





'''simple docstring'''







import argparse
import logging
import os
import re

import tensorflow as tf

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


lowerCAmelCase_					:    List[str]      	=			logging.getLogger(__name__)

lowerCAmelCase_					:    List[Any]      	=			tf.data.AUTOTUNE






def  _lowerCamelCase				( )	->	Optional[int]:
				_a								= argparse.ArgumentParser(description="Train a masked language model on TPU."						)
				parser.add_argument(
				    "--pretrained_model_config"    ,							type=lowercase    ,							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=lowercase    ,							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=lowercase    ,							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=lowercase    ,							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=lowercase    ,							help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."    ,							)

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

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

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

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

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

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

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

				_a								= parser.parse_args()
				return args






def  _lowerCamelCase				( lowercase						:      Union[str, Any]						)	->	Optional[int]:
				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(lowercase						)
				tf.tpu.experimental.initialize_tpu_system(lowercase						)

				return tpu






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

				return num_samples






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      Tuple    ,							lowercase						:      List[str]    ,							lowercase						:      Any    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]=None						)	->	int:
				_a								= count_samples(lowercase						)
				_a								= tf.data.Dataset.from_tensor_slices(lowercase						)
				if shuffle:
								_a								= dataset.shuffle(len(lowercase						)						)
				_a								= tf.data.TFRecordDataset(lowercase    ,							num_parallel_reads=lowercase						)
				# 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(lowercase						)						)
				_a								= dataset.map(lowercase    ,							num_parallel_calls=lowercase						)
				if shuffle:
								assert shuffle_buffer_size is not None
								_a								= dataset.shuffle(args.shuffle_buffer_size						)
				_a								= dataset.batch(lowercase    ,							drop_remainder=lowercase						)
				_a								= dataset.map(lowercase    ,							num_parallel_calls=lowercase						)
				_a								= dataset.prefetch(lowercase						)
				return dataset






def  _lowerCamelCase				( lowercase						:      Union[str, Any]						)	->	Dict:
				if not args.no_tpu:
								_a								= initialize_tpu(lowercase						)
								_a								= tf.distribute.TPUStrategy(lowercase						)
				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(lowercase						)

				_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(lowercase						)
								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=lowercase    ,							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=lowercase    ,							metrics=["accuracy"]						)

				def decode_fn(lowercase						:      int						):
								_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(lowercase    ,							lowercase						)

				# 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=lowercase    ,							mlm_probability=args.mlm_probability    ,							mlm=lowercase    ,							return_tensors="tf"						)

				def mask_with_collator(lowercase						:      List[Any]						):
								# 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(lowercase						)    ,							mask_token_id=tokenizer.mask_token_id    ,							special_tokens_mask=lowercase    ,							)
								return batch

				_a								= args.per_replica_batch_size * strategy.num_replicas_in_sync

				_a								= prepare_dataset(
				    lowercase    ,							decode_fn=lowercase    ,							mask_fn=lowercase    ,							batch_size=lowercase    ,							shuffle=lowercase    ,							shuffle_buffer_size=args.shuffle_buffer_size    ,							)

				_a								= prepare_dataset(
				    lowercase    ,							decode_fn=lowercase    ,							mask_fn=lowercase    ,							batch_size=lowercase    ,							shuffle=lowercase    ,							)

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

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

				model.save_pretrained(args.output_dir						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Any      	=			parse_args()
					main(args)



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None







class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase ):
 _UpperCamelCase			  =  2
 @register_to_config
 def __init__(							self      ,							A_ = 0.02      ,							A_ = 100      ,							A_ = 1.007      ,							A_ = 80      ,							A_ = 0.05      ,							A_ = 50      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sigma_max

       # setable values
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						np.IntTensor					=					None
       __lowerCAmelCase					:						torch.FloatTensor					=					None  # sigma(t_i)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->torch.FloatTensor:



       '''simple docstring'''





       return sample
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					num_inference_steps
       __lowerCAmelCase					:						Dict					=					np.arange(0      ,							self.num_inference_steps					)[::-1].copy()
       __lowerCAmelCase					:						Optional[Any]					=					torch.from_numpy(A_					).to(A_					)
       __lowerCAmelCase					:						Tuple					=					[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       __lowerCAmelCase					:						Optional[int]					=					torch.tensor(A_      ,							dtype=torch.floataa      ,							device=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None					)					->Tuple[torch.FloatTensor, float]:



       '''simple docstring'''





       if self.config.s_min <= sigma <= self.config.s_max:
             __lowerCAmelCase					:						List[str]					=					min(self.config.s_churn / self.num_inference_steps      ,							2**0.5 - 1					)
       else:
             __lowerCAmelCase					:						List[str]					=					0

       # sample eps ~ N(0, S_noise^2 * I)
       __lowerCAmelCase					:						int					=					self.config.s_noise * randn_tensor(sample.shape      ,							generator=A_					).to(sample.device					)
       __lowerCAmelCase					:						str					=					sigma + gamma * sigma
       __lowerCAmelCase					:						Any					=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					sample_hat + sigma_hat * model_output
       __lowerCAmelCase					:						int					=					(sample_hat - pred_original_sample) / sigma_hat
       __lowerCAmelCase					:						Tuple					=					sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					sample_prev + sigma_prev * model_output
       __lowerCAmelCase					:						List[Any]					=					(sample_prev - pred_original_sample) / sigma_prev
       __lowerCAmelCase					:						Any					=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       raise NotImplementedError()


