




"""simple docstring"""







import argparse
import logging
import os
import re

import tensorflow as tf

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


__A   :  str					=      logging.getLogger(__name__)

__A   :  Tuple					=      tf.data.AUTOTUNE

def 						snake_case__       (					)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				argparse.ArgumentParser(description="Train a masked language model on TPU."				)
		parser.add_argument(
		    "--pretrained_model_config",  type=_lowerCamelCase,  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=_lowerCamelCase,  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=_lowerCamelCase,  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=_lowerCamelCase,  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=_lowerCamelCase,  help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.",  )

		parser.add_argument(
		    "--gcp_project",  type=_lowerCamelCase,  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=_lowerCamelCase,  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=_lowerCamelCase,  default=2**18,  help="Size of the shuffle buffer (in samples)",  )

		parser.add_argument(
		    "--eval_dataset",  type=_lowerCamelCase,  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=_lowerCamelCase,  default=1,  help="Number of epochs to train for.",  )

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

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

		parser.add_argument(
		    "--max_length",  type=_lowerCamelCase,  default=5_12,  help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py",  )

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

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

		__lowercase				:		Optional[Any]												=				parser.parse_args()
		return args

def 						snake_case__       (					_lowerCamelCase				)			->Union[str, Any]:






		"""simple docstring"""


		try:
				if args.tpu_name:
						__lowercase				:		Tuple												=				tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name,  zone=args.tpu_zone,  project=args.gcp_project				)
				else:
						__lowercase				:		Tuple												=				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(_lowerCamelCase				)
		tf.tpu.experimental.initialize_tpu_system(_lowerCamelCase				)

		return tpu

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		__lowercase				:		Tuple												=				0
		for file in file_list:
				__lowercase				:		Any												=				file.split("/"				)[-1]
				__lowercase				:		Optional[int]												=				re.search(R"-\d+-(\d+)\.tfrecord",  _lowerCamelCase				).group(1				)
				__lowercase				:		List[Any]												=				int(_lowerCamelCase				)
				num_samples += sample_count

		return num_samples

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=None				)			->int:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		if not args.no_tpu:
				__lowercase				:		Any												=				initialize_tpu(_lowerCamelCase				)
				__lowercase				:		Union[str, Any]												=				tf.distribute.TPUStrategy(_lowerCamelCase				)
		else:
				__lowercase				:		int												=				tf.distribute.OneDeviceStrategy(device="/gpu:0"				)

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

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

		__lowercase				:		Optional[Any]												=				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				:		Tuple												=				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				:		Union[str, Any]												=				count_samples(_lowerCamelCase				)

		__lowercase				:		List[str]												=				num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
		__lowercase				:		str												=				steps_per_epoch * args.num_epochs

		with strategy.scope():
				__lowercase				:		Optional[Any]												=				TFAutoModelForMaskedLM.from_config(_lowerCamelCase				)
				model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
				__lowercase						,__lowercase				:		Optional[int]												=				create_optimizer(
				    num_train_steps=_lowerCamelCase,  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=_lowerCamelCase,  metrics=["accuracy"]				)

		def decode_fn(_lowerCamelCase				):
				__lowercase				:		List[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(_lowerCamelCase,  _lowerCamelCase				)

		# 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				:		Optional[int]												=				DataCollatorForLanguageModeling(
		    tokenizer=_lowerCamelCase,  mlm_probability=args.mlm_probability,  mlm=_lowerCamelCase,  return_tensors="tf"				)

		def mask_with_collator(_lowerCamelCase				):
				# TF really needs an isin() function
				__lowercase				:		List[str]												=				(
				    ~tf.cast(batch["attention_mask"],  tf.bool				)
				    | (batch["input_ids"] == tokenizer.cls_token_id)
				    | (batch["input_ids"] == tokenizer.sep_token_id)
				)
				__lowercase						,__lowercase				:		Optional[int]												=				data_collator.tf_mask_tokens(
				    batch["input_ids"],  vocab_size=len(_lowerCamelCase				),  mask_token_id=tokenizer.mask_token_id,  special_tokens_mask=_lowerCamelCase,  )
				return batch

		__lowercase				:		Dict												=				args.per_replica_batch_size * strategy.num_replicas_in_sync

		__lowercase				:		Optional[Any]												=				prepare_dataset(
		    _lowerCamelCase,  decode_fn=_lowerCamelCase,  mask_fn=_lowerCamelCase,  batch_size=_lowerCamelCase,  shuffle=_lowerCamelCase,  shuffle_buffer_size=args.shuffle_buffer_size,  )

		__lowercase				:		List[str]												=				prepare_dataset(
		    _lowerCamelCase,  decode_fn=_lowerCamelCase,  mask_fn=_lowerCamelCase,  batch_size=_lowerCamelCase,  shuffle=_lowerCamelCase,  )

		__lowercase				:		int												=				[]
		if args.hub_model_id:
				callbacks.append(
				    PushToHubCallback(output_dir=args.output_dir,  hub_model_id=args.hub_model_id,  tokenizer=_lowerCamelCase				)				)

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

		model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
						__A   :  str					=      parse_args()
						main(args)






"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


__A   :  Optional[int]					=      {
    'Acehnese Arabic': 'ace_Arab',
    'Acehnese Latin': 'ace_Latn',
    'Mesopotamian Arabic': 'acm_Arab',
    'Ta\'izzi-Adeni Arabic': 'acq_Arab',
    'Tunisian Arabic': 'aeb_Arab',
    'Afrikaans': 'afr_Latn',
    'South Levantine Arabic': 'ajp_Arab',
    'Akan': 'aka_Latn',
    'Amharic': 'amh_Ethi',
    'North Levantine Arabic': 'apc_Arab',
    'Modern Standard Arabic': 'arb_Arab',
    'Modern Standard Arabic Romanized': 'arb_Latn',
    'Najdi Arabic': 'ars_Arab',
    'Moroccan Arabic': 'ary_Arab',
    'Egyptian Arabic': 'arz_Arab',
    'Assamese': 'asm_Beng',
    'Asturian': 'ast_Latn',
    'Awadhi': 'awa_Deva',
    'Central Aymara': 'ayr_Latn',
    'South Azerbaijani': 'azb_Arab',
    'North Azerbaijani': 'azj_Latn',
    'Bashkir': 'bak_Cyrl',
    'Bambara': 'bam_Latn',
    'Balinese': 'ban_Latn',
    'Belarusian': 'bel_Cyrl',
    'Bemba': 'bem_Latn',
    'Bengali': 'ben_Beng',
    'Bhojpuri': 'bho_Deva',
    'Banjar Arabic': 'bjn_Arab',
    'Banjar Latin': 'bjn_Latn',
    'Standard Tibetan': 'bod_Tibt',
    'Bosnian': 'bos_Latn',
    'Buginese': 'bug_Latn',
    'Bulgarian': 'bul_Cyrl',
    'Catalan': 'cat_Latn',
    'Cebuano': 'ceb_Latn',
    'Czech': 'ces_Latn',
    'Chokwe': 'cjk_Latn',
    'Central Kurdish': 'ckb_Arab',
    'Crimean Tatar': 'crh_Latn',
    'Welsh': 'cym_Latn',
    'Danish': 'dan_Latn',
    'German': 'deu_Latn',
    'Southwestern Dinka': 'dik_Latn',
    'Dyula': 'dyu_Latn',
    'Dzongkha': 'dzo_Tibt',
    'Greek': 'ell_Grek',
    'English': 'eng_Latn',
    'Esperanto': 'epo_Latn',
    'Estonian': 'est_Latn',
    'Basque': 'eus_Latn',
    'Ewe': 'ewe_Latn',
    'Faroese': 'fao_Latn',
    'Fijian': 'fij_Latn',
    'Finnish': 'fin_Latn',
    'Fon': 'fon_Latn',
    'French': 'fra_Latn',
    'Friulian': 'fur_Latn',
    'Nigerian Fulfulde': 'fuv_Latn',
    'Scottish Gaelic': 'gla_Latn',
    'Irish': 'gle_Latn',
    'Galician': 'glg_Latn',
    'Guarani': 'grn_Latn',
    'Gujarati': 'guj_Gujr',
    'Haitian Creole': 'hat_Latn',
    'Hausa': 'hau_Latn',
    'Hebrew': 'heb_Hebr',
    'Hindi': 'hin_Deva',
    'Chhattisgarhi': 'hne_Deva',
    'Croatian': 'hrv_Latn',
    'Hungarian': 'hun_Latn',
    'Armenian': 'hye_Armn',
    'Igbo': 'ibo_Latn',
    'Ilocano': 'ilo_Latn',
    'Indonesian': 'ind_Latn',
    'Icelandic': 'isl_Latn',
    'Italian': 'ita_Latn',
    'Javanese': 'jav_Latn',
    'Japanese': 'jpn_Jpan',
    'Kabyle': 'kab_Latn',
    'Jingpho': 'kac_Latn',
    'Kamba': 'kam_Latn',
    'Kannada': 'kan_Knda',
    'Kashmiri Arabic': 'kas_Arab',
    'Kashmiri Devanagari': 'kas_Deva',
    'Georgian': 'kat_Geor',
    'Central Kanuri Arabic': 'knc_Arab',
    'Central Kanuri Latin': 'knc_Latn',
    'Kazakh': 'kaz_Cyrl',
    'Kabiyè': 'kbp_Latn',
    'Kabuverdianu': 'kea_Latn',
    'Khmer': 'khm_Khmr',
    'Kikuyu': 'kik_Latn',
    'Kinyarwanda': 'kin_Latn',
    'Kyrgyz': 'kir_Cyrl',
    'Kimbundu': 'kmb_Latn',
    'Northern Kurdish': 'kmr_Latn',
    'Kikongo': 'kon_Latn',
    'Korean': 'kor_Hang',
    'Lao': 'lao_Laoo',
    'Ligurian': 'lij_Latn',
    'Limburgish': 'lim_Latn',
    'Lingala': 'lin_Latn',
    'Lithuanian': 'lit_Latn',
    'Lombard': 'lmo_Latn',
    'Latgalian': 'ltg_Latn',
    'Luxembourgish': 'ltz_Latn',
    'Luba-Kasai': 'lua_Latn',
    'Ganda': 'lug_Latn',
    'Luo': 'luo_Latn',
    'Mizo': 'lus_Latn',
    'Standard Latvian': 'lvs_Latn',
    'Magahi': 'mag_Deva',
    'Maithili': 'mai_Deva',
    'Malayalam': 'mal_Mlym',
    'Marathi': 'mar_Deva',
    'Minangkabau Arabic ': 'min_Arab',
    'Minangkabau Latin': 'min_Latn',
    'Macedonian': 'mkd_Cyrl',
    'Plateau Malagasy': 'plt_Latn',
    'Maltese': 'mlt_Latn',
    'Meitei Bengali': 'mni_Beng',
    'Halh Mongolian': 'khk_Cyrl',
    'Mossi': 'mos_Latn',
    'Maori': 'mri_Latn',
    'Burmese': 'mya_Mymr',
    'Dutch': 'nld_Latn',
    'Norwegian Nynorsk': 'nno_Latn',
    'Norwegian Bokmål': 'nob_Latn',
    'Nepali': 'npi_Deva',
    'Northern Sotho': 'nso_Latn',
    'Nuer': 'nus_Latn',
    'Nyanja': 'nya_Latn',
    'Occitan': 'oci_Latn',
    'West Central Oromo': 'gaz_Latn',
    'Odia': 'ory_Orya',
    'Pangasinan': 'pag_Latn',
    'Eastern Panjabi': 'pan_Guru',
    'Papiamento': 'pap_Latn',
    'Western Persian': 'pes_Arab',
    'Polish': 'pol_Latn',
    'Portuguese': 'por_Latn',
    'Dari': 'prs_Arab',
    'Southern Pashto': 'pbt_Arab',
    'Ayacucho Quechua': 'quy_Latn',
    'Romanian': 'ron_Latn',
    'Rundi': 'run_Latn',
    'Russian': 'rus_Cyrl',
    'Sango': 'sag_Latn',
    'Sanskrit': 'san_Deva',
    'Santali': 'sat_Olck',
    'Sicilian': 'scn_Latn',
    'Shan': 'shn_Mymr',
    'Sinhala': 'sin_Sinh',
    'Slovak': 'slk_Latn',
    'Slovenian': 'slv_Latn',
    'Samoan': 'smo_Latn',
    'Shona': 'sna_Latn',
    'Sindhi': 'snd_Arab',
    'Somali': 'som_Latn',
    'Southern Sotho': 'sot_Latn',
    'Spanish': 'spa_Latn',
    'Tosk Albanian': 'als_Latn',
    'Sardinian': 'srd_Latn',
    'Serbian': 'srp_Cyrl',
    'Swati': 'ssw_Latn',
    'Sundanese': 'sun_Latn',
    'Swedish': 'swe_Latn',
    'Swahili': 'swh_Latn',
    'Silesian': 'szl_Latn',
    'Tamil': 'tam_Taml',
    'Tatar': 'tat_Cyrl',
    'Telugu': 'tel_Telu',
    'Tajik': 'tgk_Cyrl',
    'Tagalog': 'tgl_Latn',
    'Thai': 'tha_Thai',
    'Tigrinya': 'tir_Ethi',
    'Tamasheq Latin': 'taq_Latn',
    'Tamasheq Tifinagh': 'taq_Tfng',
    'Tok Pisin': 'tpi_Latn',
    'Tswana': 'tsn_Latn',
    'Tsonga': 'tso_Latn',
    'Turkmen': 'tuk_Latn',
    'Tumbuka': 'tum_Latn',
    'Turkish': 'tur_Latn',
    'Twi': 'twi_Latn',
    'Central Atlas Tamazight': 'tzm_Tfng',
    'Uyghur': 'uig_Arab',
    'Ukrainian': 'ukr_Cyrl',
    'Umbundu': 'umb_Latn',
    'Urdu': 'urd_Arab',
    'Northern Uzbek': 'uzn_Latn',
    'Venetian': 'vec_Latn',
    'Vietnamese': 'vie_Latn',
    'Waray': 'war_Latn',
    'Wolof': 'wol_Latn',
    'Xhosa': 'xho_Latn',
    'Eastern Yiddish': 'ydd_Hebr',
    'Yoruba': 'yor_Latn',
    'Yue Chinese': 'yue_Hant',
    'Chinese Simplified': 'zho_Hans',
    'Chinese Traditional': 'zho_Hant',
    'Standard Malay': 'zsm_Latn',
    'Zulu': 'zul_Latn',
}




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     str       =      "facebook/nllb-200-distilled-600M"
				__UpperCAmelCase :     Tuple       =      (
				    "This is a tool that translates text from a language to another. It takes three inputs: `text`, which should "
				    "be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, "
				    "which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in "
				    "plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."
				)
				__UpperCAmelCase :     str       =      "translator"
				__UpperCAmelCase :     Optional[int]       =      AutoTokenizer
				__UpperCAmelCase :     Dict       =      AutoModelForSeqaSeqLM
				__UpperCAmelCase :     Tuple       =      LANGUAGE_CODES

				__UpperCAmelCase :     int       =      ["text", "text", "text"]
				__UpperCAmelCase :     int       =      ["text"]



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Any						):
						if src_lang not in self.lang_to_code:
								raise ValueError(f'{src_lang} is not a supported language.'						)
						if tgt_lang not in self.lang_to_code:
								raise ValueError(f'{tgt_lang} is not a supported language.'						)
						__lowercase				:		List[str]												=				self.lang_to_code[src_lang]
						__lowercase				:		List[str]												=				self.lang_to_code[tgt_lang]
						return self.pre_processor._build_translation_inputs(
						    lowercase__    ,   return_tensors="pt"    ,   src_lang=lowercase__    ,   tgt_lang=lowercase__						)



				def       snake_case    (     self					:	str    ,   lowercase__					:	Tuple						):
						return self.model.generate(**lowercase__						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[Any]						):
						return self.post_processor.decode(outputs[0].tolist()    ,   skip_special_tokens=lowercase__						)
