


import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
      import numpy as np
      import torch

      from . import (
          AlbertForPreTraining,
          BartForConditionalGeneration,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          CamembertForMaskedLM,
          CTRLLMHeadModel,
          DistilBertForMaskedLM,
          DistilBertForQuestionAnswering,
          DPRContextEncoder,
          DPRQuestionEncoder,
          DPRReader,
          ElectraForPreTraining,
          FlaubertWithLMHeadModel,
          GPTaLMHeadModel,
          LayoutLMForMaskedLM,
          LxmertForPreTraining,
          LxmertVisualFeatureEncoder,
          OpenAIGPTLMHeadModel,
          RobertaForMaskedLM,
          RobertaForSequenceClassification,
          TaForConditionalGeneration,
          TransfoXLLMHeadModel,
          XLMRobertaForMaskedLM,
          XLMWithLMHeadModel,
          XLNetLMHeadModel,
      )


logging.set_verbosity_info()

snake_case__				:	Dict					       =					{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: int						,						A__						: List[Any]						,						A__						: Dict						,						A__						: List[str]=False						,						A__						: Union[str, Any]=True					)     ->    Union[str, Any]:
   if model_type not in MODEL_CLASSES:
      raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()					)}.'''					)

   lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   List[str]		= MODEL_CLASSES[model_type]

   # Initialise TF model
   if config_file in aws_config_map:
      lowerCamelCase_						:   Tuple		= cached_file(A__						,						A__						,						force_download=not use_cached_models					)
   lowerCamelCase_						:   Union[str, Any]		= config_class.from_json_file(A__					)
   lowerCamelCase_						:   int		= True
   lowerCamelCase_						:   List[str]		= True
   print(f'''Building TensorFlow model from configuration: {config}'''					)
   lowerCamelCase_						:   Tuple		= model_class(A__					)

   # Load weights from tf checkpoint
   if pytorch_checkpoint_path in aws_config_map.keys():
      lowerCamelCase_						:   Optional[int]		= cached_file(
          A__						,						A__						,						force_download=not use_cached_models					)
   # Load PyTorch checkpoint in tf2 model:
   lowerCamelCase_						:   List[Any]		= load_pytorch_checkpoint_in_tfa_model(A__						,						A__					)

   if compare_with_pt_model:
      lowerCamelCase_						:   Optional[int]		= tf_model(tf_model.dummy_inputs						,						training=A__					)  # build the network

      lowerCamelCase_						:   List[str]		= torch.load(A__						,						map_location="""cpu"""					)
      lowerCamelCase_						:   Optional[Any]		= pt_model_class.from_pretrained(
          pretrained_model_name_or_path=A__						,						config=A__						,						state_dict=A__					)

      with torch.no_grad():
         lowerCamelCase_						:   Optional[int]		= pt_model(**pt_model.dummy_inputs					)

      lowerCamelCase_						:   Dict		= pto[0].numpy()
      lowerCamelCase_						:   Tuple		= tfo[0].numpy()
      lowerCamelCase_						:   Optional[Any]		= np.amax(np.abs(np_pt - np_tf					)					)
      print(f'''Max absolute difference between models outputs {diff}'''					)
      assert diff <= 2e-2, f'''Error, model absolute difference is >2e-2: {diff}'''

   # Save pytorch-model
   print(f'''Save TensorFlow model to {tf_dump_path}'''					)
   tf_model.save_weights(A__						,						save_format="""h5"""					)


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Union[str, Any]						,						A__						: Optional[Any]=None						,						A__						: str=None						,						A__						: Optional[int]=False						,						A__						: str=False						,						A__						: Union[str, Any]=False						,						A__						: Dict=False						,						)     ->    int:
   if args_model_type is None:
      lowerCamelCase_						:   List[str]		= list(MODEL_CLASSES.keys()					)
   else:
      lowerCamelCase_						:   List[str]		= [args_model_type]

   for j, model_type in enumerate(A__						,						start=1					):
      print("""=""" * 100					)
      print(f''' Converting model type {j}/{len(A__					)}: {model_type}'''					)
      print("""=""" * 100					)
      if model_type not in MODEL_CLASSES:
         raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()					)}.'''					)

      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   int		= MODEL_CLASSES[model_type]

      if model_shortcut_names_or_path is None:
         lowerCamelCase_						:   Optional[int]		= list(aws_model_maps.keys()					)
      if config_shortcut_names_or_path is None:
         lowerCamelCase_						:   Optional[int]		= model_shortcut_names_or_path

      for i, (model_shortcut_name, config_shortcut_name) in enumerate(
          zip(A__						,						A__					)						,						start=1					):
         print("""-""" * 100					)
         if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
            if not only_convert_finetuned_models:
               print(f'''    Skipping finetuned checkpoint {model_shortcut_name}'''					)
               continue
            lowerCamelCase_						:   List[str]		= model_shortcut_name
         elif only_convert_finetuned_models:
            print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''					)
            continue
         print(
             f'''    Converting checkpoint {i}/{len(A__					)}: {model_shortcut_name} - model_type {model_type}'''					)
         print("""-""" * 100					)

         if config_shortcut_name in aws_config_map:
            lowerCamelCase_						:   Union[str, Any]		= cached_file(A__						,						A__						,						force_download=not use_cached_models					)
         else:
            lowerCamelCase_						:   str		= config_shortcut_name

         if model_shortcut_name in aws_model_maps:
            lowerCamelCase_						:   List[Any]		= cached_file(A__						,						A__						,						force_download=not use_cached_models					)
         else:
            lowerCamelCase_						:   int		= model_shortcut_name

         if os.path.isfile(A__					):
            lowerCamelCase_						:   int		= """converted_model"""

         convert_pt_checkpoint_to_tf(
             model_type=A__						,						pytorch_checkpoint_path=A__						,						config_file=A__						,						tf_dump_path=os.path.join(A__						,						model_shortcut_name + """-tf_model.h5"""					)						,						compare_with_pt_model=A__						,						)
         if remove_cached_files:
            os.remove(A__					)
            os.remove(A__					)


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
      )
      parser.add_argument(
          '--model_type',
          default=None,
          type=str,
          help=(
              F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
              'convert all the models from AWS.'
          ),
      )
      parser.add_argument(
          '--pytorch_checkpoint_path',
          default=None,
          type=str,
          help=(
              'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
              'If not given, will download and convert all the checkpoints from AWS.'
          ),
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          help=(
              'The config json file corresponding to the pre-trained model. \n'
              'This specifies the model architecture. If not given and '
              '--pytorch_checkpoint_path is not given or is a shortcut name '
              'use the configuration associated to the shortcut name on the AWS'
          ),
      )
      parser.add_argument(
          '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
      )
      parser.add_argument(
          '--use_cached_models',
          action='store_true',
          help='Use cached models if possible instead of updating to latest checkpoint versions.',
      )
      parser.add_argument(
          '--remove_cached_files',
          action='store_true',
          help='Remove pytorch models after conversion (save memory when converting in batches).',
      )
      parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
      snake_case__				:	Optional[Any]					       =					parser.parse_args()

      # if args.pytorch_checkpoint_path is not None:
      #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
      #                                 args.pytorch_checkpoint_path,
      #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
      #                                 args.tf_dump_path,
      #                                 compare_with_pt_model=args.compare_with_pt_model,
      #                                 use_cached_models=args.use_cached_models)
      # else:
      convert_all_pt_checkpoints_to_tf(
          args.model_type.lower() if args.model_type is not None else None,
          args.tf_dump_path,
          model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
          if args.pytorch_checkpoint_path is not None
          else None,
          config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
          compare_with_pt_model=args.compare_with_pt_model,
          use_cached_models=args.use_cached_models,
          remove_cached_files=args.remove_cached_files,
          only_convert_finetuned_models=args.only_convert_finetuned_models,
      )









import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


snake_case__				:	Dict					       =					logging.getLogger(__name__)


def    __lowerCamelCase   (      )     ->    Any:
   lowerCamelCase_						:   str		= 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=1000						,						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=512						,						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."""						,						)

   lowerCamelCase_						:   Optional[int]		= parser.parse_args()
   return args


def    __lowerCamelCase   (      A__						: Tuple					)     ->    str:
   def fn(A__						: Dict					):
      return tokenizer(examples["""text"""]					)

   return fn


def    __lowerCamelCase   (      A__						: Union[str, Any]					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Dict		= []
   for i in range(len(tokenized_data["""input_ids"""]					)					):
      lowerCamelCase_						:   List[str]		= {
          """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_						:   Optional[int]		= tf.train.Features(feature=A__					)
      lowerCamelCase_						:   Tuple		= tf.train.Example(features=A__					)
      lowerCamelCase_						:   Optional[Any]		= example.SerializeToString()
      records.append(A__					)
   return records


def    __lowerCamelCase   (      A__						: Tuple					)     ->    Union[str, Any]:
   lowerCamelCase_						:   int		= datasets.load_dataset(args.dataset_name						,						args.dataset_config						,						split=args.split					)

   if args.limit is not None:
      lowerCamelCase_						:   str		= min(len(A__					)						,						args.limit					)
      lowerCamelCase_						:   Dict		= dataset.select(range(A__					)					)
      print(f'''Limiting the dataset to {args.limit} entries.'''					)

   lowerCamelCase_						:   str		= 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_						:   Optional[int]		= os.path.join(args.output_dir						,						args.split					)
      if not os.path.exists(A__					):
         os.makedirs(A__					)
   else:
      lowerCamelCase_						:   Any		= os.path.join(args.output_dir						,						args.split					)

   # Tokenize the whole dataset at once.
   lowerCamelCase_						:   List[str]		= tokenize_function(A__					)
   lowerCamelCase_						:   List[Any]		= 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__						: Union[str, Any]					):
      # Concatenate all texts.
      lowerCamelCase_						:   Optional[int]		= {k: sum(examples[k]						,						[]					) for k in examples.keys()}
      lowerCamelCase_						:   Union[str, Any]		= 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_						:   Union[str, Any]		= (total_length // args.max_length) * args.max_length
      # Split by chunks of max_len.
      lowerCamelCase_						:   List[str]		= {
          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

   lowerCamelCase_						:   Optional[int]		= dataset_tokenized.map(A__						,						batched=A__						,						batch_size=1000						,						num_proc=4					)

   lowerCamelCase_						:   List[str]		= 0
   lowerCamelCase_						:   Tuple		= 0
   for shard in range(0						,						len(A__					)						,						args.shard_size					):
      lowerCamelCase_						:   Union[str, Any]		= grouped_dataset[shard : shard + args.shard_size]
      lowerCamelCase_						:   Optional[int]		= len(dataset_snapshot["""input_ids"""]					)
      lowerCamelCase_						:   List[str]		= os.path.join(A__						,						f'''dataset-{shard_count}-{records_containing}.tfrecord'''					)
      lowerCamelCase_						:   Optional[int]		= get_serialized_examples(A__					)

      with tf.io.TFRecordWriter(A__					) as out_file:
         for i in range(len(A__					)					):
            lowerCamelCase_						:   Dict		= 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__":
      snake_case__				:	str					       =					parse_args()
      main(args)





