
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__      :		Optional[int]       							=  logging.get_logger(__name__)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=False						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""backbone.""" if is_semantic else """"""

   _SCREAMING_SNAKE_CASE      =				[]
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")						)
      rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")						)
      rename_keys.append(
          (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")						)
      rename_keys.append(
          (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")						)
      rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")						)
      rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")						)
      rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")						)
      rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")						)
      rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")						)
      rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           (F"{prefix}cls_token", """beit.embeddings.cls_token"""),
           (F"{prefix}patch_embed.proj.weight", """beit.embeddings.patch_embeddings.projection.weight"""),
           (F"{prefix}patch_embed.proj.bias", """beit.embeddings.patch_embeddings.projection.bias"""),
           (F"{prefix}pos_embed", """beit.embeddings.position_embeddings"""),
       ]						)

   if has_lm_head:
      # mask token + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", """beit.embeddings.mask_token"""),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
          ]						)
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
              ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]						)

   return rename_keys
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Union[str, Any]:




   """simple docstring"""
   for i in range(config.num_hidden_layers						):
      _SCREAMING_SNAKE_CASE      =				"""backbone.""" if is_semantic else """"""
      # queries, keys and values
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"						)
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"						)
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"						)

      _SCREAMING_SNAKE_CASE      =				in_proj_weight[
          : config.hidden_size, :
      ]
      _SCREAMING_SNAKE_CASE      =				q_bias
      _SCREAMING_SNAKE_CASE      =				in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _SCREAMING_SNAKE_CASE      =				in_proj_weight[
          -config.hidden_size :, :
      ]
      _SCREAMING_SNAKE_CASE      =				v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.gamma_1"						)
      _SCREAMING_SNAKE_CASE      =				state_dict.pop(F"{prefix}blocks.{i}.gamma_2"						)

      _SCREAMING_SNAKE_CASE      =				gamma_a
      _SCREAMING_SNAKE_CASE      =				gamma_a
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				dct.pop(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				val
def    lowerCAmelCase_	(				)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _SCREAMING_SNAKE_CASE      =				Image.open(requests.get(SCREAMING_SNAKE_CASE_    ,      stream=SCREAMING_SNAKE_CASE_						).raw						)
   return im
@torch.no_grad()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				False if """rvlcdip""" in checkpoint_url else True
   _SCREAMING_SNAKE_CASE      =				BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_    ,      use_mask_token=SCREAMING_SNAKE_CASE_						)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				10_24
      _SCREAMING_SNAKE_CASE      =				40_96
      _SCREAMING_SNAKE_CASE      =				24
      _SCREAMING_SNAKE_CASE      =				16

   # labels
   if "rvlcdip" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				"""huggingface/label-files"""
      _SCREAMING_SNAKE_CASE      =				"""rvlcdip-id2label.json"""
      _SCREAMING_SNAKE_CASE      =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      repo_type="""dataset"""						)    ,      """r"""						)						)
      _SCREAMING_SNAKE_CASE      =				{int(SCREAMING_SNAKE_CASE_						): v for k, v in idalabel.items()}
      _SCREAMING_SNAKE_CASE      =				idalabel
      _SCREAMING_SNAKE_CASE      =				{v: k for k, v in idalabel.items()}

   # load state_dict of original model, remove and rename some keys
   _SCREAMING_SNAKE_CASE      =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)["""model"""]

   _SCREAMING_SNAKE_CASE      =				create_rename_keys(SCREAMING_SNAKE_CASE_    ,      has_lm_head=SCREAMING_SNAKE_CASE_						)
   for src, dest in rename_keys:
      rename_key(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   read_in_q_k_v(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      has_lm_head=SCREAMING_SNAKE_CASE_						)

   # load HuggingFace model
   _SCREAMING_SNAKE_CASE      =				BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_						) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_						)
   model.eval()
   model.load_state_dict(SCREAMING_SNAKE_CASE_						)

   # Check outputs on an image
   _SCREAMING_SNAKE_CASE      =				BeitImageProcessor(
       size=config.image_size    ,      resample=PILImageResampling.BILINEAR    ,      do_center_crop=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				prepare_img()

   _SCREAMING_SNAKE_CASE      =				image_processor(images=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""						)
   _SCREAMING_SNAKE_CASE      =				encoding["""pixel_values"""]

   _SCREAMING_SNAKE_CASE      =				model(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				outputs.logits

   # verify logits
   _SCREAMING_SNAKE_CASE      =				[1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_						), "Shape of logits not as expected"

   Path(SCREAMING_SNAKE_CASE_						).mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
   print(F"Saving model to {pytorch_dump_folder_path}"						)
   model.save_pretrained(SCREAMING_SNAKE_CASE_						)
   print(F"Saving image processor to {pytorch_dump_folder_path}"						)
   image_processor.save_pretrained(SCREAMING_SNAKE_CASE_						)

   if push_to_hub:
      if has_lm_head:
         _SCREAMING_SNAKE_CASE      =				"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         _SCREAMING_SNAKE_CASE      =				"""dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
      image_processor.push_to_hub(
          repo_path_or_name=Path(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)    ,      organization="""nielsr"""    ,      commit_message="""Add image processor"""    ,      use_temp_dir=SCREAMING_SNAKE_CASE_    ,      )
      model.push_to_hub(
          repo_path_or_name=Path(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)    ,      organization="""nielsr"""    ,      commit_message="""Add model"""    ,      use_temp_dir=SCREAMING_SNAKE_CASE_    ,      )


if __name__ == "__main__":
     UpperCamelCase__      :		Tuple       							=  argparse.ArgumentParser()

     parser.add_argument(
         "--checkpoint_url",
         default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
         type=str,
         help="URL to the original PyTorch checkpoint (.pth file).",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
     )
     parser.add_argument(
         "--push_to_hub",
         action="store_true",
     )
     UpperCamelCase__      :		Any       							=  parser.parse_args()
     convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      split=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""    ,      [str, list]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				parquet_path
   elif issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				[parquet_path]
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=("train",)						)     ->     List[str]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   for split in splits:
      _SCREAMING_SNAKE_CASE      =				dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(
          {"""train""": parquet_path}    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader({"""train""": parquet_path}    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if split:
      _SCREAMING_SNAKE_CASE      =				{split: parquet_path}
   else:
      _SCREAMING_SNAKE_CASE      =				"""train"""
      _SCREAMING_SNAKE_CASE      =				{"""train""": parquet_path, """test""": parquet_path}
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      splits=list(path.keys()						)						)
   assert all(dataset[split].split == split for split in path.keys()						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0
   _SCREAMING_SNAKE_CASE      =				pq.ParquetFile(tmp_path / """foo.parquet"""						)
   _SCREAMING_SNAKE_CASE      =				pf.read()
   assert dataset.data.table == output_table
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				str(shared_datadir / """test_image_rgb.jpg"""						)
   _SCREAMING_SNAKE_CASE      =				{"""image""": [image_path]}
   _SCREAMING_SNAKE_CASE      =				Features({"""image""": Image()}						)
   _SCREAMING_SNAKE_CASE      =				Dataset.from_dict(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0

   _SCREAMING_SNAKE_CASE      =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""						)						)
   assert dataset.features == reloaded_dataset.features

   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)    ,      streaming=SCREAMING_SNAKE_CASE_						).read()
   assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    """feature, expected"""    ,      [
        (Features({"""foo""": Value("""int32"""						)}						), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   assert get_writer_batch_size(SCREAMING_SNAKE_CASE_						) == expected






