import argparse
import logging
import os
import re

import tensorflow as tf

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


__a  :     int       =  logging.getLogger(__name__)

__a  :     str       =  tf.data.AUTOTUNE





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							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.'''							)

				__lowercase 			=							parser.parse_args()
				return args





def 				UpperCAmelCase						(    lowercase							):







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

				return tpu





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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

				return num_samples





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







				"""simple docstring"""



				__lowercase 			=							count_samples(lowercase							)
				__lowercase 			=							tf.data.Dataset.from_tensor_slices(lowercase							)
				if shuffle:
								__lowercase 			=							dataset.shuffle(len(lowercase							)							)
				__lowercase 			=							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
				__lowercase 			=							dataset.apply(tf.data.experimental.assert_cardinality(lowercase							)							)
				__lowercase 			=							dataset.map(lowercase							,			num_parallel_calls=lowercase							)
				if shuffle:
								assert shuffle_buffer_size is not None
								__lowercase 			=							dataset.shuffle(args.shuffle_buffer_size							)
				__lowercase 			=							dataset.batch(lowercase							,			drop_remainder=lowercase							)
				__lowercase 			=							dataset.map(lowercase							,			num_parallel_calls=lowercase							)
				__lowercase 			=							dataset.prefetch(lowercase							)
				return dataset





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				if not args.no_tpu:
								__lowercase 			=							initialize_tpu(lowercase							)
								__lowercase 			=							tf.distribute.TPUStrategy(lowercase							)
				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(lowercase							)

				__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(lowercase							)
								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=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							):
								__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(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.
				__lowercase 			=							DataCollatorForLanguageModeling(
				    tokenizer=lowercase							,			mlm_probability=args.mlm_probability							,			mlm=lowercase							,			return_tensors='''tf'''							)

				def mask_with_collator(lowercase							):
								# 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(lowercase							)							,			mask_token_id=tokenizer.mask_token_id							,			special_tokens_mask=lowercase							,			)
								return batch

				__lowercase 			=							args.per_replica_batch_size * strategy.num_replicas_in_sync

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

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

				__lowercase 			=							[]
				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__":
		__a  :     int       =  parse_args()
		main(args)
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a  :     str       =  logging.get_logger(__name__)

__a  :     int       =  {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						'''wavlm'''





			def __init__(   self      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-5      ,   lowerCAmelCase__="group"      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,   lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)      ,   lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=1_28      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=8_00      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=0.05      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=1_00      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__="mean"      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 15_00)      ,   lowerCAmelCase__=(5, 3, 3, 1, 1)      ,   lowerCAmelCase__=(1, 2, 3, 1, 1)      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=80      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Tuple:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__      ,   pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__						)
							__lowercase 			=							hidden_size
							__lowercase 			=							feat_extract_norm
							__lowercase 			=							feat_extract_activation
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							conv_bias
							__lowercase 			=							num_buckets
							__lowercase 			=							max_bucket_distance
							__lowercase 			=							num_conv_pos_embeddings
							__lowercase 			=							num_conv_pos_embedding_groups
							__lowercase 			=							len(self.conv_dim						)
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							feat_proj_dropout
							__lowercase 			=							final_dropout
							__lowercase 			=							layerdrop
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							initializer_range
							__lowercase 			=							num_ctc_classes
							__lowercase 			=							vocab_size
							__lowercase 			=							do_stable_layer_norm
							__lowercase 			=							use_weighted_layer_sum
							__lowercase 			=							classifier_proj_size

							if (
							    (len(self.conv_stride						) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
							    or (len(self.conv_dim						) != self.num_feat_extract_layers)
							):
											raise ValueError(
											    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
											    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
											    F" {len(self.conv_dim						)}`, `len(config.conv_stride) = {len(self.conv_stride						)}`,"
											    F" `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							__lowercase 			=							apply_spec_augment
							__lowercase 			=							mask_time_prob
							__lowercase 			=							mask_time_length
							__lowercase 			=							mask_time_min_masks
							__lowercase 			=							mask_feature_prob
							__lowercase 			=							mask_feature_length

							# parameters for pretraining with codevector quantized representations
							__lowercase 			=							num_codevectors_per_group
							__lowercase 			=							num_codevector_groups
							__lowercase 			=							contrastive_logits_temperature
							__lowercase 			=							num_negatives
							__lowercase 			=							codevector_dim
							__lowercase 			=							proj_codevector_dim
							__lowercase 			=							diversity_loss_weight

							# ctc loss
							__lowercase 			=							ctc_loss_reduction
							__lowercase 			=							ctc_zero_infinity

							# adapter
							__lowercase 			=							add_adapter
							__lowercase 			=							adapter_kernel_size
							__lowercase 			=							adapter_stride
							__lowercase 			=							num_adapter_layers
							__lowercase 			=							output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							__lowercase 			=							classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							xvector_output_dim





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return functools.reduce(operator.mul      ,   self.conv_stride      ,   1						)