		:		Tuple				    =				uncond_embeddings[0]

									if do_classifier_free_guidance:
												a_		:		str				    =				uncond_embeddings.shape[1]
												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												a_		:		str				    =				uncond_embeddings.repeat(1       ,     _lowercase  )
												a_		:		str				    =				uncond_embeddings.reshape(batch_size * num_images_per_prompt       ,     _lowercase       ,     -1  )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												a_		:		Optional[Any]				    =				np.concatenate([uncond_embeddings, prompt_embeds]  )

									return prompt_embeds





import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
	from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
	__snake_case							:     List[str] 						=	True
	from torch.cuda.amp import autocast


__snake_case							:     Dict 						=	logging.getLogger(__name__)



@dataclass
class    A__:
						"""simple docstring"""






						_A  :							str   			=	field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
						_A  :							Optional[str]   			=	field(
						    default=a_,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},       )
						_A  :							Optional[bool]   			=	field(
						    default=a_,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}    )
						_A  :							Optional[bool]   			=	field(
						    default=a_,       metadata={'''help''': '''Whether to log verbose messages or not.'''},       )







def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       ,					datefmt="""%m/%d/%Y %H:%M:%S"""       ,					handlers=[logging.StreamHandler(sys.stdout)]       ,					)
			a_		:		Optional[int]				    =				logging.WARNING
			if model_args.verbose_logging:
						a_		:		Any				    =				logging.DEBUG
			elif trainer_utils.is_main_process(training_args.local_rank):
						a_		:		List[str]				    =				logging.INFO
			logger.setLevel(a__)



@dataclass
class    A__:
						"""simple docstring"""






						_A  :							str   			=	field(
						    default=a_,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
						_A  :							Optional[str]   			=	field(
						    default=a_,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}    )
						_A  :							Optional[str]   			=	field(
						    default='''train''',       metadata={
						        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
						    },       )
						_A  :							Optional[str]   			=	field(
						    default='''validation''',       metadata={
						        '''help''': (
						            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
						        )
						    },       )
						_A  :							Optional[str]   			=	field(
						    default='''text''',       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''},       )
						_A  :							Optional[str]   			=	field(
						    default='''file''',       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''},       )
						_A  :							Optional[bool]   			=	field(
						    default=a_,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''},       )
						_A  :							Optional[float]   			=	field(
						    default=a_,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''},       )
						_A  :							Optional[str]   			=	field(
						    default='''librispeech''',       metadata={
						        '''help''': (
						            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
						            ''' \'buckwalter\'.'''
						        )
						    },       )
						_A  :							bool   			=	field(
						    default=a_,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}    )
						_A  :							Optional[int]   			=	field(
						    default=a_,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''},       )







@dataclass
class    A__:
						"""simple docstring"""






						_A  :							bool   			=	False
						_A  :							Optional[str]   			=	None
						_A  :							Optional[str]   			=	"|"
						_A  :							Optional[Dict[str, str]]   			=	field(default_factory=a_    )
						_A  :							Optional[Set[str]]   			=	field(default_factory=a_    )
						_A  :							Optional[Callable[[str], str]]   			=	None





						@classmethod
						def 	UpperCamelCase__			(  cls       ,     _lowercase  )      ->				Optional[Any]:
									if name == "librispeech":
												return cls()
									if name == "timit":
												return cls(
												    do_lower_case=_lowercase       ,     translation_table=str.maketrans({"""-""": """ """}  )       ,     )
									if name == "buckwalter":
												a_		:		Tuple				    =				{
												    """-""": """ """,  # sometimes used to represent pauses
												    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
												}
												return cls(
												    vocab_file=pathlib.Path(_lowercase  ).parent.joinpath("""vocab/buckwalter.json"""  )       ,     word_delimiter_token="""/"""       ,     translation_table=str.maketrans(_lowercase  )       ,     words_to_remove={"""sil"""}       ,     untransliterator=arabic.buckwalter.untransliterate       ,     )
									raise ValueError(F'''Unsupported orthography: \'{name}\'.'''  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				str:
									# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
									if len(self.translation_table  ) > 0:
												a_		:		Dict				    =				text.translate(self.translation_table  )
									if len(self.words_to_remove  ) == 0:
												a_		:		Any				    =				""" """.join(text.split()  )  # clean up whitespaces
									else:
												a_		:		Optional[int]				    =				""" """.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
									return text





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				WavaVecaProcessor:
									a_		:		List[str]				    =				WavaVecaFeatureExtractor.from_pretrained(
									    model_args.model_name_or_path       ,     cache_dir=model_args.cache_dir  )
									if self.vocab_file:
												a_		:		List[Any]				    =				WavaVecaCTCTokenizer(
												    self.vocab_file       ,     cache_dir=model_args.cache_dir       ,     do_lower_case=self.do_lower_case       ,     word_delimiter_token=self.word_delimiter_token       ,     )
									else:
												a_		:		Dict				    =				WavaVecaCTCTokenizer.from_pretrained(
												    model_args.model_name_or_path       ,     cache_dir=model_args.cache_dir       ,     do_lower_case=self.do_lower_case       ,     word_delimiter_token=self.word_delimiter_token       ,     )
									return WavaVecaProcessor(_lowercase       ,     _lowercase  )







@dataclass
class    A__:
						"""simple docstring"""






						_A  :							WavaVecaProcessor
						_A  :							Union[bool, str]   			=	True
						_A  :							Optional[int]   			=	None
						_A  :							Optional[int]   			=	None
						_A  :							Optional[int]   			=	None
						_A  :							Optional[int]   			=	None





						def __call__(  self       ,     _lowercase  )      ->				Dict[str, torch.Tensor]:
									# split inputs and labels since they have to be of different lenghts and need
									# different padding methods
									a_		:		Dict				    =				[{"""input_values""": feature["""input_values"""]} for feature in features]
									a_		:		str				    =				[{"""input_ids""": feature["""labels"""]} for feature in features]

									a_		:		Optional[Any]				    =				self.processor.pad(
									    _lowercase       ,     padding=self.padding       ,     max_length=self.max_length       ,     pad_to_multiple_of=self.pad_to_multiple_of       ,     return_tensors="""pt"""       ,     )
									a_		:		Any				    =				self.processor.pad(
									    labels=_lowercase       ,     padding=self.padding       ,     max_length=self.max_length_labels       ,     pad_to_multiple_of=self.pad_to_multiple_of_labels       ,     return_tensors="""pt"""       ,     )

									# replace padding with -100 to ignore loss correctly
									a_		:		Optional[int]				    =				labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1  )       ,     -100  )

									a_		:		Tuple				    =				labels

									return batch







class    A__(a_    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				torch.Tensor:
									model.train()
									a_		:		Union[str, Any]				    =				self._prepare_inputs(_lowercase  )

									if self.use_amp:
												with autocast():
															a_		:		List[Any]				    =				self.compute_loss(_lowercase       ,     _lowercase  )
									else:
												a_		:		Optional[Any]				    =				self.compute_loss(_lowercase       ,     _lowercase  )

									if self.args.n_gpu > 1:
												if model.module.config.ctc_loss_reduction == "mean":
															a_		:		List[str]				    =				loss.mean()
												elif model.module.config.ctc_loss_reduction == "sum":
															a_		:		int				    =				loss.sum() / (inputs["""labels"""] >= 0).sum()
												else:
															raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''  )

									if self.args.gradient_accumulation_steps > 1:
												a_		:		Any				    =				loss / self.args.gradient_accumulation_steps

									if self.use_amp:
												self.scaler.scale(_lowercase  ).backward()
									elif self.use_apex:
												with amp.scale_loss(_lowercase       ,     self.optimizer  ) as scaled_loss:
															scaled_loss.backward()
									elif self.deepspeed:
												self.deepspeed.backward(_lowercase  )
									else:
												loss.backward()

									return loss.detach()







def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		str				    =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))

			a_    ,			a_    ,			a_		:		str				    =				parser.parse_args_into_dataclasses()
			configure_logger(a__       ,					a__)

			a_		:		List[str]				    =				Orthography.from_name(data_args.orthography.lower())
			a_		:		Optional[Any]				    =				orthography.create_processor(a__)
			a_		:		Any				    =				WavaVecaForCTC.from_pretrained(
			    model_args.model_name_or_path       ,					cache_dir=model_args.cache_dir       ,					gradient_checkpointing=training_args.gradient_checkpointing       ,					vocab_size=len(processor.tokenizer)       ,					)

			a_		:		str				    =				datasets.load_dataset(
			    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=data_args.train_split_name)
			a_		:		Tuple				    =				datasets.load_dataset(
			    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=data_args.validation_split_name)

			a_		:		str				    =				datasets.load_metric("""wer""")
			a_		:		Optional[Any]				    =				processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
			a_		:		Union[str, Any]				    =				"""""".join(t for t in processor.tokenizer.get_vocab().keys() if len(a__) == 1)
			a_		:		Optional[Any]				    =				re.compile(  # remove characters not in vocabulary
			    rf'''[^\s{re.escape(a__)}]'''       ,					flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0       ,					)
			a_		:		Optional[Any]				    =				[]

			def prepare_example(a__):  # TODO(elgeish) make use of multiprocessing?
						a_    ,			a_		:		str				    =				librosa.load(example[data_args.speech_file_column]       ,					sr=a__)
						if data_args.max_duration_in_seconds is not None:
									a_		:		Optional[int]				    =				len(example["""speech"""]) / example["""sampling_rate"""]
						# Normalize and clean up text; order matters!
						a_		:		List[Any]				    =				orthography.preprocess_for_training(example[data_args.target_text_column])
						a_		:		Optional[Any]				    =				vocabulary_text_cleaner.sub(""""""       ,					a__)
						if updated_text != example[data_args.target_text_column]:
									text_updates.append((example[data_args.target_text_column], updated_text))
									a_		:		Union[str, Any]				    =				updated_text
						return example

			a_		:		Optional[int]				    =				train_dataset.map(a__       ,					remove_columns=[data_args.speech_file_column])
			a_		:		Optional[int]				    =				val_dataset.map(a__       ,					remove_columns=[data_args.speech_file_column])

			if data_args.max_duration_in_seconds is not None:

						def filter_by_max_duration(a__):
									return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

						a_		:		int				    =				len(a__)
						a_		:		Optional[Any]				    =				len(a__)
						a_		:		Tuple				    =				train_dataset.filter(a__       ,					remove_columns=["""duration_in_seconds"""])
						a_		:		List[Any]				    =				val_dataset.filter(a__       ,					remove_columns=["""duration_in_seconds"""])
						if len(a__) > old_train_size:
									logger.warning(
									    f'''Filtered out {len(a__) - old_train_size} train example(s) longer than'''
									    f''' {data_args.max_duration_in_seconds} second(s).''')
						if len(a__) > old_val_size:
									logger.warning(
									    f'''Filtered out {len(a__) - old_val_size} validation example(s) longer than'''
									    f''' {data_args.max_duration_in_seconds} second(s).''')
			logger.info(f'''Split sizes: {len(a__)} train and {len(a__)} validation.''')

			logger.warning(f'''Updated {len(a__)} transcript(s) using \'{data_args.orthography}\' orthography rules.''')
			if logger.isEnabledFor(logging.DEBUG):
						for original_text, updated_text in text_updates:
									logger.debug(f'''Updated text: "{original_text}" -> "{updated_text}"''')
			a_		:		List[str]				    =				None

			def prepare_dataset(a__):
						# check that all files have the correct sampling rate
						assert (
						    len(set(batch["""sampling_rate"""])) == 1
						), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

						a_		:		str				    =				processor(
						    audio=batch["""speech"""]       ,					text=batch[data_args.target_text_column]       ,					sampling_rate=batch["""sampling_rate"""][0])
						batch.update(a__)
						return batch

			a_		:		Optional[Any]				    =				train_dataset.map(
			    a__       ,					batch_size=training_args.per_device_train_batch_size       ,					batched=a__       ,					num_proc=data_args.preprocessing_num_workers       ,					)
			a_		:		List[str]				    =				val_dataset.map(
			    a__       ,					batch_size=training_args.per_device_train_batch_size       ,					batched=a__       ,					num_proc=data_args.preprocessing_num_workers       ,					)

			a_		:		List[str]				    =				DataCollatorCTCWithPadding(processor=a__       ,					padding=a__)

			def compute_metrics(a__):
						a_		:		List[Any]				    =				pred.predictions
						a_		:		List[str]				    =				np.argmax(a__       ,					axis=-1)

						a_		:		Optional[Any]				    =				processor.tokenizer.pad_token_id

						a_		:		Optional[Any]				    =				processor.batch_decode(a__)
						# we do not want to group tokens when computing the metrics
						a_		:		Optional[Any]				    =				processor.batch_decode(pred.label_ids       ,					group_tokens=a__)
						if logger.isEnabledFor(logging.DEBUG):
									for reference, predicted in zip(a__       ,					a__):
												logger.debug(f'''reference: "{reference}"''')
												logger.debug(f'''predicted: "{predicted}"''')
												if orthography.untransliterator is not None:
															logger.debug(f'''reference (untransliterated): "{orthography.untransliterator(a__)}"''')
															logger.debug(f'''predicted (untransliterated): "{orthography.untransliterator(a__)}"''')

						a_		:		Optional[int]				    =				wer_metric.compute(predictions=a__       ,					references=a__)

						return {"wer": wer}

			if model_args.freeze_feature_extractor:
						model.freeze_feature_extractor()

			a_		:		Dict				    =				CTCTrainer(
			    model=a__       ,					data_collator=a__       ,					args=a__       ,					compute_metrics=a__       ,					train_dataset=a__       ,					eval_dataset=a__       ,					tokenizer=processor.feature_extractor       ,					)

			trainer.train()


if __name__ == "__main__":
	main()


