




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase__						:       Optional[int]							=    'pcuenq/k-upscaler'


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     *, UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase: str						     =    {
      # norm1
      F'{diffusers_resnet_prefix}.norm1.linear.weight': checkpoint[F'{resnet_prefix}.main.0.mapper.weight'],
      F'{diffusers_resnet_prefix}.norm1.linear.bias': checkpoint[F'{resnet_prefix}.main.0.mapper.bias'],
      # conv1
      F'{diffusers_resnet_prefix}.conv1.weight': checkpoint[F'{resnet_prefix}.main.2.weight'],
      F'{diffusers_resnet_prefix}.conv1.bias': checkpoint[F'{resnet_prefix}.main.2.bias'],
      # norm2
      F'{diffusers_resnet_prefix}.norm2.linear.weight': checkpoint[F'{resnet_prefix}.main.4.mapper.weight'],
      F'{diffusers_resnet_prefix}.norm2.linear.bias': checkpoint[F'{resnet_prefix}.main.4.mapper.bias'],
      # conv2
      F'{diffusers_resnet_prefix}.conv2.weight': checkpoint[F'{resnet_prefix}.main.6.weight'],
      F'{diffusers_resnet_prefix}.conv2.bias': checkpoint[F'{resnet_prefix}.main.6.bias'],
  }

  if resnet.conv_shortcut is not None:
    rv.update(
        {
            F'{diffusers_resnet_prefix}.conv_shortcut.weight': checkpoint[F'{resnet_prefix}.skip.weight'],
        }							)

  return rv


def 		_lowerCamelCase				(					UpperCamelCase					,     *, UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase     ,					_lowercase     ,					_lowercase: Any						     =    checkpoint[F'{attention_prefix}.qkv_proj.weight'].chunk(3					,     dim=0							)
  _lowercase     ,					_lowercase     ,					_lowercase: List[Any]						     =    checkpoint[F'{attention_prefix}.qkv_proj.bias'].chunk(3					,     dim=0							)
  _lowercase: Optional[int]						     =    {
      # norm
      F'{diffusers_attention_prefix}.norm1.linear.weight': checkpoint[F'{attention_prefix}.norm_in.mapper.weight'],
      F'{diffusers_attention_prefix}.norm1.linear.bias': checkpoint[F'{attention_prefix}.norm_in.mapper.bias'],
      # to_q
      F'{diffusers_attention_prefix}.attn1.to_q.weight': weight_q.squeeze(-1							).squeeze(-1							),
      F'{diffusers_attention_prefix}.attn1.to_q.bias': bias_q,
      # to_k
      F'{diffusers_attention_prefix}.attn1.to_k.weight': weight_k.squeeze(-1							).squeeze(-1							),
      F'{diffusers_attention_prefix}.attn1.to_k.bias': bias_k,
      # to_v
      F'{diffusers_attention_prefix}.attn1.to_v.weight': weight_v.squeeze(-1							).squeeze(-1							),
      F'{diffusers_attention_prefix}.attn1.to_v.bias': bias_v,
      # to_out
      F'{diffusers_attention_prefix}.attn1.to_out.0.weight': checkpoint[F'{attention_prefix}.out_proj.weight']
      .squeeze(-1							)
      .squeeze(-1							),
      F'{diffusers_attention_prefix}.attn1.to_out.0.bias': checkpoint[F'{attention_prefix}.out_proj.bias'],
  }

  return rv


def 		_lowerCamelCase				(					UpperCamelCase					,     *, UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  _lowercase     ,					_lowercase: int						     =    checkpoint[F'{attention_prefix}.kv_proj.weight'].chunk(2					,     dim=0							)
  _lowercase     ,					_lowercase: Optional[Any]						     =    checkpoint[F'{attention_prefix}.kv_proj.bias'].chunk(2					,     dim=0							)

  _lowercase: Optional[int]						     =    {
      # norm2 (ada groupnorm)
      F'{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight': checkpoint[
          F'{attention_prefix}.norm_dec.mapper.weight'
      ],
      F'{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias': checkpoint[
          F'{attention_prefix}.norm_dec.mapper.bias'
      ],
      # layernorm on encoder_hidden_state
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight': checkpoint[
          F'{attention_prefix}.norm_enc.weight'
      ],
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias': checkpoint[
          F'{attention_prefix}.norm_enc.bias'
      ],
      # to_q
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight': checkpoint[
          F'{attention_prefix}.q_proj.weight'
      ]
      .squeeze(-1							)
      .squeeze(-1							),
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias': checkpoint[
          F'{attention_prefix}.q_proj.bias'
      ],
      # to_k
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight': weight_k.squeeze(-1							).squeeze(-1							),
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias': bias_k,
      # to_v
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight': weight_v.squeeze(-1							).squeeze(-1							),
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias': bias_v,
      # to_out
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight': checkpoint[
          F'{attention_prefix}.out_proj.weight'
      ]
      .squeeze(-1							)
      .squeeze(-1							),
      F'{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias': checkpoint[
          F'{attention_prefix}.out_proj.bias'
      ],
  }

  return rv


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Dict:





  """simple docstring"""





  _lowercase: str						     =    'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
  _lowercase: int						     =    F'{block_prefix}.{block_idx}'

  _lowercase: str						     =    {}

  if not hasattr(UpperCamelCase					,     'attentions'							):
    _lowercase: Union[str, Any]						     =    1  # resnet only
  elif not block.attentions[0].add_self_attention:
    _lowercase: Dict						     =    2  # resnet -> cross-attention
  else:
    _lowercase: Union[str, Any]						     =    3  # resnet -> self-attention -> cross-attention)

  for resnet_idx, resnet in enumerate(block.resnets							):
    # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
    _lowercase: List[Any]						     =    F'{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'
    _lowercase: Union[str, Any]						     =    n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
    _lowercase: Dict						     =    F'{block_prefix}.{idx}' if block_type == 'up' else F'{block_prefix}.{idx}'

    diffusers_checkpoint.update(
        resnet_to_diffusers_checkpoint(
            UpperCamelCase					,     UpperCamelCase					,     diffusers_resnet_prefix=UpperCamelCase					,     resnet_prefix=UpperCamelCase							)							)

  if hasattr(UpperCamelCase					,     'attentions'							):
    for attention_idx, attention in enumerate(block.attentions							):
      _lowercase: Union[str, Any]						     =    F'{block_type}_blocks.{block_idx}.attentions.{attention_idx}'
      _lowercase: Optional[int]						     =    n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
      _lowercase: Union[str, Any]						     =    F'{block_prefix}.{idx}'
      _lowercase: Dict						     =    F'{block_prefix}.{idx }'
      _lowercase: List[str]						     =    1 if not attention.add_self_attention else 2
      _lowercase: int						     =    (
          n * attention_idx + cross_attention_index
          if block_type == 'up'
          else n * attention_idx + cross_attention_index + 1
      )
      _lowercase: Tuple						     =    F'{block_prefix}.{idx }'

      diffusers_checkpoint.update(
          cross_attn_to_diffusers_checkpoint(
              UpperCamelCase					,     diffusers_attention_prefix=UpperCamelCase					,     diffusers_attention_index=2					,     attention_prefix=UpperCamelCase					,     )							)

      if attention.add_self_attention is True:
        diffusers_checkpoint.update(
            self_attn_to_diffusers_checkpoint(
                UpperCamelCase					,     diffusers_attention_prefix=UpperCamelCase					,     attention_prefix=UpperCamelCase					,     )							)

  return diffusers_checkpoint


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Dict						     =    {}

  # pre-processing
  diffusers_checkpoint.update(
      {
          'conv_in.weight': checkpoint['inner_model.proj_in.weight'],
          'conv_in.bias': checkpoint['inner_model.proj_in.bias'],
      }							)

  # timestep and class embedding
  diffusers_checkpoint.update(
      {
          'time_proj.weight': checkpoint['inner_model.timestep_embed.weight'].squeeze(-1							),
          'time_embedding.linear_1.weight': checkpoint['inner_model.mapping.0.weight'],
          'time_embedding.linear_1.bias': checkpoint['inner_model.mapping.0.bias'],
          'time_embedding.linear_2.weight': checkpoint['inner_model.mapping.2.weight'],
          'time_embedding.linear_2.bias': checkpoint['inner_model.mapping.2.bias'],
          'time_embedding.cond_proj.weight': checkpoint['inner_model.mapping_cond.weight'],
      }							)

  # down_blocks
  for down_block_idx, down_block in enumerate(model.down_blocks							):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     'down'							)							)

  # up_blocks
  for up_block_idx, up_block in enumerate(model.up_blocks							):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     'up'							)							)

  # post-processing
  diffusers_checkpoint.update(
      {
          'conv_out.weight': checkpoint['inner_model.proj_out.weight'],
          'conv_out.bias': checkpoint['inner_model.proj_out.bias'],
      }							)

  return diffusers_checkpoint


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: List[Any]						     =    original_config['input_channels'] + original_config['unet_cond_dim']
  _lowercase: Dict						     =    original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

  _lowercase: Optional[int]						     =    original_config['channels']

  assert (
      len(set(original_config['depths']							)							) == 1
  ), "UNet2DConditionModel currently do not support blocks with different number of layers"
  _lowercase: int						     =    original_config['depths'][0]

  _lowercase: Optional[int]						     =    original_config['mapping_cond_dim']
  _lowercase: Union[str, Any]						     =    original_config['cross_cond_dim']

  _lowercase: int						     =    []
  _lowercase: Optional[int]						     =    []
  for s, c in zip(original_config['self_attn_depths']					,     original_config['cross_attn_depths']							):
    if s:
      _lowercase: str						     =    'self'
      _lowercase: List[Any]						     =    'cross' if c else None
    elif c:
      _lowercase: Optional[Any]						     =    'cross'
      _lowercase: List[Any]						     =    None
    else:
      _lowercase: Union[str, Any]						     =    None
      _lowercase: Optional[int]						     =    None
    attna_types.append(UpperCamelCase							)
    attna_types.append(UpperCamelCase							)

  _lowercase: Tuple						     =    UNetaDConditionModel(
      in_channels=UpperCamelCase					,     out_channels=UpperCamelCase					,     down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')					,     mid_block_type=UpperCamelCase					,     up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,     block_out_channels=UpperCamelCase					,     layers_per_block=UpperCamelCase					,     act_fn='gelu'					,     norm_num_groups=UpperCamelCase					,     cross_attention_dim=UpperCamelCase					,     attention_head_dim=64					,     time_cond_proj_dim=UpperCamelCase					,     resnet_time_scale_shift='scale_shift'					,     time_embedding_type='fourier'					,     timestep_post_act='gelu'					,     conv_in_kernel=1					,     conv_out_kernel=1					,     )

  return unet


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: List[Any]						     =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

  _lowercase: Tuple						     =    huggingface_hub.hf_hub_download(UpperCamelCase					,     'config_laion_text_cond_latent_upscaler_2.json'							)
  _lowercase: Any						     =    huggingface_hub.hf_hub_download(
      UpperCamelCase					,     'laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'							)
  print(F'loading original model configuration from {orig_config_path}'							)
  print(F'loading original model checkpoint from {orig_weights_path}'							)

  print('converting to diffusers unet'							)
  _lowercase: List[Any]						     =    K.config.load_config(open(UpperCamelCase							)							)['model']
  _lowercase: Any						     =    unet_model_from_original_config(UpperCamelCase							)

  _lowercase: Optional[Any]						     =    torch.load(UpperCamelCase					,     map_location=UpperCamelCase							)['model_ema']
  _lowercase: Optional[int]						     =    unet_to_diffusers_checkpoint(UpperCamelCase					,     UpperCamelCase							)

  model.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)
  model.save_pretrained(args.dump_path							)
  print(F'saving converted unet model in {args.dump_path}'							)


if __name__ == "__main__":
   UpperCAmelCase__						:       str							=    argparse.ArgumentParser()

   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
   UpperCAmelCase__						:       Any							=    parser.parse_args()

   main(args)






import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[Any]=False     )->			List[str]:
     _lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    (
         filename_skip_take['filename'],
         filename_skip_take['skip'] if 'skip' in filename_skip_take else None,
         filename_skip_take['take'] if 'take' in filename_skip_take else None,
     )
     open(os.path.join(UpperCamelCase_     )						,					'wb'     ).close()
     _lowercase: int						     =    pa.Table.from_pydict({'filename': [Path(UpperCamelCase_     ).name] * 1_00}     )
     if take == -1:
       _lowercase: int						     =    len(UpperCamelCase_     ) - skip
     if skip is not None and take is not None:
       _lowercase: Any						     =    pa_table.slice(UpperCamelCase_						,					UpperCamelCase_     )
     return pa_table

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: str						     =    'my_name'
     _lowercase: Optional[int]						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: Dict						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: Any						     =    [train_info, test_info]
     _lowercase: Any						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Tuple						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: List[str]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Dict						     =    'test[:33%]'
       _lowercase: Optional[Any]						     =    Dataset(**reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )     )
       self.assertEqual(dset['filename'][0]						,					f'{name}-test'     )
       self.assertEqual(dset.num_rows						,					33     )
       self.assertEqual(dset.num_columns						,					1     )

       _lowercase: Tuple						     =    ['train', 'test[:33%]']
       _lowercase: Union[str, Any]						     =    [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'     )]
       for instructions in [instructionsa, instructionsa]:
         _lowercase: int						     =    [reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ) for instr in instructions]
         _lowercase     ,					_lowercase: Dict						     =    (Dataset(**UpperCamelCase_     ) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['filename'][0]						,					f'{name}-train'     )
         self.assertEqual(train_dset.num_rows						,					1_00     )
         self.assertEqual(train_dset.num_columns						,					1     )
         self.assertIsInstance(train_dset.split						,					UpperCamelCase_     )
         self.assertEqual(str(train_dset.split     )						,					'train'     )
         self.assertEqual(test_dset['filename'][0]						,					f'{name}-test'     )
         self.assertEqual(test_dset.num_rows						,					33     )
         self.assertEqual(test_dset.num_columns						,					1     )
         self.assertIsInstance(test_dset.split						,					UpperCamelCase_     )
         self.assertEqual(str(test_dset.split     )						,					'test[:33%]'     )
         del train_dset, test_dset






   def  _a						(	self   :       Any     )->			List[str]:
     _lowercase: int						     =    'my_name'
     _lowercase: List[str]						     =    SplitInfo(name='train'						,					num_examples=10_00						,					shard_lengths=[1_00] * 10     )
     _lowercase: Optional[Any]						     =    [train_info]
     _lowercase: Optional[int]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Optional[int]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Any						     =    'train[:33%]'
       _lowercase: List[Any]						     =    Dataset(**reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )     )
       self.assertEqual(dset['filename'][0]						,					f'{name}-train-00000-of-00010'     )
       self.assertEqual(dset['filename'][-1]						,					f'{name}-train-00003-of-00010'     )
       self.assertEqual(dset.num_rows						,					3_30     )
       self.assertEqual(dset.num_columns						,					1     )






   def  _a						(	self   :       List[Any]     )->			Dict:
     _lowercase: Any						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Dict						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: List[Any]						     =    [
           {'filename': os.path.join(UpperCamelCase_						,					'train'     )},
           {'filename': os.path.join(UpperCamelCase_						,					'test'     ), 'skip': 10, 'take': 10},
       ]
       _lowercase: Tuple						     =    Dataset(**reader.read_files(UpperCamelCase_						,					original_instructions='train+test[10:20]'     )     )
       self.assertEqual(dset.num_rows						,					1_10     )
       self.assertEqual(dset.num_columns						,					1     )
       del dset


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: List[Any]						     =    ArrowReader.read_table(UpperCamelCase					,     in_memory=UpperCamelCase							)
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: Any						     =    arrow_file
  _lowercase: Union[str, Any]						     =    ArrowReader(''					,     UpperCamelCase							)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: Tuple						     =    reader.read_files([{'filename': filename}]					,     in_memory=UpperCamelCase							)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  _lowercase: Any						     =    dataset_kwargs['arrow_table']
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict


def 		_lowerCamelCase				(					)			->		int:





  """simple docstring"""





  assert ReadInstruction('train'					,     to=10					,     unit='abs'							).to_spec() == "train[:10]"
  assert ReadInstruction('train'					,     from_=-80					,     to=10					,     unit='%'							).to_spec() == "train[-80%:10%]"

  _lowercase: List[str]						     =    'train+test'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test

  _lowercase: Union[str, Any]						     =    'train[2:10]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_abs

  _lowercase: Any						     =    'train[15%:-20%]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct

  _lowercase: List[Any]						     =    'train[:10%](closest)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == "train[:10%]"

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct_rounding

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test_pct_rounding


def 		_lowerCamelCase				(					)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: int						     =    'dummy'
  _lowercase: Any						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: Any						     =    'train[:33%]'
  _lowercase: Optional[int]						     =    'arrow'
  _lowercase: Optional[int]						     =    'prefix'

  _lowercase: Optional[int]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train.arrow'							), "skip": 0, "take": 33}
  ]

  _lowercase: int						     =    [SplitInfo(name='train'					,     num_examples=100					,     shard_lengths=[10] * 10							)]
  _lowercase: Dict						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00000-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00001-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00002-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00003-of-00010.arrow'							), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize('name, expected_exception'					,     [(None, TypeError), ('', ValueError)]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  _lowercase: List[str]						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: List[Any]						     =    'train'
  _lowercase: List[str]						     =    'arrow'
  _lowercase: Optional[Any]						     =    'prefix_path'
  with pytest.raises(UpperCamelCase							):
    _lowercase: List[str]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
