
import argparse
import logging
import os
import re

import tensorflow as tf

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


SCREAMING_SNAKE_CASE							:Union[str, Any]										=    logging.getLogger(__name__)

SCREAMING_SNAKE_CASE							:Tuple										=    tf.data.AUTOTUNE


def 						UpperCAmelCase      (     )		->     Tuple:







				"""simple docstring"""



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

				parser.add_argument(
				    "--gcp_project"		,       type=a_		,       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=a_		,       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=a_		,       default=2**1_8		,       help="Size of the shuffle buffer (in samples)"		,       )

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

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

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

				parser.add_argument(
				    "--max_length"		,       type=a_		,       default=5_1_2		,       help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"		,       )

				parser.add_argument(
				    "--mlm_probability"		,       type=a_		,       default=0.15		,       help="Fraction of tokens to mask during training."		,       )

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

				__A             =			parser.parse_args()
				return args


def 						UpperCAmelCase      (     a_						)		->     Union[str, Any]:







				"""simple docstring"""



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

				return tpu


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				for file in file_list:
								__A             =			file.split("/"						)[-1]
								__A             =			re.search(r"-\d+-(\d+)\.tfrecord"		,       a_						).group(1						)
								__A             =			int(a_						)
								num_samples += sample_count

				return num_samples


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_		,       a_=None						)		->     str:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_						)		->     Optional[Any]:







				"""simple docstring"""



				if not args.no_tpu:
								__A             =			initialize_tpu(a_						)
								__A             =			tf.distribute.TPUStrategy(a_						)
				else:
								__A             =			tf.distribute.OneDeviceStrategy(device="/gpu:0"						)

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

				__A             =			AutoTokenizer.from_pretrained(args.tokenizer						)
				__A             =			AutoConfig.from_pretrained(args.pretrained_model_config						)
				__A             =			tokenizer.vocab_size

				__A             =			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}.'''						)
				__A             =			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}.'''						)

				__A             =			count_samples(a_						)

				__A             =			num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
				__A             =			steps_per_epoch * args.num_epochs

				with strategy.scope():
								__A             =			TFAutoModelForMaskedLM.from_config(a_						)
								model(model.dummy_inputs						)  # Pass some dummy inputs through the model to ensure all the weights are built
								__A      ,							__A             =			create_optimizer(
								    num_train_steps=a_		,       num_warmup_steps=total_train_steps // 2_0		,       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=a_		,       metrics=["accuracy"]						)

				def decode_fn(a_						):
								__A             =			{
								    "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(a_		,       a_						)

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

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

				__A             =			args.per_replica_batch_size * strategy.num_replicas_in_sync

				__A             =			prepare_dataset(
				    a_		,       decode_fn=a_		,       mask_fn=a_		,       batch_size=a_		,       shuffle=a_		,       shuffle_buffer_size=args.shuffle_buffer_size		,       )

				__A             =			prepare_dataset(
				    a_		,       decode_fn=a_		,       mask_fn=a_		,       batch_size=a_		,       shuffle=a_		,       )

				__A             =			[]
				if args.hub_model_id:
								callbacks.append(
								    PushToHubCallback(output_dir=args.output_dir		,       hub_model_id=args.hub_model_id		,       tokenizer=a_						)						)

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

				model.save_pretrained(args.output_dir						)


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


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
