




'''simple docstring'''


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_snake_case : Tuple  				=						logging.get_logger(__name__)

_snake_case : List[Any]  				=						{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class      A       (						_a       ):
							lowercase_							     =							'gptj'
							lowercase_							     =							{
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    Tuple=5_04_00				,     lowerCAmelCase_			:    int=20_48				,     lowerCAmelCase_			:    Optional[int]=40_96				,     lowerCAmelCase_			:    Tuple=28				,     lowerCAmelCase_			:    List[str]=16				,     lowerCAmelCase_			:    Any=64				,     lowerCAmelCase_			:    Union[str, Any]=None				,     lowerCAmelCase_			:    Any="gelu_new"				,     lowerCAmelCase_			:    List[str]=0.0				,     lowerCAmelCase_			:    Optional[int]=0.0				,     lowerCAmelCase_			:    Union[str, Any]=0.0				,     lowerCAmelCase_			:    int=1e-5				,     lowerCAmelCase_			:    Union[str, Any]=0.0_2				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    Tuple=5_02_56				,     lowerCAmelCase_			:    Union[str, Any]=5_02_56				,     lowerCAmelCase_			:    List[str]=False				,     **lowerCAmelCase_			:    Dict				,     )     ->				Dict:


														"""simple docstring"""


														_a      	=							vocab_size
														_a      	=							n_positions
														_a      	=							n_embd
														_a      	=							n_layer
														_a      	=							n_head
														_a      	=							n_inner
														_a      	=							rotary_dim
														_a      	=							activation_function
														_a      	=							resid_pdrop
														_a      	=							embd_pdrop
														_a      	=							attn_pdrop
														_a      	=							layer_norm_epsilon
														_a      	=							initializer_range
														_a      	=							use_cache

														_a      	=							bos_token_id
														_a      	=							eos_token_id

														super().__init__(
														    bos_token_id=lowerCAmelCase_				,     eos_token_id=lowerCAmelCase_				,     tie_word_embeddings=lowerCAmelCase_				,     **lowerCAmelCase_   )



class      A       (						_a       ):







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    PretrainedConfig				,     lowerCAmelCase_			:    str = "default"				,     lowerCAmelCase_			:    List[PatchingSpec] = None				,     lowerCAmelCase_			:    bool = False				,     )     ->				Optional[int]:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_				,     task=lowerCAmelCase_				,     patching_specs=lowerCAmelCase_				,     use_past=lowerCAmelCase_   )
														if not getattr(self._config				,     '''pad_token_id'''				,     lowerCAmelCase_   ):
																					# TODO: how to do that better?
																					_a      	=							0







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														_a      	=							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
														if self.use_past:
																					self.fill_with_past_key_values_(lowerCAmelCase_				,     direction='''inputs'''   )
																					_a      	=							{0: '''batch''', 1: '''past_sequence + sequence'''}
														else:
																					_a      	=							{0: '''batch''', 1: '''sequence'''}

														return common_inputs







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				int:


														"""simple docstring"""


														return self._config.n_layer







							@property
							def   __lowerCAmelCase     (    self			:    int   )     ->				int:


														"""simple docstring"""


														return self._config.n_head







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    PreTrainedTokenizer				,     lowerCAmelCase_			:    int = -1				,     lowerCAmelCase_			:    int = -1				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    Optional[TensorType] = None				,     )     ->				Mapping[str, Any]:


														"""simple docstring"""


														_a      	=							super(lowerCAmelCase_				,     self   ).generate_dummy_inputs(
														    lowerCAmelCase_				,     batch_size=lowerCAmelCase_				,     seq_length=lowerCAmelCase_				,     is_pair=lowerCAmelCase_				,     framework=lowerCAmelCase_   )

														# We need to order the input in the way they appears in the forward()
														_a      	=							OrderedDict({'''input_ids''': common_inputs['''input_ids''']}   )

														# Need to add the past_keys
														if self.use_past:
																					if not is_torch_available():
																												raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
																					else:
																												import torch

																												_a	,				_a      	=							common_inputs['''input_ids'''].shape
																												# Not using the same length for past_key_values
																												_a      	=							seqlen + 2
																												_a      	=							(
																												    batch,
																												    self.num_attention_heads,
																												    past_key_values_length,
																												    self._config.hidden_size // self.num_attention_heads,
																												)
																												_a      	=							[
																												    (torch.zeros(lowerCAmelCase_   ), torch.zeros(lowerCAmelCase_   )) for _ in range(self.num_layers   )
																												]

														_a      	=							common_inputs['''attention_mask''']
														if self.use_past:
																					_a      	=							ordered_inputs['''attention_mask'''].dtype
																					_a      	=							torch.cat(
																					    [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase_				,     lowerCAmelCase_				,     dtype=lowerCAmelCase_   )]				,     dim=1   )

														return ordered_inputs







							@property
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				int:


														"""simple docstring"""


														return 13



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		(				):


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

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

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

				parser.add_argument(
				    '''--learning_rate'''		,  type=snake_case__		,  default=1e-4		,  help='''Learning rate to use for training.'''		,  )

				parser.add_argument(
				    '''--weight_decay_rate'''		,  type=snake_case__		,  default=1e-3		,  help='''Weight decay rate to use for training.'''		,  )

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

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

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

				lowercase_	    =   parser.parse_args()
				return args




def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				try:
								if args.tpu_name:
												lowercase_	    =   tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name		,  zone=args.tpu_zone		,  project=args.gcp_project   )
								else:
												lowercase_	    =   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(snake_case__   )
				tf.tpu.experimental.initialize_tpu_system(snake_case__   )

				return tpu




def 							a		(				snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   0
				for file in file_list:
								lowercase_	    =   file.split('''/'''   )[-1]
								lowercase_	    =   re.search(r'''-\d+-(\d+)\.tfrecord'''		,  snake_case__   ).group(1   )
								lowercase_	    =   int(snake_case__   )
								num_samples += sample_count

				return num_samples




def 							a		(				snake_case__:   Dict		,  snake_case__:   Dict		,  snake_case__:   Union[str, Any]		,  snake_case__:   Optional[int]		,  snake_case__:   Optional[int]		,  snake_case__:   Dict=None   ):


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




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				if not args.no_tpu:
								lowercase_	    =   initialize_tpu(snake_case__   )
								lowercase_	    =   tf.distribute.TPUStrategy(snake_case__   )
				else:
								lowercase_	    =   tf.distribute.OneDeviceStrategy(device='''/gpu:0'''   )

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

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

				lowercase_	    =   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_	    =   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_	    =   count_samples(snake_case__   )

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

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

				def decode_fn(snake_case__:   Dict   ):
								lowercase_	    =   {
								    '''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(snake_case__		,  snake_case__   )

				# 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_	    =   DataCollatorForLanguageModeling(
				    tokenizer=snake_case__		,  mlm_probability=args.mlm_probability		,  mlm=snake_case__		,  return_tensors='''tf'''   )

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

				lowercase_	    =   args.per_replica_batch_size * strategy.num_replicas_in_sync

				lowercase_	    =   prepare_dataset(
				    snake_case__		,  decode_fn=snake_case__		,  mask_fn=snake_case__		,  batch_size=snake_case__		,  shuffle=snake_case__		,  shuffle_buffer_size=args.shuffle_buffer_size		,  )

				lowercase_	    =   prepare_dataset(
				    snake_case__		,  decode_fn=snake_case__		,  mask_fn=snake_case__		,  batch_size=snake_case__		,  shuffle=snake_case__		,  )

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

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

				model.save_pretrained(args.output_dir   )


if __name__ == "__main__":
						__a         =   parse_args()
						main(args)




