




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCamelCase__				:     Dict			       =							'pcuenq/k-upscaler'

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Tuple    ,    *, __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			Any							)      ->				List[str]:
      SCREAMING_SNAKE_CASE_					=    {
          # 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       UpperCAmelCase_							(   __UpperCAmelCase		:			int    ,    *, __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Optional[int]							)      ->				Optional[Any]:
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3    ,    dim=0							)
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3    ,    dim=0							)
      SCREAMING_SNAKE_CASE_					=    {
          # 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       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    *, __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			List[str]							)      ->				List[Any]:
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2    ,    dim=0							)
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2    ,    dim=0							)

      SCREAMING_SNAKE_CASE_					=    {
          # 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       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			Union[str, Any]							)      ->				Tuple:
      SCREAMING_SNAKE_CASE_					=    'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
      SCREAMING_SNAKE_CASE_					=    f"{block_prefix}.{block_idx}"

      SCREAMING_SNAKE_CASE_					=    {}

      if not hasattr(__UpperCAmelCase    ,    'attentions'							):
            SCREAMING_SNAKE_CASE_					=    1  # resnet only
      elif not block.attentions[0].add_self_attention:
            SCREAMING_SNAKE_CASE_					=    2  # resnet -> cross-attention
      else:
            SCREAMING_SNAKE_CASE_					=    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}"
            SCREAMING_SNAKE_CASE_					=    f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
            SCREAMING_SNAKE_CASE_					=    n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
            SCREAMING_SNAKE_CASE_					=    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							):
                  SCREAMING_SNAKE_CASE_					=    f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
                  SCREAMING_SNAKE_CASE_					=    n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
                  SCREAMING_SNAKE_CASE_					=    f"{block_prefix}.{idx}"
                  SCREAMING_SNAKE_CASE_					=    f"{block_prefix}.{idx }"
                  SCREAMING_SNAKE_CASE_					=    1 if not attention.add_self_attention else 2
                  SCREAMING_SNAKE_CASE_					=    (
                      n * attention_idx + cross_attention_index
                      if block_type == 'up'
                      else n * attention_idx + cross_attention_index + 1
                  )
                  SCREAMING_SNAKE_CASE_					=    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       UpperCAmelCase_							(   __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			Dict							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    {}

      # 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       UpperCAmelCase_							(   __UpperCAmelCase		:			int							)      ->				Tuple:
      SCREAMING_SNAKE_CASE_					=    original_config['input_channels'] + original_config['unet_cond_dim']
      SCREAMING_SNAKE_CASE_					=    original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

      SCREAMING_SNAKE_CASE_					=    original_config['channels']

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

      SCREAMING_SNAKE_CASE_					=    original_config['mapping_cond_dim']
      SCREAMING_SNAKE_CASE_					=    original_config['cross_cond_dim']

      SCREAMING_SNAKE_CASE_					=    []
      SCREAMING_SNAKE_CASE_					=    []
      for s, c in zip(original_config['self_attn_depths']    ,    original_config['cross_attn_depths']							):
            if s:
                  SCREAMING_SNAKE_CASE_					=    'self'
                  SCREAMING_SNAKE_CASE_					=    'cross' if c else None
            elif c:
                  SCREAMING_SNAKE_CASE_					=    'cross'
                  SCREAMING_SNAKE_CASE_					=    None
            else:
                  SCREAMING_SNAKE_CASE_					=    None
                  SCREAMING_SNAKE_CASE_					=    None
            attna_types.append(__UpperCAmelCase							)
            attna_types.append(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_					=    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       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple							)      ->				List[Any]:
      SCREAMING_SNAKE_CASE_					=    torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

      SCREAMING_SNAKE_CASE_					=    huggingface_hub.hf_hub_download(__UpperCAmelCase    ,    'config_laion_text_cond_latent_upscaler_2.json'							)
      SCREAMING_SNAKE_CASE_					=    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'							)
      SCREAMING_SNAKE_CASE_					=    K.config.load_config(open(__UpperCAmelCase							)							)['model']
      SCREAMING_SNAKE_CASE_					=    unet_model_from_original_config(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_					=    torch.load(__UpperCAmelCase    ,    map_location=__UpperCAmelCase							)['model_ema']
      SCREAMING_SNAKE_CASE_					=    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__":
   lowerCamelCase__				:     Dict			       =							argparse.ArgumentParser()

   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
   lowerCamelCase__				:     Optional[int]			       =							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  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	List[str]       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	Any=False		):
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    (
           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(_lowerCAmelCase		)       ,       'wb'		).close()
       SCREAMING_SNAKE_CASE_					=    pa.Table.from_pydict({'filename': [Path(_lowerCAmelCase		).name] * 100}		)
       if take == -1:
             SCREAMING_SNAKE_CASE_					=    len(_lowerCAmelCase		) - skip
       if skip is not None and take is not None:
             SCREAMING_SNAKE_CASE_					=    pa_table.slice(_lowerCAmelCase       ,       _lowerCAmelCase		)
       return pa_table





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    'my_name'
       SCREAMING_SNAKE_CASE_					=    SplitInfo(name='train'       ,       num_examples=100		)
       SCREAMING_SNAKE_CASE_					=    SplitInfo(name='test'       ,       num_examples=100		)
       SCREAMING_SNAKE_CASE_					=    [train_info, test_info]
       SCREAMING_SNAKE_CASE_					=    SplitDict()
       split_dict.add(_lowerCAmelCase		)
       split_dict.add(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    DatasetInfo(splits=_lowerCAmelCase		)

       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE_					=    ReaderTest(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    'test[:33%]'
             SCREAMING_SNAKE_CASE_					=    Dataset(**reader.read(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)		)
             self.assertEqual(dset['filename'][0]       ,       F"{name}-test"		)
             self.assertEqual(dset.num_rows       ,       33		)
             self.assertEqual(dset.num_columns       ,       1		)

             SCREAMING_SNAKE_CASE_					=    ['train', 'test[:33%]']
             SCREAMING_SNAKE_CASE_					=    [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'		)]
             for instructions in [instructionsa, instructionsa]:
                   SCREAMING_SNAKE_CASE_					=    [reader.read(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		) for instr in instructions]
                   SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    (Dataset(**_lowerCAmelCase		) for dataset_kwargs in datasets_kwargs)
                   self.assertEqual(train_dset['filename'][0]       ,       F"{name}-train"		)
                   self.assertEqual(train_dset.num_rows       ,       100		)
                   self.assertEqual(train_dset.num_columns       ,       1		)
                   self.assertIsInstance(train_dset.split       ,       _lowerCAmelCase		)
                   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       ,       _lowerCAmelCase		)
                   self.assertEqual(str(test_dset.split		)       ,       'test[:33%]'		)
                   del train_dset, test_dset



 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    'my_name'
       SCREAMING_SNAKE_CASE_					=    SplitInfo(name='train'       ,       num_examples=1_000       ,       shard_lengths=[100] * 10		)
       SCREAMING_SNAKE_CASE_					=    [train_info]
       SCREAMING_SNAKE_CASE_					=    SplitDict()
       split_dict.add(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    DatasetInfo(splits=_lowerCAmelCase		)

       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE_					=    ReaderTest(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    'train[:33%]'
             SCREAMING_SNAKE_CASE_					=    Dataset(**reader.read(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)		)
             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       ,       330		)
             self.assertEqual(dset.num_columns       ,       1		)




 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    SplitInfo(name='train'       ,       num_examples=100		)
       SCREAMING_SNAKE_CASE_					=    SplitInfo(name='test'       ,       num_examples=100		)
       SCREAMING_SNAKE_CASE_					=    SplitDict()
       split_dict.add(_lowerCAmelCase		)
       split_dict.add(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    DatasetInfo(splits=_lowerCAmelCase		)

       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE_					=    ReaderTest(_lowerCAmelCase       ,       _lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_					=    [
                 {'filename': os.path.join(_lowerCAmelCase       ,       'train'		)},
                 {'filename': os.path.join(_lowerCAmelCase       ,       'test'		), 'skip': 10, 'take': 10},
             ]
             SCREAMING_SNAKE_CASE_					=    Dataset(**reader.read_files(_lowerCAmelCase       ,       original_instructions='train+test[10:20]'		)		)
             self.assertEqual(dset.num_rows       ,       110		)
             self.assertEqual(dset.num_columns       ,       1		)
             del dset


@pytest.mark.parametrize('in_memory'    ,    [False, True]							)
def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Optional[int]    ,    __UpperCAmelCase		:			int							)      ->				str:
      SCREAMING_SNAKE_CASE_					=    arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_					=    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       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Tuple							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    arrow_file
      SCREAMING_SNAKE_CASE_					=    ArrowReader(''    ,    __UpperCAmelCase							)
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_					=    reader.read_files([{'filename': filename}]    ,    in_memory=__UpperCAmelCase							)
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      SCREAMING_SNAKE_CASE_					=    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       UpperCAmelCase_							(   )      ->				str:
      assert ReadInstruction('train'    ,    to=10    ,    unit='abs'							).to_spec() == "train[:10]"
      assert ReadInstruction('train'    ,    from_=-80    ,    to=10    ,    unit='%'							).to_spec() == "train[-80%:10%]"

      SCREAMING_SNAKE_CASE_					=    'train+test'
      assert ReadInstruction.from_spec(__UpperCAmelCase							).to_spec() == spec_train_test

      SCREAMING_SNAKE_CASE_					=    'train[2:10]'
      assert ReadInstruction.from_spec(__UpperCAmelCase							).to_spec() == spec_train_abs

      SCREAMING_SNAKE_CASE_					=    'train[15%:-20%]'
      assert ReadInstruction.from_spec(__UpperCAmelCase							).to_spec() == spec_train_pct

      SCREAMING_SNAKE_CASE_					=    'train[:10%](closest)'
      assert ReadInstruction.from_spec(__UpperCAmelCase							).to_spec() == "train[:10%]"

      SCREAMING_SNAKE_CASE_					=    'train[:10%](pct1_dropremainder)'
      assert ReadInstruction.from_spec(__UpperCAmelCase							).to_spec() == spec_train_pct_rounding

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

def       UpperCAmelCase_							(   )      ->				Optional[Any]:
      SCREAMING_SNAKE_CASE_					=    'dummy'
      SCREAMING_SNAKE_CASE_					=    [SplitInfo(name='train'    ,    num_examples=1_00							)]
      SCREAMING_SNAKE_CASE_					=    'train[:33%]'
      SCREAMING_SNAKE_CASE_					=    'arrow'
      SCREAMING_SNAKE_CASE_					=    'prefix'

      SCREAMING_SNAKE_CASE_					=    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}
      ]

      SCREAMING_SNAKE_CASE_					=    [SplitInfo(name='train'    ,    num_examples=1_00    ,    shard_lengths=[10] * 10							)]
      SCREAMING_SNAKE_CASE_					=    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       UpperCAmelCase_							(   __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			List[str]							)      ->				Optional[Any]:
      SCREAMING_SNAKE_CASE_					=    [SplitInfo(name='train'    ,    num_examples=1_00							)]
      SCREAMING_SNAKE_CASE_					=    'train'
      SCREAMING_SNAKE_CASE_					=    'arrow'
      SCREAMING_SNAKE_CASE_					=    'prefix_path'
      with pytest.raises(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    make_file_instructions(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase							)