





"""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	     =					logging.getLogger(__name__)

A	     =					tf.data.AUTOTUNE

def       __A (     )						->						Union[str, Any]:
				__a	:  Optional[Any]         =					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**18				,    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_12				,    help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''				,    )

				parser.add_argument(
				    '''--mlm_probability'''				,    type=a_				,    default=0.1_5				,    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	:  str         =					parser.parse_args()
				return args

def       __A (     a_  :str)						->						Any:
				try:
								if args.tpu_name:
												__a	:  Dict         =					tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name				,    zone=args.tpu_zone				,    project=args.gcp_project)
								else:
												__a	:  Any         =					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       __A (     a_  :Optional[Any])						->						Optional[int]:
				__a	:  Union[str, Any]         =					0
				for file in file_list:
								__a	:  str         =					file.split('''/''')[-1]
								__a	:  List[Any]         =					re.search(R'''-\d+-(\d+)\.tfrecord'''				,    a_).group(1)
								__a	:  Dict         =					int(a_)
								num_samples += sample_count

				return num_samples

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

def       __A (     a_  :List[str])						->						Optional[int]:
				if not args.no_tpu:
								__a	:  Any         =					initialize_tpu(a_)
								__a	:  Any         =					tf.distribute.TPUStrategy(a_)
				else:
								__a	:  str         =					tf.distribute.OneDeviceStrategy(device='''/gpu:0''')

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

				__a	:  List[Any]         =					AutoTokenizer.from_pretrained(args.tokenizer)
				__a	:  List[Any]         =					AutoConfig.from_pretrained(args.pretrained_model_config)
				__a	:  int         =					tokenizer.vocab_size

				__a	:  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}.""")
				__a	:  List[str]         =					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	:  int         =					count_samples(a_)

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

				with strategy.scope():
								__a	:  Tuple         =					TFAutoModelForMaskedLM.from_config(a_)
								model(model.dummy_inputs)  # Pass some dummy inputs through the model to ensure all the weights are built
								__a	:  int         =					create_optimizer(
								    num_train_steps=a_				,    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=a_				,    metrics=['''accuracy'''])

				def decode_fn(a_  :Optional[int]):
								__a	:  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(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	:  List[Any]         =					DataCollatorForLanguageModeling(
				    tokenizer=a_				,    mlm_probability=args.mlm_probability				,    mlm=a_				,    return_tensors='''tf''')

				def mask_with_collator(a_  :Tuple):
								# TF really needs an isin() function
								__a	:  str         =					(
								    ~tf.cast(batch['''attention_mask''']				,    tf.bool)
								    | (batch["input_ids"] == tokenizer.cls_token_id)
								    | (batch["input_ids"] == tokenizer.sep_token_id)
								)
								__a	:  str         =					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	:  List[Any]         =					args.per_replica_batch_size * strategy.num_replicas_in_sync

				__a	:  Tuple         =					prepare_dataset(
				    a_				,    decode_fn=a_				,    mask_fn=a_				,    batch_size=a_				,    shuffle=a_				,    shuffle_buffer_size=args.shuffle_buffer_size				,    )

				__a	:  Union[str, Any]         =					prepare_dataset(
				    a_				,    decode_fn=a_				,    mask_fn=a_				,    batch_size=a_				,    shuffle=a_				,    )

				__a	:  Any         =					[]
				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__":
		A	     =					parse_args()
		main(args)






"""simple docstring"""






import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def       __A (     a_  :List[Any]=None				,    a_  :Tuple=None)						->						List[Any]:
				return field(default_factory=lambda: default				,    metadata=a_)




@dataclass
class 			__lowercase     :







					'''simple docstring'''




					__lowerCAmelCase					=					field(
					    metadata={'''help''': '''The csv file to plot.'''}				, )
					__lowerCAmelCase					=					field(
					    default=_UpperCamelCase				, metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}				, )
					__lowerCAmelCase					=					field(
					    default=_UpperCamelCase				, metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}				, )
					__lowerCAmelCase					=					field(
					    default=_UpperCamelCase				, metadata={'''help''': '''Disable logarithmic scale when plotting'''}				, )
					__lowerCAmelCase					=					field(
					    default=_UpperCamelCase				, metadata={
					        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
					    }				, )
					__lowerCAmelCase					=					field(
					    default=_UpperCamelCase				, metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}				, )
					__lowerCAmelCase					=					list_field(
					    default=_UpperCamelCase				, metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}							)






def       __A (     a_  :Optional[Any])						->						Any:
				try:
								int(a_)
								return True
				except ValueError:
								return False

def       __A (     a_  :List[Any])						->						Any:
				try:
								float(a_)
								return True
				except ValueError:
								return False




class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Dict         =					args
									__a	:  Tuple         =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

									with open(self.args.csv_file		,     newline=''''''       ) as csv_file:
													__a	:  int         =					csv.DictReader(_UpperCAmelCase       )
													for row in reader:
																	__a	:  Union[str, Any]         =					row['''model''']
																	self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']       )       )
																	self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']       )       )
																	if can_convert_to_int(row['''result''']       ):
																					# value is not None
																					__a	:  Optional[int]         =					int(row['''result''']       )
																	elif can_convert_to_float(row['''result''']       ):
																					# value is not None
																					__a	:  Optional[Any]         =					float(row['''result''']       )







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  Optional[int]         =					plt.subplots()
									__a	:  str         =					'''Time usage''' if self.args.is_time else '''Memory usage'''
									__a	:  str         =					title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

									if not self.args.no_log_scale:
													# set logarithm scales
													ax.set_xscale('''log'''       )
													ax.set_yscale('''log'''       )

									for axis in [ax.xaxis, ax.yaxis]:
													axis.set_major_formatter(ScalarFormatter()       )

									for model_name_idx, model_name in enumerate(self.result_dict.keys()       ):
													__a	:  str         =					sorted(set(self.result_dict[model_name]['''bsz''']       )       )
													__a	:  Dict         =					sorted(set(self.result_dict[model_name]['''seq_len''']       )       )
													__a	:  Dict         =					self.result_dict[model_name]['''result''']

													((__a)    ,  (__a))	:  List[Any]         =					(
													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
													)

													__a	:  Any         =					(
													    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
													)

													for inner_loop_value in inner_loop_array:
																	if self.args.plot_along_batch:
																					__a	:  Optional[int]         =					np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,     dtype=_UpperCAmelCase		,     )
																	else:
																					__a	:  Dict         =					np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,     dtype=np.floataa		,     )

																	((__a)    ,  (__a))	:  Union[str, Any]         =					(
																	    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																	)

																	__a	:  Any         =					np.asarray(_UpperCAmelCase		,     _UpperCAmelCase       )[: len(_UpperCAmelCase       )]
																	plt.scatter(
																	    _UpperCAmelCase		,     _UpperCAmelCase		,     label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""       )
																	plt.plot(_UpperCAmelCase		,     _UpperCAmelCase		,     '''--'''       )

													title_str += f""" {label_model_name} vs."""

									__a	:  Optional[Any]         =					title_str[:-4]
									__a	:  Optional[int]         =					'''Time in s''' if self.args.is_time else '''Memory in MB'''

									# plot
									plt.title(_UpperCAmelCase       )
									plt.xlabel(_UpperCAmelCase       )
									plt.ylabel(_UpperCAmelCase       )
									plt.legend()

									if self.args.figure_png_file is not None:
													plt.savefig(self.args.figure_png_file       )
									else:
													plt.show()






def       __A (     )						->						List[str]:
				__a	:  List[str]         =					HfArgumentParser(a_)
				__a	:  Optional[int]         =					parser.parse_args_into_dataclasses()[0]
				__a	:  Tuple         =					Plot(args=a_)
				plot.plot()


if __name__ == "__main__":
		main()