




"""simple docstring"""



import argparse
import logging
import os
import re

import tensorflow as tf

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


SCREAMING_SNAKE_CASE    =	logging.getLogger(__name__)

SCREAMING_SNAKE_CASE    =	tf.data.AUTOTUNE







def   _SCREAMING_SNAKE_CASE       (							)						->		str:
 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=5_12      ,			help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"      ,			)

 parser.add_argument(
     "--mlm_probability"      ,			type=lowercase_      ,			default=0.1_5      ,			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   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 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   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_=None						)						->		List[str]:
 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   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Any:
 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_						):
  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_						):
  # 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__":
      SCREAMING_SNAKE_CASE    =	parse_args()
      main(args)











"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class      UpperCAmelCase_  (						nn.Module							):




    def __init__(   self						:	Optional[int]     ,			snake_case_						:	int = 16     ,			snake_case_						:	int = 88     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	int = 1     ,			snake_case_						:	float = 0.0     ,			snake_case_						:	int = 32     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	bool = False     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	str = "geglu"     ,			snake_case_						:	Optional[int] = None     ,			)				->							str:




     '''simple docstring'''

     super().__init__()
     A__	=    nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=snake_case_     ,			attention_head_dim=snake_case_     ,			in_channels=snake_case_     ,			num_layers=snake_case_     ,			dropout=snake_case_     ,			norm_num_groups=snake_case_     ,			cross_attention_dim=snake_case_     ,			attention_bias=snake_case_     ,			sample_size=snake_case_     ,			num_vector_embeds=snake_case_     ,			activation_fn=snake_case_     ,			num_embeds_ada_norm=snake_case_     ,			)
             for _ in range(2				)
         ]				)

     # Variables that can be set by a pipeline:

     # The ratio of transformer1 to transformer2's output states to be combined during inference
     A__	=    0.5

     # The shape of `encoder_hidden_states` is expected to be
     # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
     A__	=    [77, 257]

     # Which transformer to use to encode which condition.
     # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
     A__	=    [1, 0]




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	bool = True     ,			)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    hidden_states

     A__	=    []
     A__	=    0
     # attention_mask is not used yet
     for i in range(2				):
      # for each of the two transformers, pass the corresponding condition tokens
      A__	=    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      A__	=    self.transformer_index_for_condition[i]
      A__	=    self.transformers[transformer_index](
          snake_case_     ,			encoder_hidden_states=snake_case_     ,			timestep=snake_case_     ,			cross_attention_kwargs=snake_case_     ,			return_dict=snake_case_     ,			)[0]
      encoded_states.append(encoded_state - input_states				)
      tokens_start += self.condition_lengths[i]

     A__	=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
     A__	=    output_states + input_states

     if not return_dict:
      return (output_states,)

     return TransformeraDModelOutput(sample=snake_case_				)





