"""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											=  logging.getLogger(__name__)

__lowerCamelCase											=  tf.data.AUTOTUNE






def   UpperCAmelCase					(	):






							"""simple docstring"""





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

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

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

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

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

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

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

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

							A__	     =							parser.parse_args()
							return args






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""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(UpperCAmelCase__				)
							tf.tpu.experimental.initialize_tpu_system(UpperCAmelCase__				)

							return tpu






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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

							return num_samples






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__=None				):






							"""simple docstring"""





							A__	     =							count_samples(UpperCAmelCase__				)
							A__	     =							tf.data.Dataset.from_tensor_slices(UpperCAmelCase__				)
							if shuffle:
														A__	     =							dataset.shuffle(len(UpperCAmelCase__				)				)
							A__	     =							tf.data.TFRecordDataset(UpperCAmelCase__       ,    num_parallel_reads=UpperCAmelCase__				)
							# 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(UpperCAmelCase__				)				)
							A__	     =							dataset.map(UpperCAmelCase__       ,    num_parallel_calls=UpperCAmelCase__				)
							if shuffle:
														assert shuffle_buffer_size is not None
														A__	     =							dataset.shuffle(args.shuffle_buffer_size				)
							A__	     =							dataset.batch(UpperCAmelCase__       ,    drop_remainder=UpperCAmelCase__				)
							A__	     =							dataset.map(UpperCAmelCase__       ,    num_parallel_calls=UpperCAmelCase__				)
							A__	     =							dataset.prefetch(UpperCAmelCase__				)
							return dataset






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if not args.no_tpu:
														A__	     =							initialize_tpu(UpperCAmelCase__				)
														A__	     =							tf.distribute.TPUStrategy(UpperCAmelCase__				)
							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(UpperCAmelCase__				)

							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(UpperCAmelCase__				)
														model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
														A__	     =							create_optimizer(
														    num_train_steps=UpperCAmelCase__       ,    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=UpperCAmelCase__       ,    metrics=['accuracy']				)

							def decode_fn(UpperCamelCase__				):
														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(UpperCAmelCase__       ,    UpperCAmelCase__				)

							# 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=UpperCAmelCase__       ,    mlm_probability=args.mlm_probability       ,    mlm=UpperCAmelCase__       ,    return_tensors='tf'				)

							def mask_with_collator(UpperCamelCase__				):
														# 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__	     =							data_collator.tf_mask_tokens(
														    batch['input_ids']       ,    vocab_size=len(UpperCAmelCase__				)       ,    mask_token_id=tokenizer.mask_token_id       ,    special_tokens_mask=UpperCAmelCase__       ,    )
														return batch

							A__	     =							args.per_replica_batch_size * strategy.num_replicas_in_sync

							A__	     =							prepare_dataset(
							    UpperCAmelCase__       ,    decode_fn=UpperCAmelCase__       ,    mask_fn=UpperCAmelCase__       ,    batch_size=UpperCAmelCase__       ,    shuffle=UpperCAmelCase__       ,    shuffle_buffer_size=args.shuffle_buffer_size       ,    )

							A__	     =							prepare_dataset(
							    UpperCAmelCase__       ,    decode_fn=UpperCAmelCase__       ,    mask_fn=UpperCAmelCase__       ,    batch_size=UpperCAmelCase__       ,    shuffle=UpperCAmelCase__       ,    )

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

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

							model.save_pretrained(args.output_dir				)


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






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





