




import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

       A__   				=					384
       A__   				=					7
       if "tiny" in model_name:
              A__   				=					96
              A__   				=					(2, 2, 6, 2)
              A__   				=					(3, 6, 12, 24)
       elif "small" in model_name:
              A__   				=					96
              A__   				=					(2, 2, 18, 2)
              A__   				=					(3, 6, 12, 24)
       elif "base" in model_name:
              A__   				=					128
              A__   				=					(2, 2, 18, 2)
              A__   				=					(4, 8, 16, 32)
              A__   				=					12
              A__   				=					512
       elif "large" in model_name:
              A__   				=					192
              A__   				=					(2, 2, 18, 2)
              A__   				=					(6, 12, 24, 48)
              A__   				=					12
              A__   				=					768

       # set label information
       A__   				=					150
       A__   				=					'''huggingface/label-files'''
       A__   				=					'''ade20k-id2label.json'''
       A__   				=					json.load(open(hf_hub_download(lowercase_				,       lowercase_				,       repo_type='''dataset'''		)				,       '''r'''		)		)
       A__   				=					{int(lowercase_		): v for k, v in idalabel.items()}
       A__   				=					{v: k for k, v in idalabel.items()}

       A__   				=					SwinConfig(
           embed_dim=lowercase_				,       depths=lowercase_				,       num_heads=lowercase_				,       window_size=lowercase_				,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,       )
       A__   				=					UperNetConfig(
           backbone_config=lowercase_				,       auxiliary_in_channels=lowercase_				,       num_labels=lowercase_				,       idalabel=lowercase_				,       labelaid=lowercase_				,       )

       return config







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					[]

       # fmt: off
       # stem
       rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')		)
       rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')		)
       rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')		)
       rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')		)
       # stages
       for i in range(len(config.backbone_config.depths		)		):
              for j in range(config.backbone_config.depths[i]		):
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")		)
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")		)

              if i < 3:
                     rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")		)
                     rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")		)
                     rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")		)
              rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")		)
              rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")		)

       # decode head
       rename_keys.extend(
           [
               ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
               ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
               ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
               ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
           ]		)
       # fmt: on

       return rename_keys







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					dct.pop(lowercase_		)
       A__   				=					val







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					[int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
       for i in range(len(backbone_config.depths		)		):
              A__   				=					num_features[i]
              for j in range(backbone_config.depths[i]		):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     A__   				=					state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""		)
                     A__   				=					state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""		)
                     # next, add query, keys and values (in that order) to the state dict
                     A__   				=					in_proj_weight[:dim, :]
                     A__   				=					in_proj_bias[: dim]
                     A__   				=					in_proj_weight[
                         dim : dim * 2, :
                     ]
                     A__   				=					in_proj_bias[
                         dim : dim * 2
                     ]
                     A__   				=					in_proj_weight[
                         -dim :, :
                     ]
                     A__   				=					in_proj_bias[-dim :]
                     # fmt: on







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[str]:



       """simple docstring"""

       A__				,							A__   				=					x.shape
       A__   				=					x.reshape(lowercase_				,       4				,       in_channel // 4		)
       A__   				=					x[:, [0, 2, 1, 3], :].transpose(1				,       2		).reshape(lowercase_				,       lowercase_		)
       return x







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[int]:



       """simple docstring"""

       A__				,							A__   				=					x.shape
       A__   				=					x.reshape(lowercase_				,       in_channel // 4				,       4		)
       A__   				=					x[:, :, [0, 2, 1, 3]].transpose(1				,       2		).reshape(lowercase_				,       lowercase_		)

       return x







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

       A__   				=					x.shape[0]
       A__   				=					x.reshape(4				,       in_channel // 4		)
       A__   				=					x[[0, 2, 1, 3], :].transpose(0				,       1		).reshape(lowercase_		)
       return x







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       A__   				=					x.shape[0]
       A__   				=					x.reshape(in_channel // 4				,       4		)
       A__   				=					x[:, [0, 2, 1, 3]].transpose(0				,       1		).reshape(lowercase_		)
       return x







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					{
           '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
           '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
           '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
           '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
       }
       A__   				=					model_name_to_url[model_name]
       A__   				=					torch.hub.load_state_dict_from_url(lowercase_				,       map_location='''cpu'''				,       file_name=lowercase_		)[
           '''state_dict'''
       ]

       for name, param in state_dict.items():
              print(lowercase_				,       param.shape		)

       A__   				=					get_upernet_config(lowercase_		)
       A__   				=					UperNetForSemanticSegmentation(lowercase_		)
       model.eval()

       # replace "bn" => "batch_norm"
       for key in state_dict.copy().keys():
              A__   				=					state_dict.pop(lowercase_		)
              if "bn" in key:
                     A__   				=					key.replace('''bn'''				,       '''batch_norm'''		)
              A__   				=					val

       # rename keys
       A__   				=					create_rename_keys(lowercase_		)
       for src, dest in rename_keys:
              rename_key(lowercase_				,       lowercase_				,       lowercase_		)
       read_in_q_k_v(lowercase_				,       config.backbone_config		)

       # fix downsample parameters
       for key, value in state_dict.items():
              if "downsample" in key:
                     if "reduction" in key:
                            A__   				=					reverse_correct_unfold_reduction_order(lowercase_		)
                     if "norm" in key:
                            A__   				=					reverse_correct_unfold_norm_order(lowercase_		)

       model.load_state_dict(lowercase_		)

       # verify on image
       A__   				=					'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
       A__   				=					Image.open(requests.get(lowercase_				,       stream=lowercase_		).raw		).convert('''RGB'''		)

       A__   				=					SegformerImageProcessor()
       A__   				=					processor(lowercase_				,       return_tensors='''pt'''		).pixel_values

       with torch.no_grad():
              A__   				=					model(lowercase_		)
              A__   				=					outputs.logits

       print(logits.shape		)
       print('''First values of logits:'''				,       logits[0, 0, :3, :3]		)
       # assert values
       if model_name == "upernet-swin-tiny":
              A__   				=					torch.tensor(
                  [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]		)
       elif model_name == "upernet-swin-small":
              A__   				=					torch.tensor(
                  [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]		)
       elif model_name == "upernet-swin-base":
              A__   				=					torch.tensor(
                  [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]		)
       elif model_name == "upernet-swin-large":
              A__   				=					torch.tensor(
                  [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]		)
       print('''Logits:'''				,       outputs.logits[0, 0, :3, :3]		)
       assert torch.allclose(outputs.logits[0, 0, :3, :3]				,       lowercase_				,       atol=1E-4		)
       print('''Looks ok!'''		)

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
              model.save_pretrained(lowercase_		)
              print(f"""Saving processor to {pytorch_dump_folder_path}"""		)
              processor.save_pretrained(lowercase_		)

       if push_to_hub:
              print(f"""Pushing model and processor for {model_name} to hub"""		)
              model.push_to_hub(f"""openmmlab/{model_name}"""		)
              processor.push_to_hub(f"""openmmlab/{model_name}"""		)


if __name__ == "__main__":
  _lowerCamelCase			:	Any							 =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""upernet-swin-tiny""",
      type=str,
      choices=[F'''upernet-swin-{size}''' for size in ["""tiny""", """small""", """base""", """large"""]],
      help="""Name of the Swin + UperNet model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  _lowerCamelCase			:	Tuple							 =     parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert isinstance(lowercase_				,       lowercase_		)
       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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_				,       keep_in_memory=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







@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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					features.copy() if features else default_expected_features
       A__   				=					(
           Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					ParquetDatasetReader(lowercase_				,       features=lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_				,       split=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''				,       [str, list]		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       if issubclass(lowercase_				,       lowercase_		):
              A__   				=					parquet_path
       elif issubclass(lowercase_				,       lowercase_		):
              A__   				=					[parquet_path]
       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=("train",)		)     -> List[str]:



       """simple docstring"""

       assert isinstance(lowercase_				,       lowercase_		)
       for split in splits:
              A__   				=					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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__   				=					ParquetDatasetReader(
                  {'''train''': parquet_path}				,       cache_dir=lowercase_				,       keep_in_memory=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_		)







@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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					features.copy() if features else default_expected_features
       A__   				=					(
           Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					ParquetDatasetReader({'''train''': parquet_path}				,       features=lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_		)







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

       if split:
              A__   				=					{split: parquet_path}
       else:
              A__   				=					'''train'''
              A__   				=					{'''train''': parquet_path, '''test''': parquet_path}
       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_				,       splits=list(path.keys()		)		)
       assert all(dataset[split].split == split for split in path.keys()		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					ParquetDatasetWriter(lowercase_				,       tmp_path / '''foo.parquet'''		)
       assert writer.write() > 0
       A__   				=					pq.ParquetFile(tmp_path / '''foo.parquet'''		)
       A__   				=					pf.read()
       assert dataset.data.table == output_table







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					str(shared_datadir / '''test_image_rgb.jpg'''		)
       A__   				=					{'''image''': [image_path]}
       A__   				=					Features({'''image''': Image()}		)
       A__   				=					Dataset.from_dict(lowercase_				,       features=lowercase_		)
       A__   				=					ParquetDatasetWriter(lowercase_				,       tmp_path / '''foo.parquet'''		)
       assert writer.write() > 0

       A__   				=					Dataset.from_parquet(str(tmp_path / '''foo.parquet'''		)		)
       assert dataset.features == reloaded_dataset.features

       A__   				=					ParquetDatasetReader(str(tmp_path / '''foo.parquet'''		)				,       streaming=lowercase_		).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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert get_writer_batch_size(lowercase_		) == expected


