
"""simple docstring"""



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


a					:	Union[str, Any]							=		logging.getLogger(__name__)


def  _UpperCamelCase	(						)    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   argparse.ArgumentParser(
        description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""      )
    parser.add_argument(
        """--dataset_name"""					,      type=_A					,      default="""wikitext"""					,      help="""Name of the training. Explore datasets at: hf.co/datasets."""					,      )
    parser.add_argument(
        """--dataset_config"""					,      type=_A					,      default="""wikitext-103-raw-v1"""					,      help="""Configuration name of the dataset."""      )
    parser.add_argument(
        """--tokenizer_name_or_path"""					,      type=_A					,      default="""sayakpaul/unigram-tokenizer-wikitext"""					,      help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""					,      )
    parser.add_argument(
        """--shard_size"""					,      type=_A					,      default=1_0_0_0					,      help="""Number of entries to go in a single shard."""					,      )
    parser.add_argument("""--split"""					,      type=_A					,      default="""train"""					,      choices=["""train""", """test""", """validation"""]      )
    parser.add_argument(
        """--limit"""					,      default=_A					,      type=_A					,      help="""Limit the number of shards (used for debugging)."""					,      )
    parser.add_argument(
        """--max_length"""					,      type=_A					,      default=5_1_2					,      help="""Maximum sequence length. For training on TPUs, it helps to have a maximum"""
        """ sequence length that is a multiple of 8."""					,      )
    parser.add_argument(
        """--output_dir"""					,      default="""tf-tpu"""					,      type=_A					,      help="""Output directory where the TFRecord shards will be saved. If the"""
        """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"""
        """ shards will be directly saved to a Google Cloud Storage bucket."""					,      )

    _UpperCAmelCase          =   parser.parse_args()
    return args


def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    def fn(_A      ):
        return tokenizer(examples["""text"""]      )

    return fn


def  _UpperCamelCase	(						_A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   []
    for i in range(len(tokenized_data["""input_ids"""]      )      ):
        _UpperCAmelCase          =   {
            """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i]      )      ),
            """attention_mask""": tf.train.Feature(
                intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i]      )      ),
        }
        _UpperCAmelCase          =   tf.train.Features(feature=_A      )
        _UpperCAmelCase          =   tf.train.Example(features=_A      )
        _UpperCAmelCase          =   example.SerializeToString()
        records.append(_A      )
    return records


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   datasets.load_dataset(args.dataset_name					,      args.dataset_config					,      split=args.split      )

    if args.limit is not None:
        _UpperCAmelCase          =   min(len(_A      )					,      args.limit      )
        _UpperCAmelCase          =   dataset.select(range(_A      )      )
        print(F"""Limiting the dataset to {args.limit} entries."""      )

    _UpperCAmelCase          =   AutoTokenizer.from_pretrained(args.tokenizer_name_or_path      )

    # Handle output directory creation.
    # For serializing into a Google Cloud Storage Bucket, one needs to first
    # create a bucket.
    if "gs" not in args.output_dir:
        if not os.path.exists(args.output_dir      ):
            os.makedirs(args.output_dir      )
        _UpperCAmelCase          =   os.path.join(args.output_dir					,      args.split      )
        if not os.path.exists(_A      ):
            os.makedirs(_A      )
    else:
        _UpperCAmelCase          =   os.path.join(args.output_dir					,      args.split      )

    # Tokenize the whole dataset at once.
    _UpperCAmelCase          =   tokenize_function(_A      )
    _UpperCAmelCase          =   dataset.map(_A					,      batched=_A					,      num_proc=4					,      remove_columns=["""text"""]      )

    # We need to concatenate all our texts together, and then split the result
    # into chunks of a fixed size, which we will call block_size. To do this, we
    # will use the map method again, with the option batched=True. When we use batched=True,
    # the function we pass to map() will be passed multiple inputs at once, allowing us
    # to group them into more or fewer examples than we had in the input.
    # This allows us to create our new fixed-length samples. The advantage of this
    # method is that we don't lose a whole lot of content from the dataset compared to the
    # case where we simply tokenize with a pre-defined max_length.

    def group_texts(_A      ):
        # Concatenate all texts.
        _UpperCAmelCase          =   {k: sum(examples[k]					,      []      ) for k in examples.keys()}
        _UpperCAmelCase          =   len(concatenated_examples[list(examples.keys()      )[0]]      )
        # We drop the small remainder, though you could add padding instead if the model supports it
        # In this, as in all things, we advise you to follow your heart 🫀
        _UpperCAmelCase          =   (total_length // args.max_length) * args.max_length
        # Split by chunks of max_len.
        _UpperCAmelCase          =   {
            k: [t[i : i + args.max_length] for i in range(0					,      _A					,      args.max_length      )]
            for k, t in concatenated_examples.items()
        }
        return result

    _UpperCAmelCase          =   dataset_tokenized.map(_A					,      batched=_A					,      batch_size=1_0_0_0					,      num_proc=4      )

    _UpperCAmelCase          =   0
    _UpperCAmelCase          =   0
    for shard in range(0					,      len(_A      )					,      args.shard_size      ):
        _UpperCAmelCase          =   grouped_dataset[shard : shard + args.shard_size]
        _UpperCAmelCase          =   len(dataset_snapshot["""input_ids"""]      )
        _UpperCAmelCase          =   os.path.join(_A					,      F"""dataset-{shard_count}-{records_containing}.tfrecord"""      )
        _UpperCAmelCase          =   get_serialized_examples(_A      )

        with tf.io.TFRecordWriter(_A      ) as out_file:
            for i in range(len(_A      )      ):
                _UpperCAmelCase          =   serialized_examples[i]
                out_file.write(_A      )
            print("""Wrote file {} containing {} records""".format(_A					,      _A      )      )

        shard_count += 1
        total_records += records_containing

    with open(F"""split-{args.split}-records-count.txt"""					,      """w"""      ) as f:
        print(F"""Total {args.split} records: {total_records}"""					,      file=_A      )


if __name__ == "__main__":
 a					:	str							=		parse_args()
 main(args)

"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







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






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                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
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # 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])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

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

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

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )