

import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Optional[int]				    =				TimesformerConfig()

			if "large" in model_name:
						a_		:		Optional[Any]				    =				9_6

			if "hr" in model_name:
						a_		:		List[Any]				    =				1_6
						a_		:		Union[str, Any]				    =				4_4_8

			a_		:		int				    =				"""huggingface/label-files"""
			if "k400" in model_name:
						a_		:		List[str]				    =				4_0_0
						a_		:		Optional[Any]				    =				"""kinetics400-id2label.json"""
			elif "k600" in model_name:
						a_		:		Any				    =				6_0_0
						a_		:		Tuple				    =				"""kinetics600-id2label.json"""
			elif "ssv2" in model_name:
						a_		:		Union[str, Any]				    =				1_7_4
						a_		:		Any				    =				"""something-something-v2-id2label.json"""
			else:
						raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'.""")
			a_		:		Union[str, Any]				    =				json.load(open(hf_hub_download(a__       ,					a__       ,					repo_type="""dataset""")       ,					"""r"""))
			a_		:		Optional[Any]				    =				{int(a__): v for k, v in idalabel.items()}
			a_		:		Optional[Any]				    =				idalabel
			a_		:		str				    =				{v: k for k, v in idalabel.items()}

			return config


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if "encoder." in name:
						a_		:		List[str]				    =				name.replace("""encoder."""       ,					"""""")
			if "cls_token" in name:
						a_		:		Optional[int]				    =				name.replace("""cls_token"""       ,					"""timesformer.embeddings.cls_token""")
			if "pos_embed" in name:
						a_		:		int				    =				name.replace("""pos_embed"""       ,					"""timesformer.embeddings.position_embeddings""")
			if "time_embed" in name:
						a_		:		Dict				    =				name.replace("""time_embed"""       ,					"""timesformer.embeddings.time_embeddings""")
			if "patch_embed.proj" in name:
						a_		:		List[str]				    =				name.replace("""patch_embed.proj"""       ,					"""timesformer.embeddings.patch_embeddings.projection""")
			if "patch_embed.norm" in name:
						a_		:		str				    =				name.replace("""patch_embed.norm"""       ,					"""timesformer.embeddings.norm""")
			if "blocks" in name:
						a_		:		int				    =				name.replace("""blocks"""       ,					"""timesformer.encoder.layer""")
			if "attn.proj" in name:
						a_		:		Optional[int]				    =				name.replace("""attn.proj"""       ,					"""attention.output.dense""")
			if "attn" in name and "bias" not in name and "temporal" not in name:
						a_		:		List[str]				    =				name.replace("""attn"""       ,					"""attention.self""")
			if "attn" in name and "temporal" not in name:
						a_		:		Tuple				    =				name.replace("""attn"""       ,					"""attention.attention""")
			if "temporal_norm1" in name:
						a_		:		str				    =				name.replace("""temporal_norm1"""       ,					"""temporal_layernorm""")
			if "temporal_attn.proj" in name:
						a_		:		Optional[Any]				    =				name.replace("""temporal_attn"""       ,					"""temporal_attention.output.dense""")
			if "temporal_fc" in name:
						a_		:		Any				    =				name.replace("""temporal_fc"""       ,					"""temporal_dense""")
			if "norm1" in name and "temporal" not in name:
						a_		:		Union[str, Any]				    =				name.replace("""norm1"""       ,					"""layernorm_before""")
			if "norm2" in name:
						a_		:		List[Any]				    =				name.replace("""norm2"""       ,					"""layernorm_after""")
			if "mlp.fc1" in name:
						a_		:		Dict				    =				name.replace("""mlp.fc1"""       ,					"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_		:		List[Any]				    =				name.replace("""mlp.fc2"""       ,					"""output.dense""")
			if "norm.weight" in name and "fc" not in name and "temporal" not in name:
						a_		:		Union[str, Any]				    =				name.replace("""norm.weight"""       ,					"""timesformer.layernorm.weight""")
			if "norm.bias" in name and "fc" not in name and "temporal" not in name:
						a_		:		int				    =				name.replace("""norm.bias"""       ,					"""timesformer.layernorm.bias""")
			if "head" in name:
						a_		:		List[Any]				    =				name.replace("""head"""       ,					"""classifier""")

			return name


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			for key in orig_state_dict.copy().keys():
						a_		:		str				    =				orig_state_dict.pop(a__)

						if key.startswith("""model."""):
									a_		:		List[Any]				    =				key.replace("""model."""       ,					"""""")

						if "qkv" in key:
									a_		:		List[Any]				    =				key.split(""".""")
									a_		:		Union[str, Any]				    =				int(key_split[1])
									a_		:		Tuple				    =				"""timesformer.encoder.layer."""
									if "temporal" in key:
												a_		:		List[str]				    =				""".temporal_attention.attention.qkv."""
									else:
												a_		:		Tuple				    =				""".attention.attention.qkv."""
									if "weight" in key:
												a_		:		str				    =				val
									else:
												a_		:		List[str]				    =				val
						else:
									a_		:		List[Any]				    =				val

			return orig_state_dict


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Tuple				    =				hf_hub_download(
			    repo_id="""hf-internal-testing/spaghetti-video"""       ,					filename="""eating_spaghetti.npy"""       ,					repo_type="""dataset""")
			a_		:		Tuple				    =				np.load(a__)
			return list(a__)


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		int				    =				get_timesformer_config(a__)

			a_		:		Optional[Any]				    =				TimesformerForVideoClassification(a__)

			# download original checkpoint, hosted on Google Drive
			a_		:		str				    =				"""pytorch_model.bin"""
			gdown.cached_download(a__       ,					a__       ,					quiet=a__)
			a_		:		Dict				    =				torch.load(a__       ,					map_location="""cpu""")
			if "model" in files:
						a_		:		Optional[int]				    =				files["""model"""]
			elif "module" in files:
						a_		:		Optional[int]				    =				files["""module"""]
			else:
						a_		:		str				    =				files["""model_state"""]
			a_		:		Dict				    =				convert_state_dict(a__       ,					a__)

			model.load_state_dict(a__)
			model.eval()

			# verify model on basic input
			a_		:		List[str]				    =				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]       ,					image_std=[0.5, 0.5, 0.5])
			a_		:		Tuple				    =				prepare_video()
			a_		:		Optional[int]				    =				image_processor(video[:8]       ,					return_tensors="""pt""")

			a_		:		int				    =				model(**a__)
			a_		:		Any				    =				outputs.logits

			a_		:		List[str]				    =				[
			    # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
			    """timesformer-base-finetuned-k400""",
			    """timesformer-large-finetuned-k400""",
			    """timesformer-hr-finetuned-k400""",
			    # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
			    """timesformer-base-finetuned-k600""",
			    """timesformer-large-finetuned-k600""",
			    """timesformer-hr-finetuned-k600""",
			    # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
			    """timesformer-base-finetuned-ssv2""",
			    """timesformer-large-finetuned-ssv2""",
			    """timesformer-hr-finetuned-ssv2""",
			]

			# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
			if model_name == "timesformer-base-finetuned-k400":
						a_		:		str				    =				torch.Size([1, 4_0_0])
						a_		:		List[str]				    =				torch.tensor([-0.3016, -0.7713, -0.4205])
			elif model_name == "timesformer-base-finetuned-k600":
						a_		:		Tuple				    =				torch.Size([1, 6_0_0])
						a_		:		List[str]				    =				torch.tensor([-0.7267, -0.7466, 3.2404])
			elif model_name == "timesformer-base-finetuned-ssv2":
						a_		:		Tuple				    =				torch.Size([1, 1_7_4])
						a_		:		Tuple				    =				torch.tensor([-0.9059, 0.6433, -3.1457])
			elif model_name == "timesformer-large-finetuned-k400":
						a_		:		Any				    =				torch.Size([1, 4_0_0])
						a_		:		str				    =				torch.tensor([0, 0, 0])
			elif model_name == "timesformer-large-finetuned-k600":
						a_		:		int				    =				torch.Size([1, 6_0_0])
						a_		:		Optional[int]				    =				torch.tensor([0, 0, 0])
			elif model_name == "timesformer-large-finetuned-ssv2":
						a_		:		Optional[int]				    =				torch.Size([1, 1_7_4])
						a_		:		int				    =				torch.tensor([0, 0, 0])
			elif model_name == "timesformer-hr-finetuned-k400":
						a_		:		Union[str, Any]				    =				torch.Size([1, 4_0_0])
						a_		:		Optional[int]				    =				torch.tensor([-0.9617, -3.7311, -3.7708])
			elif model_name == "timesformer-hr-finetuned-k600":
						a_		:		Dict				    =				torch.Size([1, 6_0_0])
						a_		:		str				    =				torch.tensor([2.5273, 0.7127, 1.8848])
			elif model_name == "timesformer-hr-finetuned-ssv2":
						a_		:		List[str]				    =				torch.Size([1, 1_7_4])
						a_		:		Dict				    =				torch.tensor([-3.6756, -0.7513, 0.7180])
			else:
						raise ValueError(f'''Model name not supported. Should be one of {model_names}''')

			# verify logits
			assert logits.shape == expected_shape
			assert torch.allclose(logits[0, :3]       ,					a__       ,					atol=1e-4)
			print("""Logits ok!""")

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model and image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a__)
						model.save_pretrained(a__)

			if push_to_hub:
						print("""Pushing to the hub...""")
						model.push_to_hub(f'''fcakyon/{model_name}''')


if __name__ == "__main__":
	__snake_case							:     Tuple 						=	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download""",
	    type=str,
	    help=(
	        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
	        """ download link."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument("""--model_name""", default="""timesformer-base-finetuned-k400""", type=str, help="""Name of the model.""")
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	__snake_case							:     Union[str, Any] 						=	parser.parse_args()
	convert_timesformer_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
	)





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()


