

import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


__snake_case							:     List[str] 						=	logging.get_logger(__name__)


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		Tuple				    =				nn.functional.normalize(lowerCamelCase_)
			a_		:		Union[str, Any]				    =				nn.functional.normalize(lowerCamelCase_)
			return torch.mm(lowerCamelCase_       ,					normalized_text_embeds.t())



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






						_A  :							List[str]   			=	CLIPConfig

						_A  :							Optional[Any]   			=	['''CLIPEncoderLayer''']





						def __init__(  self       ,     _lowercase  )      ->				Any:
									super().__init__(_lowercase  )

									a_		:		Tuple				    =				CLIPVisionModel(config.vision_config  )
									a_		:		Optional[Any]				    =				nn.Linear(config.vision_config.hidden_size       ,     config.projection_dim       ,     bias=_lowercase  )

									a_		:		str				    =				nn.Parameter(torch.ones(17       ,     config.projection_dim  )       ,     requires_grad=_lowercase  )
									a_		:		int				    =				nn.Parameter(torch.ones(3       ,     config.projection_dim  )       ,     requires_grad=_lowercase  )

									a_		:		Optional[int]				    =				nn.Parameter(torch.ones(17  )       ,     requires_grad=_lowercase  )
									a_		:		Union[str, Any]				    =				nn.Parameter(torch.ones(3  )       ,     requires_grad=_lowercase  )





						@torch.no_grad()
						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				int:
									a_		:		Tuple				    =				self.vision_model(_lowercase  )[1]  # pooled_output
									a_		:		Dict				    =				self.visual_projection(_lowercase  )

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									a_		:		Dict				    =				cosine_distance(_lowercase       ,     self.special_care_embeds  ).cpu().float().numpy()
									a_		:		Any				    =				cosine_distance(_lowercase       ,     self.concept_embeds  ).cpu().float().numpy()

									a_		:		Dict				    =				[]
									a_		:		Optional[Any]				    =				image_embeds.shape[0]
									for i in range(_lowercase  ):
												a_		:		Any				    =				{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

												# increase this value to create a stronger `nfsw` filter
												# at the cost of increasing the possibility of filtering benign images
												a_		:		int				    =				0.0

												for concept_idx in range(len(special_cos_dist[0]  )  ):
															a_		:		Tuple				    =				special_cos_dist[i][concept_idx]
															a_		:		Any				    =				self.special_care_embeds_weights[concept_idx].item()
															a_		:		List[str]				    =				round(concept_cos - concept_threshold + adjustment       ,     3  )
															if result_img["special_scores"][concept_idx] > 0:
																		result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}  )
																		a_		:		List[Any]				    =				0.0_1

												for concept_idx in range(len(cos_dist[0]  )  ):
															a_		:		Dict				    =				cos_dist[i][concept_idx]
															a_		:		Optional[Any]				    =				self.concept_embeds_weights[concept_idx].item()
															a_		:		Tuple				    =				round(concept_cos - concept_threshold + adjustment       ,     3  )
															if result_img["concept_scores"][concept_idx] > 0:
																		result_img["bad_concepts"].append(_lowercase  )

												result.append(_lowercase  )

									a_		:		str				    =				[len(res["""bad_concepts"""]  ) > 0 for res in result]

									for idx, has_nsfw_concept in enumerate(_lowercase  ):
												if has_nsfw_concept:
															if torch.is_tensor(_lowercase  ) or torch.is_tensor(images[0]  ):
																		a_		:		Tuple				    =				torch.zeros_like(images[idx]  )  # black image
															else:
																		a_		:		List[str]				    =				np.zeros(images[idx].shape  )  # black image

									if any(_lowercase  ):
												logger.warning(
												    """Potential NSFW content was detected in one or more images. A black image will be returned instead."""
												    """ Try again with a different prompt and/or seed."""  )

									return images, has_nsfw_concepts





						@torch.no_grad()
						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				Any:
									a_		:		Any				    =				self.vision_model(_lowercase  )[1]  # pooled_output
									a_		:		Any				    =				self.visual_projection(_lowercase  )

									a_		:		Union[str, Any]				    =				cosine_distance(_lowercase       ,     self.special_care_embeds  )
									a_		:		Optional[Any]				    =				cosine_distance(_lowercase       ,     self.concept_embeds  )

									# increase this value to create a stronger `nsfw` filter
									# at the cost of increasing the possibility of filtering benign images
									a_		:		Dict				    =				0.0

									a_		:		str				    =				special_cos_dist - self.special_care_embeds_weights + adjustment
									# special_scores = special_scores.round(decimals=3)
									a_		:		Any				    =				torch.any(special_scores > 0       ,     dim=1  )
									a_		:		Dict				    =				special_care * 0.0_1
									a_		:		List[Any]				    =				special_adjustment.unsqueeze(1  ).expand(-1       ,     cos_dist.shape[1]  )

									a_		:		Tuple				    =				(cos_dist - self.concept_embeds_weights) + special_adjustment
									# concept_scores = concept_scores.round(decimals=3)
									a_		:		List[str]				    =				torch.any(concept_scores > 0       ,     dim=1  )

									a_		:		List[str]				    =				0.0  # black image

									return images, has_nsfw_concepts





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()


