



"""simple docstring"""







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Any=1						)						->   List[Any]:

  """simple docstring"""




  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('.'						)[n_shave_prefix_segments:]						)
  else:
    return ".".join(path.split('.'						)[:n_shave_prefix_segments]						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				List[str]=0						)						->   List[str]:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= []
  for old_item in old_list:
    lowerCAmelCase_   :				Dict								= old_item.replace('in_layers.0'    ,   'norm1'						)
    lowerCAmelCase_   :				List[Any]								= new_item.replace('in_layers.2'    ,   'conv1'						)

    lowerCAmelCase_   :				Tuple								= new_item.replace('out_layers.0'    ,   'norm2'						)
    lowerCAmelCase_   :				str								= new_item.replace('out_layers.3'    ,   'conv2'						)

    lowerCAmelCase_   :				Tuple								= new_item.replace('emb_layers.1'    ,   'time_emb_proj'						)
    lowerCAmelCase_   :				Any								= new_item.replace('skip_connection'    ,   'conv_shortcut'						)

    lowerCAmelCase_   :				Tuple								= shave_segments(lowerCAmelCase__    ,   n_shave_prefix_segments=lowerCAmelCase__						)

    mapping.append({'old': old_item, 'new': new_item}						)

  return mapping

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Dict=0						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Union[str, Any]								= []
  for old_item in old_list:
    lowerCAmelCase_   :				List[str]								= old_item

    lowerCAmelCase_   :				int								= new_item.replace('norm.weight'    ,   'group_norm.weight'						)
    lowerCAmelCase_   :				Optional[Any]								= new_item.replace('norm.bias'    ,   'group_norm.bias'						)

    lowerCAmelCase_   :				Tuple								= new_item.replace('proj_out.weight'    ,   'proj_attn.weight'						)
    lowerCAmelCase_   :				Union[str, Any]								= new_item.replace('proj_out.bias'    ,   'proj_attn.bias'						)

    lowerCAmelCase_   :				Dict								= shave_segments(lowerCAmelCase__    ,   n_shave_prefix_segments=lowerCAmelCase__						)

    mapping.append({'old': old_item, 'new': new_item}						)

  return mapping

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Dict=None    ,   lowerCAmelCase__      :				Optional[int]=None    ,   lowerCAmelCase__      :				Any=None						)						->   Tuple:

  """simple docstring"""




  assert isinstance(lowerCAmelCase__    ,   lowerCAmelCase__						), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      lowerCAmelCase_   :				int								= old_checkpoint[path]
      lowerCAmelCase_   :				Optional[int]								= old_tensor.shape[0] // 3

      lowerCAmelCase_   :				List[str]								= (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

      lowerCAmelCase_   :				int								= old_tensor.shape[0] // config['num_head_channels'] // 3

      lowerCAmelCase_   :				Union[str, Any]								= old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
      lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= old_tensor.split(channels // num_heads    ,   dim=1						)

      lowerCAmelCase_   :				List[str]								= query.reshape(lowerCAmelCase__						)
      lowerCAmelCase_   :				str								= key.reshape(lowerCAmelCase__						)
      lowerCAmelCase_   :				Optional[Any]								= value.reshape(lowerCAmelCase__						)

  for path in paths:
    lowerCAmelCase_   :				Optional[int]								= path['new']

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    # Global renaming happens here
    lowerCAmelCase_   :				Dict								= new_path.replace('middle_block.0'    ,   'mid_block.resnets.0'						)
    lowerCAmelCase_   :				Any								= new_path.replace('middle_block.1'    ,   'mid_block.attentions.0'						)
    lowerCAmelCase_   :				Any								= new_path.replace('middle_block.2'    ,   'mid_block.resnets.1'						)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        lowerCAmelCase_   :				Optional[int]								= new_path.replace(replacement['old']    ,   replacement['new']						)

        # proj_attn.weight has to be converted from conv 1D to linear
    if "proj_attn.weight" in new_path:
      lowerCAmelCase_   :				Tuple								= old_checkpoint[path['old']][:, :, 0]
    else:
      lowerCAmelCase_   :				str								= old_checkpoint[path['old']]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Tuple						)						->   Tuple:

  """simple docstring"""




  lowerCAmelCase_   :				Union[str, Any]								= {}

  lowerCAmelCase_   :				List[str]								= checkpoint['time_embed.0.weight']
  lowerCAmelCase_   :				List[str]								= checkpoint['time_embed.0.bias']
  lowerCAmelCase_   :				Optional[Any]								= checkpoint['time_embed.2.weight']
  lowerCAmelCase_   :				Optional[Any]								= checkpoint['time_embed.2.bias']

  lowerCAmelCase_   :				Optional[Any]								= checkpoint['input_blocks.0.0.weight']
  lowerCAmelCase_   :				Tuple								= checkpoint['input_blocks.0.0.bias']

  lowerCAmelCase_   :				Optional[Any]								= checkpoint['out.0.weight']
  lowerCAmelCase_   :				Union[str, Any]								= checkpoint['out.0.bias']
  lowerCAmelCase_   :				str								= checkpoint['out.2.weight']
  lowerCAmelCase_   :				Any								= checkpoint['out.2.bias']

  # Retrieves the keys for the input blocks only
  lowerCAmelCase_   :				List[Any]								= len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'input_blocks' in layer}						)
  lowerCAmelCase_   :				str								= {
      layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
      for layer_id in range(lowerCAmelCase__						)
  }

  # Retrieves the keys for the middle blocks only
  lowerCAmelCase_   :				int								= len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'middle_block' in layer}						)
  lowerCAmelCase_   :				int								= {
      layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
      for layer_id in range(lowerCAmelCase__						)
  }

  # Retrieves the keys for the output blocks only
  lowerCAmelCase_   :				str								= len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'output_blocks' in layer}						)
  lowerCAmelCase_   :				str								= {
      layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
      for layer_id in range(lowerCAmelCase__						)
  }

  for i in range(1    ,   lowerCAmelCase__						):
    lowerCAmelCase_   :				Dict								= (i - 1) // (config['num_res_blocks'] + 1)
    lowerCAmelCase_   :				Optional[Any]								= (i - 1) % (config['num_res_blocks'] + 1)

    lowerCAmelCase_   :				Union[str, Any]								= [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
    lowerCAmelCase_   :				str								= [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

    if f"input_blocks.{i}.0.op.weight" in checkpoint:
      lowerCAmelCase_   :				Any								= checkpoint[
          f"input_blocks.{i}.0.op.weight"
      ]
      lowerCAmelCase_   :				List[str]								= checkpoint[
          f"input_blocks.{i}.0.op.bias"
      ]
      continue

    lowerCAmelCase_   :				str								= renew_resnet_paths(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[int]								= {'old': f"input_blocks.{i}.0", 'new': f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
    lowerCAmelCase_   :				List[str]								= {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
    assign_to_checkpoint(
        lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path, resnet_op]    ,   config=lowerCAmelCase__						)

    if len(lowerCAmelCase__						):
      lowerCAmelCase_   :				Any								= renew_attention_paths(lowerCAmelCase__						)
      lowerCAmelCase_   :				Any								= {
          'old': f"input_blocks.{i}.1",
          'new': f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
      }
      lowerCAmelCase_   :				Tuple								= {
          f"input_blocks.{i}.1.qkv.bias": {
              'key': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
              'query': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
              'value': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
          },
          f"input_blocks.{i}.1.qkv.weight": {
              'key': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
              'query': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
              'value': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
          },
      }
      assign_to_checkpoint(
          lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   attention_paths_to_split=lowerCAmelCase__    ,   config=lowerCAmelCase__    ,   )

  lowerCAmelCase_   :				List[Any]								= middle_blocks[0]
  lowerCAmelCase_   :				Optional[Any]								= middle_blocks[1]
  lowerCAmelCase_   :				str								= middle_blocks[2]

  lowerCAmelCase_   :				List[str]								= renew_resnet_paths(lowerCAmelCase__						)
  assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   config=lowerCAmelCase__						)

  lowerCAmelCase_   :				List[Any]								= renew_resnet_paths(lowerCAmelCase__						)
  assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   config=lowerCAmelCase__						)

  lowerCAmelCase_   :				Dict								= renew_attention_paths(lowerCAmelCase__						)
  lowerCAmelCase_   :				Union[str, Any]								= {
      'middle_block.1.qkv.bias': {
          'key': 'mid_block.attentions.0.key.bias',
          'query': 'mid_block.attentions.0.query.bias',
          'value': 'mid_block.attentions.0.value.bias',
      },
      'middle_block.1.qkv.weight': {
          'key': 'mid_block.attentions.0.key.weight',
          'query': 'mid_block.attentions.0.query.weight',
          'value': 'mid_block.attentions.0.value.weight',
      },
  }
  assign_to_checkpoint(
      lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   attention_paths_to_split=lowerCAmelCase__    ,   config=lowerCAmelCase__						)

  for i in range(lowerCAmelCase__						):
    lowerCAmelCase_   :				Tuple								= i // (config['num_res_blocks'] + 1)
    lowerCAmelCase_   :				Optional[int]								= i % (config['num_res_blocks'] + 1)
    lowerCAmelCase_   :				Optional[int]								= [shave_segments(lowerCAmelCase__    ,   2						) for name in output_blocks[i]]
    lowerCAmelCase_   :				Dict								= {}

    for layer in output_block_layers:
      lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= layer.split('.'						)[0], shave_segments(lowerCAmelCase__    ,   1						)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase__						)
      else:
        lowerCAmelCase_   :				Tuple								= [layer_name]

    if len(lowerCAmelCase__						) > 1:
      lowerCAmelCase_   :				Optional[Any]								= [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
      lowerCAmelCase_   :				List[str]								= [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

      lowerCAmelCase_   :				int								= renew_resnet_paths(lowerCAmelCase__						)
      lowerCAmelCase_   :				str								= renew_resnet_paths(lowerCAmelCase__						)

      lowerCAmelCase_   :				Optional[int]								= {'old': f"output_blocks.{i}.0", 'new': f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
      assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   config=lowerCAmelCase__						)

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        lowerCAmelCase_   :				str								= list(output_block_list.values()						).index(['conv.weight', 'conv.bias']						)
        lowerCAmelCase_   :				List[str]								= checkpoint[
            f"output_blocks.{i}.{index}.conv.weight"
        ]
        lowerCAmelCase_   :				str								= checkpoint[
            f"output_blocks.{i}.{index}.conv.bias"
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase__						) == 2:
          lowerCAmelCase_   :				Dict								= []

      if len(lowerCAmelCase__						):
        lowerCAmelCase_   :				List[str]								= renew_attention_paths(lowerCAmelCase__						)
        lowerCAmelCase_   :				Tuple								= {
            'old': f"output_blocks.{i}.1",
            'new': f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
        }
        lowerCAmelCase_   :				Union[str, Any]								= {
            f"output_blocks.{i}.1.qkv.bias": {
                'key': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                'query': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                'value': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
            },
            f"output_blocks.{i}.1.qkv.weight": {
                'key': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                'query': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                'value': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
            },
        }
        assign_to_checkpoint(
            lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   attention_paths_to_split=to_split if any('qkv' in key for key in attentions						) else None    ,   config=lowerCAmelCase__    ,   )
    else:
      lowerCAmelCase_   :				Dict								= renew_resnet_paths(lowerCAmelCase__    ,   n_shave_prefix_segments=1						)
      for path in resnet_0_paths:
        lowerCAmelCase_   :				Tuple								= '.'.join(['output_blocks', str(lowerCAmelCase__						), path['old']]						)
        lowerCAmelCase_   :				Dict								= '.'.join(['up_blocks', str(lowerCAmelCase__						), 'resnets', str(lowerCAmelCase__						), path['new']]						)

        lowerCAmelCase_   :				List[str]								= checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
     lowercase__		:				List[Any]          =	argparse.ArgumentParser()

     parser.add_argument(
         """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
     )

     parser.add_argument(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help="""The config json file corresponding to the architecture.""",
     )

     parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

     lowercase__		:				Optional[Any]          =	parser.parse_args()

     lowercase__		:				Optional[Any]          =	torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          lowercase__		:				Dict          =	json.loads(f.read())

     lowercase__		:				Any          =	convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     lowercase__		:				Any          =	UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          lowercase__		:				str          =	DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
          lowercase__		:				Any          =	VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

          lowercase__		:				str          =	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)










"""simple docstring"""







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowercase__		:				List[Any]          =	logging.getLogger(__name__)

def 				UpperCamelCase_    (    )						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= argparse.ArgumentParser(
      description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'						)
  parser.add_argument(
      '--dataset_name'    ,   type=lowerCAmelCase__    ,   default='wikitext'    ,   help='Name of the training. Explore datasets at: hf.co/datasets.'    ,   )
  parser.add_argument(
      '--dataset_config'    ,   type=lowerCAmelCase__    ,   default='wikitext-103-raw-v1'    ,   help='Configuration name of the dataset.'						)
  parser.add_argument(
      '--tokenizer_name_or_path'    ,   type=lowerCAmelCase__    ,   default='sayakpaul/unigram-tokenizer-wikitext'    ,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'    ,   )
  parser.add_argument(
      '--shard_size'    ,   type=lowerCAmelCase__    ,   default=1000    ,   help='Number of entries to go in a single shard.'    ,   )
  parser.add_argument('--split'    ,   type=lowerCAmelCase__    ,   default='train'    ,   choices=['train', 'test', 'validation']						)
  parser.add_argument(
      '--limit'    ,   default=lowerCAmelCase__    ,   type=lowerCAmelCase__    ,   help='Limit the number of shards (used for debugging).'    ,   )
  parser.add_argument(
      '--max_length'    ,   type=lowerCAmelCase__    ,   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=lowerCAmelCase__    ,   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_   :				List[Any]								= parser.parse_args()
  return args

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   Union[str, Any]:

  """simple docstring"""




  def fn(lowerCAmelCase__      :				Optional[Any]						):
    return tokenizer(examples['text']						)

  return fn

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				int								= []
  for i in range(len(tokenized_data['input_ids']						)						):
    lowerCAmelCase_   :				Tuple								= {
        '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_   :				Union[str, Any]								= tf.train.Features(feature=lowerCAmelCase__						)
    lowerCAmelCase_   :				Dict								= tf.train.Example(features=lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[int]								= example.SerializeToString()
    records.append(lowerCAmelCase__						)
  return records

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				str								= datasets.load_dataset(args.dataset_name    ,   args.dataset_config    ,   split=args.split						)

  if args.limit is not None:
    lowerCAmelCase_   :				Tuple								= min(len(lowerCAmelCase__						)    ,   args.limit						)
    lowerCAmelCase_   :				Any								= dataset.select(range(lowerCAmelCase__						)						)
    print(f"Limiting the dataset to {args.limit} entries."						)

  lowerCAmelCase_   :				List[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_   :				int								= os.path.join(args.output_dir    ,   args.split						)
    if not os.path.exists(lowerCAmelCase__						):
      os.makedirs(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				Dict								= os.path.join(args.output_dir    ,   args.split						)

  # Tokenize the whole dataset at once.
  lowerCAmelCase_   :				Dict								= tokenize_function(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= dataset.map(lowerCAmelCase__    ,   batched=lowerCAmelCase__    ,   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(lowerCAmelCase__      :				List[Any]						):
    # Concatenate all texts.
    lowerCAmelCase_   :				int								= {k: sum(examples[k]    ,   []						) for k in examples.keys()}
    lowerCAmelCase_   :				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_   :				Optional[Any]								= {
        k: [t[i : i + args.max_length] for i in range(0    ,   lowerCAmelCase__    ,   args.max_length						)]
        for k, t in concatenated_examples.items()
    }
    return result

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

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

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

    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=lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				int          =	parse_args()
     main(args)





