'''simple docstring'''





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCamelCase_							=					logging.get_logger(__name__)

UpperCamelCase_							=					OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

UpperCamelCase_							=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def 							_lowerCAmelCase (      __magic_name__	:    str	)							->     int:
						for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		lowercase   :       int								=model_type_to_module_name(__magic_name__	)

																		lowercase   :       int								=importlib.import_module(f'''.{module_name}'''      ,   '''transformers.models'''	)
																		try:
																								return getattr(__magic_name__      ,   __magic_name__	)
																		except AttributeError:
																								continue

						for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
												if getattr(__magic_name__      ,   '''__name__'''      ,   __magic_name__	) == class_name:
																		return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						lowercase   :       Any								=importlib.import_module('''transformers'''	)
						if hasattr(__magic_name__      ,   __magic_name__	):
												return getattr(__magic_name__      ,   __magic_name__	)

						return None






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, os.PathLike]      ,   __magic_name__	:    Optional[Union[str, os.PathLike]] = None      ,   __magic_name__	:    bool = False      ,   __magic_name__	:    bool = False      ,   __magic_name__	:    Optional[Dict[str, str]] = None      ,   __magic_name__	:    Optional[Union[bool, str]] = None      ,   __magic_name__	:    Optional[str] = None      ,   __magic_name__	:    bool = False      ,   **__magic_name__	:    List[str]      ,   )							->     str:
						lowercase   :       str								=get_file_from_repo(
						    __magic_name__      ,   __magic_name__      ,   cache_dir=__magic_name__      ,   force_download=__magic_name__      ,   resume_download=__magic_name__      ,   proxies=__magic_name__      ,   use_auth_token=__magic_name__      ,   revision=__magic_name__      ,   local_files_only=__magic_name__      ,   )
						if resolved_config_file is None:
												logger.info(
												    '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''	)
												return {}

						with open(__magic_name__      ,   encoding='''utf-8'''	) as reader:
												return json.load(__magic_name__	)







class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		Tuple      ):
										'''simple docstring'''
										raise EnvironmentError(
										    '''AutoFeatureExtractor is designed to be instantiated '''
										    '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'''      )







				@classmethod
				@replace_list_option_in_docstrings(UpperCAmelCase__      )
				def    lowerCamelCase_  (					cls    :		List[str]		,				UpperCAmelCase__    :		Optional[Any]		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=kwargs.pop('''config'''		,				UpperCAmelCase__      )
										lowercase   :       List[Any]								=kwargs.pop('''trust_remote_code'''		,				UpperCAmelCase__      )
										lowercase   :       Optional[int]								=True

										lowercase   :       Optional[int]								=FeatureExtractionMixin.get_feature_extractor_dict(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=config_dict.get('''feature_extractor_type'''		,				UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=None
										if "AutoFeatureExtractor" in config_dict.get('''auto_map'''		,				{}      ):
																lowercase   :       int								=config_dict['''auto_map''']['''AutoFeatureExtractor''']

										# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
										if feature_extractor_class is None and feature_extractor_auto_map is None:
																if not isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																						lowercase   :       Dict								=AutoConfig.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																# It could be in `config.feature_extractor_type``
																lowercase   :       int								=getattr(UpperCAmelCase__		,				'''feature_extractor_type'''		,				UpperCAmelCase__      )
																if hasattr(UpperCAmelCase__		,				'''auto_map'''      ) and "AutoFeatureExtractor" in config.auto_map:
																						lowercase   :       int								=config.auto_map['''AutoFeatureExtractor''']

										if feature_extractor_class is not None:
																lowercase   :       int								=feature_extractor_class_from_name(UpperCAmelCase__      )

										lowercase   :       List[str]								=feature_extractor_auto_map is not None
										lowercase   :       List[Any]								=feature_extractor_class is not None or type(UpperCAmelCase__      ) in FEATURE_EXTRACTOR_MAPPING
										lowercase   :       Any								=resolve_trust_remote_code(
										    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      )

										if has_remote_code and trust_remote_code:
																lowercase   :       Any								=get_class_from_dynamic_module(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				**UpperCAmelCase__      )
																lowercase   :       Union[str, Any]								=kwargs.pop('''code_revision'''		,				UpperCAmelCase__      )
																if os.path.isdir(UpperCAmelCase__      ):
																						feature_extractor_class.register_for_auto_class()
																return feature_extractor_class.from_dict(UpperCAmelCase__		,				**UpperCAmelCase__      )
										elif feature_extractor_class is not None:
																return feature_extractor_class.from_dict(UpperCAmelCase__		,				**UpperCAmelCase__      )
										# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
										elif type(UpperCAmelCase__      ) in FEATURE_EXTRACTOR_MAPPING:
																lowercase   :       Optional[int]								=FEATURE_EXTRACTOR_MAPPING[type(UpperCAmelCase__      )]
																return feature_extractor_class.from_dict(UpperCAmelCase__		,				**UpperCAmelCase__      )

										raise ValueError(
										    F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
										    F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
										    F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()      )}'''      )







				@staticmethod
				def    lowerCamelCase_  (					UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										FEATURE_EXTRACTOR_MAPPING.register(UpperCAmelCase__		,				UpperCAmelCase__      )








'''simple docstring'''





import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


UpperCamelCase_							=					logging.getLogger(__name__)

UpperCamelCase_							=					tf.data.AUTOTUNE






def 							_lowerCAmelCase (      )							->     Any:
						lowercase   :       Dict								=argparse.ArgumentParser(description='''Train a masked language model on TPU.'''	)
						parser.add_argument(
						    '''--pretrained_model_config'''      ,   type=__magic_name__      ,   default='''roberta-base'''      ,   help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!'''      ,   )
						parser.add_argument(
						    '''--tokenizer'''      ,   type=__magic_name__      ,   default='''unigram-tokenizer-wikitext'''      ,   help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'''      ,   )

						parser.add_argument(
						    '''--per_replica_batch_size'''      ,   type=__magic_name__      ,   default=8      ,   help='''Batch size per TPU core.'''      ,   )

						parser.add_argument(
						    '''--no_tpu'''      ,   action='''store_true'''      ,   help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'''      ,   )

						parser.add_argument(
						    '''--tpu_name'''      ,   type=__magic_name__      ,   help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'''      ,   default='''local'''      ,   )

						parser.add_argument(
						    '''--tpu_zone'''      ,   type=__magic_name__      ,   help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''      ,   )

						parser.add_argument(
						    '''--gcp_project'''      ,   type=__magic_name__      ,   help='''Google cloud project name. Only used for non-Colab TPU nodes.'''	)

						parser.add_argument(
						    '''--bfloat16'''      ,   action='''store_true'''      ,   help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'''      ,   )

						parser.add_argument(
						    '''--train_dataset'''      ,   type=__magic_name__      ,   help='''Path to training dataset to load. If the path begins with `gs://`'''
						    ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''      ,   )

						parser.add_argument(
						    '''--shuffle_buffer_size'''      ,   type=__magic_name__      ,   default=2**18      ,   help='''Size of the shuffle buffer (in samples)'''      ,   )

						parser.add_argument(
						    '''--eval_dataset'''      ,   type=__magic_name__      ,   help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
						    ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''      ,   )

						parser.add_argument(
						    '''--num_epochs'''      ,   type=__magic_name__      ,   default=1      ,   help='''Number of epochs to train for.'''      ,   )

						parser.add_argument(
						    '''--learning_rate'''      ,   type=__magic_name__      ,   default=1E-4      ,   help='''Learning rate to use for training.'''      ,   )

						parser.add_argument(
						    '''--weight_decay_rate'''      ,   type=__magic_name__      ,   default=1E-3      ,   help='''Weight decay rate to use for training.'''      ,   )

						parser.add_argument(
						    '''--max_length'''      ,   type=__magic_name__      ,   default=512      ,   help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''      ,   )

						parser.add_argument(
						    '''--mlm_probability'''      ,   type=__magic_name__      ,   default=0.1_5      ,   help='''Fraction of tokens to mask during training.'''      ,   )

						parser.add_argument('''--output_dir'''      ,   type=__magic_name__      ,   required=__magic_name__      ,   help='''Path to save model checkpoints to.'''	)
						parser.add_argument('''--hub_model_id'''      ,   type=__magic_name__      ,   help='''Model ID to upload to on the Hugging Face Hub.'''	)

						lowercase   :       Union[str, Any]								=parser.parse_args()
						return args






def 							_lowerCAmelCase (      __magic_name__	:    List[str]	)							->     List[Any]:
						try:
												if args.tpu_name:
																		lowercase   :       Dict								=tf.distribute.cluster_resolver.TPUClusterResolver(
																		    args.tpu_name      ,   zone=args.tpu_zone      ,   project=args.gcp_project	)
												else:
																		lowercase   :       Optional[int]								=tf.distribute.cluster_resolver.TPUClusterResolver()
						except ValueError:
												raise RuntimeError(
												    '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
												    '''--gcp_project. When running on a TPU VM, use --tpu_name local.'''	)

						tf.config.experimental_connect_to_cluster(__magic_name__	)
						tf.tpu.experimental.initialize_tpu_system(__magic_name__	)

						return tpu






def 							_lowerCAmelCase (      __magic_name__	:    Tuple	)							->     Union[str, Any]:
						lowercase   :       str								=0
						for file in file_list:
												lowercase   :       List[str]								=file.split('''/'''	)[-1]
												lowercase   :       Union[str, Any]								=re.search(R'''-\d+-(\d+)\.tfrecord'''      ,   __magic_name__	).group(1	)
												lowercase   :       int								=int(__magic_name__	)
												num_samples += sample_count

						return num_samples






def 							_lowerCAmelCase (      __magic_name__	:    List[str]      ,   __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    Optional[int]=None	)							->     str:
						lowercase   :       int								=count_samples(__magic_name__	)
						lowercase   :       Union[str, Any]								=tf.data.Dataset.from_tensor_slices(__magic_name__	)
						if shuffle:
												lowercase   :       Union[str, Any]								=dataset.shuffle(len(__magic_name__	)	)
						lowercase   :       Any								=tf.data.TFRecordDataset(__magic_name__      ,   num_parallel_reads=__magic_name__	)
						# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
						lowercase   :       Optional[int]								=dataset.apply(tf.data.experimental.assert_cardinality(__magic_name__	)	)
						lowercase   :       str								=dataset.map(__magic_name__      ,   num_parallel_calls=__magic_name__	)
						if shuffle:
												assert shuffle_buffer_size is not None
												lowercase   :       int								=dataset.shuffle(args.shuffle_buffer_size	)
						lowercase   :       Optional[int]								=dataset.batch(__magic_name__      ,   drop_remainder=__magic_name__	)
						lowercase   :       int								=dataset.map(__magic_name__      ,   num_parallel_calls=__magic_name__	)
						lowercase   :       Union[str, Any]								=dataset.prefetch(__magic_name__	)
						return dataset






def 							_lowerCAmelCase (      __magic_name__	:    Any	)							->     str:
						if not args.no_tpu:
												lowercase   :       Optional[Any]								=initialize_tpu(__magic_name__	)
												lowercase   :       Any								=tf.distribute.TPUStrategy(__magic_name__	)
						else:
												lowercase   :       Optional[Any]								=tf.distribute.OneDeviceStrategy(device='''/gpu:0'''	)

						if args.bfloataa:
												tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''	)

						lowercase   :       Any								=AutoTokenizer.from_pretrained(args.tokenizer	)
						lowercase   :       Union[str, Any]								=AutoConfig.from_pretrained(args.pretrained_model_config	)
						lowercase   :       Optional[Any]								=tokenizer.vocab_size

						lowercase   :       str								=tf.io.gfile.glob(os.path.join(args.train_dataset      ,   '''*.tfrecord'''	)	)
						if not training_records:
												raise ValueError(f'''No .tfrecord files found in {args.train_dataset}.'''	)
						lowercase   :       Optional[int]								=tf.io.gfile.glob(os.path.join(args.eval_dataset      ,   '''*.tfrecord'''	)	)
						if not eval_records:
												raise ValueError(f'''No .tfrecord files found in {args.eval_dataset}.'''	)

						lowercase   :       Any								=count_samples(__magic_name__	)

						lowercase   :       str								=num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
						lowercase   :       Union[str, Any]								=steps_per_epoch * args.num_epochs

						with strategy.scope():
												lowercase   :       List[Any]								=TFAutoModelForMaskedLM.from_config(__magic_name__	)
												model(model.dummy_inputs	)  # Pass some dummy inputs through the model to ensure all the weights are built
												lowercase		,  lowercase   :       Dict								=create_optimizer(
												    num_train_steps=__magic_name__      ,   num_warmup_steps=total_train_steps // 20      ,   init_lr=args.learning_rate      ,   weight_decay_rate=args.weight_decay_rate      ,   )

												# Transformers models compute the right loss for their task by default when labels are passed, and will
												# use this for training unless you specify your own loss function in compile().
												model.compile(optimizer=__magic_name__      ,   metrics=['''accuracy''']	)

						def decode_fn(__magic_name__	:    Optional[Any]	):
												lowercase   :       Union[str, Any]								={
												    '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa      ,   shape=(args.max_length,)	),
												    '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa      ,   shape=(args.max_length,)	),
												}
												return tf.io.parse_single_example(__magic_name__      ,   __magic_name__	)

						# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
						# use their methods in our data pipeline.
						lowercase   :       str								=DataCollatorForLanguageModeling(
						    tokenizer=__magic_name__      ,   mlm_probability=args.mlm_probability      ,   mlm=__magic_name__      ,   return_tensors='''tf'''	)

						def mask_with_collator(__magic_name__	:    Dict	):
												# TF really needs an isin() function
												lowercase   :       int								=(
												    ~tf.cast(batch['''attention_mask''']      ,   tf.bool	)
												    | (batch['''input_ids'''] == tokenizer.cls_token_id)
												    | (batch['''input_ids'''] == tokenizer.sep_token_id)
												)
												lowercase		,  lowercase   :       Union[str, Any]								=data_collator.tf_mask_tokens(
												    batch['''input_ids''']      ,   vocab_size=len(__magic_name__	)      ,   mask_token_id=tokenizer.mask_token_id      ,   special_tokens_mask=__magic_name__      ,   )
												return batch

						lowercase   :       List[str]								=args.per_replica_batch_size * strategy.num_replicas_in_sync

						lowercase   :       Dict								=prepare_dataset(
						    __magic_name__      ,   decode_fn=__magic_name__      ,   mask_fn=__magic_name__      ,   batch_size=__magic_name__      ,   shuffle=__magic_name__      ,   shuffle_buffer_size=args.shuffle_buffer_size      ,   )

						lowercase   :       Union[str, Any]								=prepare_dataset(
						    __magic_name__      ,   decode_fn=__magic_name__      ,   mask_fn=__magic_name__      ,   batch_size=__magic_name__      ,   shuffle=__magic_name__      ,   )

						lowercase   :       Tuple								=[]
						if args.hub_model_id:
												callbacks.append(
												    PushToHubCallback(output_dir=args.output_dir      ,   hub_model_id=args.hub_model_id      ,   tokenizer=__magic_name__	)	)

						model.fit(
						    __magic_name__      ,   validation_data=__magic_name__      ,   epochs=args.num_epochs      ,   callbacks=__magic_name__      ,   )

						model.save_pretrained(args.output_dir	)


if __name__ == "__main__":
							UpperCamelCase_							=					parse_args()
							main(args)





