





"""simple docstring"""





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__					=   logging.getLogger(__name__)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       argparse.ArgumentParser(
          description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""			)
      parser.add_argument(
          """--dataset_name"""							,						type=SCREAMING_SNAKE_CASE							,						default="""wikitext"""							,						help="""Name of the training. Explore datasets at: hf.co/datasets."""							,						)
      parser.add_argument(
          """--dataset_config"""							,						type=SCREAMING_SNAKE_CASE							,						default="""wikitext-103-raw-v1"""							,						help="""Configuration name of the dataset."""			)
      parser.add_argument(
          """--tokenizer_name_or_path"""							,						type=SCREAMING_SNAKE_CASE							,						default="""sayakpaul/unigram-tokenizer-wikitext"""							,						help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""							,						)
      parser.add_argument(
          """--shard_size"""							,						type=SCREAMING_SNAKE_CASE							,						default=10_00							,						help="""Number of entries to go in a single shard."""							,						)
      parser.add_argument("""--split"""							,						type=SCREAMING_SNAKE_CASE							,						default="""train"""							,						choices=["""train""", """test""", """validation"""]			)
      parser.add_argument(
          """--limit"""							,						default=SCREAMING_SNAKE_CASE							,						type=SCREAMING_SNAKE_CASE							,						help="""Limit the number of shards (used for debugging)."""							,						)
      parser.add_argument(
          """--max_length"""							,						type=SCREAMING_SNAKE_CASE							,						default=5_12							,						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=SCREAMING_SNAKE_CASE							,						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."""							,						)

      lowerCAmelCase  						=       parser.parse_args()
      return args


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      def fn(SCREAMING_SNAKE_CASE     :						Optional[int]			):
            return tokenizer(examples["""text"""]			)

      return fn


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Union[str, Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       []
      for i in range(len(tokenized_data["""input_ids"""]			)			):
            lowerCAmelCase  						=       {
                """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]			)			),
            }
            lowerCAmelCase  						=       tf.train.Features(feature=SCREAMING_SNAKE_CASE			)
            lowerCAmelCase  						=       tf.train.Example(features=SCREAMING_SNAKE_CASE			)
            lowerCAmelCase  						=       example.SerializeToString()
            records.append(SCREAMING_SNAKE_CASE			)
      return records


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




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

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

      lowerCAmelCase  						=       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			)
            lowerCAmelCase  						=       os.path.join(args.output_dir							,						args.split			)
            if not os.path.exists(SCREAMING_SNAKE_CASE			):
                  os.makedirs(SCREAMING_SNAKE_CASE			)
      else:
            lowerCAmelCase  						=       os.path.join(args.output_dir							,						args.split			)

      # Tokenize the whole dataset at once.
      lowerCAmelCase  						=       tokenize_function(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       dataset.map(SCREAMING_SNAKE_CASE							,						batched=SCREAMING_SNAKE_CASE							,						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(SCREAMING_SNAKE_CASE     :						Any			):
            # Concatenate all texts.
            lowerCAmelCase  						=       {k: sum(examples[k]							,						[]			) for k in examples.keys()}
            lowerCAmelCase  						=       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 🫀
            lowerCAmelCase  						=       (total_length // args.max_length) * args.max_length
            # Split by chunks of max_len.
            lowerCAmelCase  						=       {
                k: [t[i : i + args.max_length] for i in range(0							,						SCREAMING_SNAKE_CASE							,						args.max_length			)]
                for k, t in concatenated_examples.items()
            }
            return result

      lowerCAmelCase  						=       dataset_tokenized.map(SCREAMING_SNAKE_CASE							,						batched=SCREAMING_SNAKE_CASE							,						batch_size=10_00							,						num_proc=4			)

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

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

            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=SCREAMING_SNAKE_CASE			)


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













"""simple docstring"""





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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'imagegpt'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(       self      ,						lowercase=512 + 1      ,						lowercase=32 * 32      ,						lowercase=512      ,						lowercase=24      ,						lowercase=8      ,						lowercase=None      ,						lowercase="quick_gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=1e-5      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						**lowercase      ,						)		->     Any:
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       n_positions
            lowerCAmelCase  						=       n_embd
            lowerCAmelCase  						=       n_layer
            lowerCAmelCase  						=       n_head
            lowerCAmelCase  						=       n_inner
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       resid_pdrop
            lowerCAmelCase  						=       embd_pdrop
            lowerCAmelCase  						=       attn_pdrop
            lowerCAmelCase  						=       layer_norm_epsilon
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       scale_attn_weights
            lowerCAmelCase  						=       use_cache
            lowerCAmelCase  						=       scale_attn_by_inverse_layer_idx
            lowerCAmelCase  						=       reorder_and_upcast_attn
            lowerCAmelCase  						=       tie_word_embeddings

            super().__init__(tie_word_embeddings=lowercase      ,						**lowercase )







class 		lowercase					(					_UpperCAmelCase					):





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ] )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = 1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 32      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       self._generate_dummy_images(lowercase      ,						lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       dict(preprocessor(images=lowercase      ,						return_tensors=lowercase ) )

            return inputs






