



import argparse
import logging
import os
import re

import tensorflow as tf

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


_snake_case		=  logging.getLogger(__name__)

_snake_case		=  tf.data.AUTOTUNE






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    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=512				, help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"				, )

							parser.add_argument(
							    "--mlm_probability"				, type=_lowerCamelCase				, default=0.15				, 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."  )

							_lowerCAmelCase     :							List[Any]    =    parser.parse_args()
							return args






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							try:
														if args.tpu_name:
																					_lowerCAmelCase     :							Union[str, Any]    =    tf.distribute.cluster_resolver.TPUClusterResolver(
																					    args.tpu_name				, zone=args.tpu_zone				, project=args.gcp_project  )
														else:
																					_lowerCAmelCase     :							Dict    =    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 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

							return num_samples






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=None  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    count_samples(_lowerCamelCase  )
							_lowerCAmelCase     :							Tuple    =    tf.data.Dataset.from_tensor_slices(_lowerCamelCase  )
							if shuffle:
														_lowerCAmelCase     :							Union[str, Any]    =    dataset.shuffle(len(_lowerCamelCase  )  )
							_lowerCAmelCase     :							List[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
							_lowerCAmelCase     :							Optional[Any]    =    dataset.apply(tf.data.experimental.assert_cardinality(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Tuple    =    dataset.map(_lowerCamelCase				, num_parallel_calls=_lowerCamelCase  )
							if shuffle:
														assert shuffle_buffer_size is not None
														_lowerCAmelCase     :							Any    =    dataset.shuffle(args.shuffle_buffer_size  )
							_lowerCAmelCase     :							Optional[Any]    =    dataset.batch(_lowerCamelCase				, drop_remainder=_lowerCamelCase  )
							_lowerCAmelCase     :							Optional[int]    =    dataset.map(_lowerCamelCase				, num_parallel_calls=_lowerCamelCase  )
							_lowerCAmelCase     :							List[str]    =    dataset.prefetch(_lowerCamelCase  )
							return dataset






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

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

							_lowerCAmelCase     :							str    =    AutoTokenizer.from_pretrained(args.tokenizer  )
							_lowerCAmelCase     :							Optional[int]    =    AutoConfig.from_pretrained(args.pretrained_model_config  )
							_lowerCAmelCase     :							Dict    =    tokenizer.vocab_size

							_lowerCAmelCase     :							Union[str, 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}."  )
							_lowerCAmelCase     :							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}."  )

							_lowerCAmelCase     :							Tuple    =    count_samples(_lowerCamelCase  )

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

							with strategy.scope():
														_lowerCAmelCase     :							str    =    TFAutoModelForMaskedLM.from_config(_lowerCamelCase  )
														model(model.dummy_inputs  )  # Pass some dummy inputs through the model to ensure all the weights are built
														_lowerCAmelCase   ,    _lowerCAmelCase     :							str    =    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  ):
														_lowerCAmelCase     :							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(_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.
							_lowerCAmelCase     :							str    =    DataCollatorForLanguageModeling(
							    tokenizer=_lowerCamelCase				, mlm_probability=args.mlm_probability				, mlm=_lowerCamelCase				, return_tensors="tf"  )

							def mask_with_collator(_lowerCamelCase  ):
														# TF really needs an isin() function
														_lowerCAmelCase     :							Optional[int]    =    (
														    ~tf.cast(batch["attention_mask"]				, tf.bool  )
														    | (batch["input_ids"] == tokenizer.cls_token_id)
														    | (batch["input_ids"] == tokenizer.sep_token_id)
														)
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    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

							_lowerCAmelCase     :							Union[str, Any]    =    args.per_replica_batch_size * strategy.num_replicas_in_sync

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

							_lowerCAmelCase     :							Optional[Any]    =    prepare_dataset(
							    _lowerCamelCase				, decode_fn=_lowerCamelCase				, mask_fn=_lowerCamelCase				, batch_size=_lowerCamelCase				, shuffle=_lowerCamelCase				, )

							_lowerCAmelCase     :							Dict    =    []
							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__":
						_snake_case		=  parse_args()
						main(args)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       Optional[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





