
import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


lowercase		:						str =					{}
for task, _ in pipeline_test_mapping.items():
			lowercase		:						Tuple =					{'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
lowercase		:						Union[str, Any] =					{
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Tuple:


					'''simple docstring'''

					if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
										return "pt"
					elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
										return "tf"
					elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
										return "flax"
					else:
										return None


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					# Use the cached results
					if PIPELINE_TEST_MAPPING[task].get(lowerCamelCase_	,				lowerCamelCase_ ) is not None:
										return PIPELINE_TEST_MAPPING[task][framework]

					__a:						List[str]		    =					pipeline_test_mapping[task]["""test"""]
					__a:						Tuple		    =					None

					if framework == "pt":
										__a:						Optional[Any]		    =					getattr(lowerCamelCase_	,				"""model_mapping"""	,				lowerCamelCase_ )
					elif framework == "tf":
										__a:						Union[str, Any]		    =					getattr(lowerCamelCase_	,				"""tf_model_mapping"""	,				lowerCamelCase_ )

					if mapping is not None:
										__a:						List[Any]		    =					dict(mapping.items() )

					# cache the results
					__a:						List[Any]		    =					mapping
					return mapping


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					__a:						Tuple		    =					get_framework(lowerCamelCase_ )
					if framework is None:
										return None
					__a:						List[str]		    =					get_mapping_for_task(lowerCamelCase_	,				lowerCamelCase_ )
					if mapping is None:
										return None

					__a:						Tuple		    =					list({model_class.config_class for model_class in test_class.all_model_classes} )
					if len(lowerCamelCase_ ) != 1:
										raise ValueError("""There should be exactly one configuration class from `test_class.all_model_classes`.""" )

					# This could be a list/tuple of model classes, but it's rare.
					__a:						Union[str, Any]		    =					mapping.get(config_classes[0]	,				lowerCamelCase_ )
					if isinstance(lowerCamelCase_	,				(tuple, list) ):
										__a:						List[str]		    =					sorted(lowerCamelCase_	,				key=lambda lowerCamelCase_ : x.__name__ )

					return model_class


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					__a:						List[Any]		    =					[(task, get_model_for_pipeline_test(lowerCamelCase_	,				lowerCamelCase_ )) for task in pipeline_test_mapping]
					__a:						Any		    =					sorted([(task, model) for task, model in mapping if model is not None]	,				key=lambda lowerCamelCase_ : x[0] )

					return dict(lowerCamelCase_ )


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					__a:						Tuple		    =					get_framework(lowerCamelCase_ )
					if framework == "pt":
										__a:						Tuple		    =					"""torch"""
					__a:						Union[str, Any]		    =					"""{}"""

					__a:						str		    =					get_pipeline_model_mapping(lowerCamelCase_ )
					if len(lowerCamelCase_ ) == 0:
										return ""

					__a:						Optional[int]		    =					[]
					for task, model_classes in mapping.items():
										if isinstance(lowerCamelCase_	,				(tuple, list) ):
															# A list/tuple of model classes
															__a:						Optional[Any]		    =					"""(""" + """, """.join([x.__name__ for x in model_classes] ) + """)"""
										else:
															# A single model class
															__a:						List[str]		    =					model_classes.__name__
										texts.append(f"""\"{task}\": {value}""" )
					__a:						str		    =					"""{""" + """, """.join(lowerCamelCase_ ) + """}"""
					__a:						Any		    =					f"""pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"""

					return text


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Tuple:


					'''simple docstring'''
					__a:						Dict		    =					{"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
					if not issubclass(lowerCamelCase_	,				unittest.TestCase ):
										return False
					return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__] ) ) > 0


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					__a:						Tuple		    =					[x for x in get_test_classes(lowerCamelCase_ ) if is_valid_test_class(lowerCamelCase_ )]

					__a:						List[Any]		    =					None
					for test_class in test_classes:
										# If a test class has defined `pipeline_model_mapping`, let's take it
										if getattr(lowerCamelCase_	,				"""pipeline_model_mapping"""	,				lowerCamelCase_ ) is not None:
															__a:						Any		    =					test_class
															break
    # Take the test class with the shortest name (just a heuristic)
					if target_test_class is None and len(lowerCamelCase_ ) > 0:
										__a:						Union[str, Any]		    =					sorted(lowerCamelCase_	,				key=lambda lowerCamelCase_ : (len(x.__name__ ), x.__name__) )[0]

					return target_test_class


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					__a:						Tuple		    =					start_idx
					for idx, line in enumerate(lines[start_idx:] ):
										__a:						Tuple		    =					len(lowerCamelCase_ ) - len(line.lstrip() )
										if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
															__a:						List[str]		    =					start_idx + idx
										elif idx > 0 and indent <= indent_level:
															# Outside the definition block of `pipeline_model_mapping`
															break

					return end_idx


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_=False )     ->			Optional[Any]:


					'''simple docstring'''
					if getattr(lowerCamelCase_	,				"""pipeline_model_mapping"""	,				lowerCamelCase_ ) is not None:
										if not overwrite:
															return "", -1

					__a:						Any		    =					get_pipeline_model_mapping_string(lowerCamelCase_ )
					if len(lowerCamelCase_ ) == 0:
										return "", -1
					__a:						List[Any]		    =					line_to_add + """\n"""

					# The code defined the class `test_class`
					__a:						int		    =					inspect.getsourcelines(lowerCamelCase_ )
					# `inspect` gives the code for an object, including decorator(s) if any.
					# We (only) need the exact line of the class definition.
					for idx, line in enumerate(lowerCamelCase_ ):
										if line.lstrip().startswith("""class """ ):
															__a:						int		    =					class_lines[idx:]
															class_start_line_no += idx
															break
					__a:						int		    =					class_start_line_no + len(lowerCamelCase_ ) - 1

					# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
					# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
					# block that appears in a `test_class`.
					__a:						Optional[int]		    =					None
					# The indent level of the line at `class_lines[start_idx]` (if defined)
					__a:						int		    =					0
					# To record if `pipeline_model_mapping` is found in `test_class`.
					__a:						int		    =					None
					for idx, line in enumerate(lowerCamelCase_ ):
										if line.strip().startswith("""all_model_classes = """ ):
															__a:						Optional[int]		    =					len(lowerCamelCase_ ) - len(line.lstrip() )
															__a:						int		    =					idx
										elif line.strip().startswith("""all_generative_model_classes = """ ):
															__a:						Dict		    =					len(lowerCamelCase_ ) - len(line.lstrip() )
															__a:						Dict		    =					idx
										elif line.strip().startswith("""pipeline_model_mapping = """ ):
															__a:						Any		    =					len(lowerCamelCase_ ) - len(line.lstrip() )
															__a:						List[Any]		    =					idx
															__a:						Any		    =					line
															break

					if start_idx is None:
										return "", -1
					# Find the ending index (inclusive) of the above found block.
					__a:						Union[str, Any]		    =					find_block_ending(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )

					# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
					# `is_timm_available()` instead of `is_torch_available()`.
					# Keep leading and trailing whitespaces
					__a:						Dict		    =					re.compile(r"""\s(is_\S+?_available\(\))\s""" )
					for line in class_lines[start_idx : end_idx + 1]:
										__a:						Union[str, Any]		    =					r.search(lowerCamelCase_ )
										if backend_condition is not None:
															# replace the leading and trailing whitespaces to the space character " ".
															__a:						Optional[int]		    =					""" """ + backend_condition[0][1:-1] + """ """
															__a:						Union[str, Any]		    =					r.sub(lowerCamelCase_	,				lowerCamelCase_ )
															break

					if def_line is None:
										# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
										# `all_model_classes` or `all_generative_model_classes`.
										__a:						Union[str, Any]		    =					end_idx
					else:
										# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
										__a:						Any		    =					start_idx - 1
										# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
										for idx in range(lowerCamelCase_	,				end_idx + 1 ):
															# These lines are going to be removed before writing to the test file.
															__a:						int		    =					None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
					__a:						Optional[int]		    =					[x.__name__ for x in test_class.__bases__]
					if "PipelineTesterMixin" not in parent_classes:
										# Put `PipelineTesterMixin` just before `unittest.TestCase`
										__a:						Optional[int]		    =					[x for x in parent_classes if x != """TestCase"""] + ["""PipelineTesterMixin"""]
										if "TestCase" in parent_classes:
															# Here we **assume** the original string is always with `unittest.TestCase`.
															_parent_classes.append("""unittest.TestCase""" )
										__a:						Any		    =					""", """.join(_parent_classes )
										for idx, line in enumerate(lowerCamelCase_ ):
															# Find the ending of the declaration of `test_class`
															if line.strip().endswith("""):""" ):
																				# mark the lines of the declaration of `test_class` to be removed
																				for _idx in range(idx + 1 ):
																									__a:						List[Any]		    =					None  # noqa
																				break
        # Add the new, one-line, class declaration for `test_class`
										__a:						Union[str, Any]		    =					f"""class {test_class.__name__}({parent_classes}):\n"""

					# Add indentation
					__a:						Dict		    =					""" """ * indent_level + line_to_add
					# Insert `pipeline_model_mapping` to `class_lines`.
					# (The line at `target_idx` should be kept by definition!)
					__a:						Any		    =					class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
					# Remove the lines that are marked to be removed
					__a:						Any		    =					[x for x in class_lines if x is not None]

					# Move from test class to module (in order to write to the test file)
					__a:						int		    =					inspect.getsourcelines(inspect.getmodule(lowerCamelCase_ ) )[0]
					# Be careful with the 1-off between line numbers and array indices
					__a:						Any		    =					module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
					__a:						List[str]		    =					"""""".join(lowerCamelCase_ )

					__a:						Union[str, Any]		    =					inspect.getsourcefile(lowerCamelCase_ )
					with open(lowerCamelCase_	,				"""w"""	,				encoding="""UTF-8"""	,				newline="""\n""" ) as fp:
										fp.write(lowerCamelCase_ )

					return line_to_add


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_=False )     ->			Optional[Any]:


					'''simple docstring'''
					__a:						Tuple		    =					find_test_class(lowerCamelCase_ )
					if test_class:
										add_pipeline_model_mapping(lowerCamelCase_	,				overwrite=lowerCamelCase_ )


if __name__ == "__main__":
			lowercase		:						str =					argparse.ArgumentParser()
			parser.add_argument(
			    '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
			)
			parser.add_argument(
			    '--all',
			    action='store_true',
			    help='If to check and modify all test files.',
			)
			parser.add_argument(
			    '--overwrite',
			    action='store_true',
			    help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
			)
			lowercase		:						str =					parser.parse_args()

			if not args.all and not args.test_file:
						raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
			elif args.all and args.test_file:
						raise ValueError('Only one of `--test_file` and `--all` could be specified.')

			lowercase		:						Tuple =					[]
			if args.test_file:
						lowercase		:						List[Any] =					[args.test_file]
			else:
						lowercase		:						Union[str, Any] =					os.path.join('tests', 'models', '**', 'test_modeling_*.py')
						for test_file in glob.glob(pattern):
									# `Flax` is not concerned at this moment
									if not test_file.startswith('test_modeling_flax_'):
												test_files.append(test_file)

			for test_file in test_files:
						if test_file in TEST_FILE_TO_IGNORE:
									print(F"[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.")
									continue
						add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)


"""simple docstring"""
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'):
			lowercase		:						str =					True
			from torch.cuda.amp import autocast


lowercase		:						int =					logging.getLogger(__name__)




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to log verbose messages or not.'''} ,				)






def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


					'''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:						List[Any]		    =					logging.WARNING
					if model_args.verbose_logging:
										__a:						Dict		    =					logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank ):
										__a:						Optional[int]		    =					logging.INFO
					logger.setLevel(lowerCamelCase_ )




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






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						False
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						"|"
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						None






							@classmethod
							def 							__lowercase  (					cls						:			List[Any]						, snake_case__						:			str    ):






												"""simple docstring"""



												if name == "librispeech":
																	return cls()
												if name == "timit":
																	return cls(
																	    do_lower_case=snake_case__						, translation_table=str.maketrans({"""-""": """ """}    )						, )
												if name == "buckwalter":
																	__a:						List[Any]		    =					{
																	    """-""": """ """,  # sometimes used to represent pauses
																	    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
																	}
																	return cls(
																	    vocab_file=pathlib.Path(snake_case__    ).parent.joinpath("""vocab/buckwalter.json"""    )						, word_delimiter_token="""/"""						, translation_table=str.maketrans(snake_case__    )						, words_to_remove={"""sil"""}						, untransliterator=arabic.buckwalter.untransliterate						, )
												raise ValueError(f"""Unsupported orthography: '{name}'."""    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			str    ):






												"""simple docstring"""



												if len(self.translation_table    ) > 0:
																	__a:						Optional[int]		    =					text.translate(self.translation_table    )
												if len(self.words_to_remove    ) == 0:
																	__a:						Tuple		    =					""" """.join(text.split()    )  # clean up whitespaces
												else:
																	__a:						int		    =					""" """.join(w for w in text.split() if w not in self.words_to_remove    )  # and clean up whilespaces
												return text






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			ModelArguments    ):






												"""simple docstring"""



												__a:						Dict		    =					WavaVecaFeatureExtractor.from_pretrained(
												    model_args.model_name_or_path						, cache_dir=model_args.cache_dir    )
												if self.vocab_file:
																	__a:						Dict		    =					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(snake_case__						, snake_case__    )






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						True
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None






							def __call__(					self						:			Optional[Any]						, snake_case__						:			List[Dict[str, Union[List[int], torch.Tensor]]]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					[{"""input_values""": feature["""input_values"""]} for feature in features]
												__a:						Optional[Any]		    =					[{"""input_ids""": feature["""labels"""]} for feature in features]

												__a:						Optional[Any]		    =					self.processor.pad(
												    snake_case__						, padding=self.padding						, max_length=self.max_length						, pad_to_multiple_of=self.pad_to_multiple_of						, return_tensors="""pt"""						, )
												__a:						str		    =					self.processor.pad(
												    labels=snake_case__						, 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:						Any		    =					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )						, -1_0_0    )

												__a:						List[Any]		    =					labels

												return batch






class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			nn.Module						, snake_case__						:			Dict[str, Union[torch.Tensor, Any]]    ):






												"""simple docstring"""



												model.train()
												__a:						int		    =					self._prepare_inputs(snake_case__    )

												if self.use_amp:
																	with autocast():
																						__a:						List[str]		    =					self.compute_loss(snake_case__						, snake_case__    )
												else:
																	__a:						Tuple		    =					self.compute_loss(snake_case__						, snake_case__    )

												if self.args.n_gpu > 1:
																	if model.module.config.ctc_loss_reduction == "mean":
																						__a:						Tuple		    =					loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																						__a:						List[str]		    =					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(snake_case__    ).backward()
												elif self.use_apex:
																	with amp.scale_loss(snake_case__						, self.optimizer    ) as scaled_loss:
																						scaled_loss.backward()
												elif self.deepspeed:
																	self.deepspeed.backward(snake_case__    )
												else:
																	loss.backward()

												return loss.detach()






def     lowerCamelCase_    (	)     ->			List[Any]:


					'''simple docstring'''
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					__a:						int		    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

					__a    ,				__a    ,				__a:						List[str]		    =					parser.parse_args_into_dataclasses()
					configure_logger(lowerCamelCase_	,				lowerCamelCase_ )

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

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

					__a:						str		    =					train_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )
					__a:						Union[str, Any]		    =					val_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )

					if data_args.max_duration_in_seconds is not None:

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

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

					logger.warning(f"""Updated {len(lowerCamelCase_ )} 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(lowerCamelCase_ ):
										# 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:						Tuple		    =					processor(
										    audio=batch["""speech"""]	,				text=batch[data_args.target_text_column]	,				sampling_rate=batch["""sampling_rate"""][0] )
										batch.update(lowerCamelCase_ )
										return batch

					__a:						Union[str, Any]		    =					train_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)
					__a:						Tuple		    =					val_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)

					__a:						Union[str, Any]		    =					DataCollatorCTCWithPadding(processor=lowerCamelCase_	,				padding=lowerCamelCase_ )

					def compute_metrics(lowerCamelCase_ ):
										__a:						Any		    =					pred.predictions
										__a:						List[Any]		    =					np.argmax(lowerCamelCase_	,				axis=-1 )

										__a:						int		    =					processor.tokenizer.pad_token_id

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

										__a:						Optional[int]		    =					wer_metric.compute(predictions=lowerCamelCase_	,				references=lowerCamelCase_ )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					__a:						Any		    =					CTCTrainer(
					    model=lowerCamelCase_	,				data_collator=lowerCamelCase_	,				args=lowerCamelCase_	,				compute_metrics=lowerCamelCase_	,				train_dataset=lowerCamelCase_	,				eval_dataset=lowerCamelCase_	,				tokenizer=processor.feature_extractor	,				)

					trainer.train()


if __name__ == "__main__":
			main()
