




import argparse

import torch

from diffusers import UNetaDConditionModel





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase=None							,    __lowerCamelCase=None							,    __lowerCamelCase=None					)   -> List[str]:
	assert isinstance(__A							,    __A					), "Paths should be a list of dicts containing 'old' and 'new' keys."

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

			a		       =							(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

			a		       =							old_tensor.shape[0] // config["""num_head_channels"""] // 3

			a		       =							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
			a       ,				a       ,				a		       =							old_tensor.split(channels // num_heads							,    dim=1					)

			a		       =							query.reshape(__A					)
			a		       =							key.reshape(__A					)
			a		       =							value.reshape(__A					)

	for path in paths:
		a		       =							path["""new"""]

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

		if additional_replacements is not None:
			for replacement in additional_replacements:
				a		       =							new_path.replace(replacement["""old"""]							,    replacement["""new"""]					)

				    # proj_attn.weight has to be converted from conv 1D to linear
		a		       =							old_checkpoint[path["""old"""]]
		a		       =							["""proj_attn.weight"""]
		a		       =							["""proj_out.weight""", """proj_in.weight"""]
		if any(k in new_path for k in names					):
			a		       =							weight[:, :, 0]
		elif any(k in new_path for k in names_a					) and len(weight.shape					) > 2 and ".attentions." not in new_path:
			a		       =							weight[:, :, 0]
		else:
			a		       =							weight





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=0					)   -> Optional[int]:
	a		       =							[]
	for old_item in old_list:
		a		       =							old_item

		#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
		#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

		#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
		#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

		#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

	return mapping





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=1					)   -> Tuple:



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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=0					)   -> Optional[Any]:
	a		       =							[]
	for old_item in old_list:
		mapping.append({"""old""": old_item, """new""": old_item}					)

	return mapping





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=0					)   -> List[str]:
	a		       =							[]
	for old_item in old_list:
		a		       =							old_item.replace("""in_layers.0"""							,    """norm1"""					)
		a		       =							new_item.replace("""in_layers.2"""							,    """conv1"""					)

		a		       =							new_item.replace("""out_layers.0"""							,    """norm2"""					)
		a		       =							new_item.replace("""out_layers.3"""							,    """conv2"""					)

		a		       =							new_item.replace("""emb_layers.1"""							,    """time_emb_proj"""					)
		a		       =							new_item.replace("""skip_connection"""							,    """conv_shortcut"""					)

		a		       =							shave_segments(__A							,    n_shave_prefix_segments=__A					)

		if "temopral_conv" not in old_item:
			mapping.append({"""old""": old_item, """new""": new_item}					)

	return mapping





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase=None							,    __lowerCamelCase=False					)   -> str:
	a		       =							{}
	a		       =							list(checkpoint.keys()					)

	a		       =							"""model.diffusion_model."""

	# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
	if sum(k.startswith("""model_ema"""					) for k in keys					) > 100 and extract_ema:
		print(f'Checkpoint {path} has both EMA and non-EMA weights.'					)
		print(
		    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
		    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""					)
		for key in keys:
			if key.startswith("""model.diffusion_model"""					):
				a		       =							"""model_ema.""" + """""".join(key.split("""."""					)[1:]					)
				a		       =							checkpoint.pop(__A					)
	else:
		if sum(k.startswith("""model_ema"""					) for k in keys					) > 100:
			print(
			    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
			    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""					)

		for key in keys:
			a		       =							checkpoint.pop(__A					)

	a		       =							{}

	a		       =							unet_state_dict["""time_embed.0.weight"""]
	a		       =							unet_state_dict["""time_embed.0.bias"""]
	a		       =							unet_state_dict["""time_embed.2.weight"""]
	a		       =							unet_state_dict["""time_embed.2.bias"""]

	if config["class_embed_type"] is None:
		# No parameters to port
		...
	elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
		a		       =							unet_state_dict["""label_emb.0.0.weight"""]
		a		       =							unet_state_dict["""label_emb.0.0.bias"""]
		a		       =							unet_state_dict["""label_emb.0.2.weight"""]
		a		       =							unet_state_dict["""label_emb.0.2.bias"""]
	else:
		raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'					)

	a		       =							unet_state_dict["""input_blocks.0.0.weight"""]
	a		       =							unet_state_dict["""input_blocks.0.0.bias"""]

	a		       =							[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""					)]
	a		       =							renew_attention_paths(__A					)
	a		       =							{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
	assign_to_checkpoint(__A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

	a		       =							unet_state_dict["""out.0.weight"""]
	a		       =							unet_state_dict["""out.0.bias"""]
	a		       =							unet_state_dict["""out.2.weight"""]
	a		       =							unet_state_dict["""out.2.bias"""]

	# Retrieves the keys for the input blocks only
	a		       =							len({""".""".join(layer.split("""."""					)[:2]					) for layer in unet_state_dict if """input_blocks""" in layer}					)
	a		       =							{
	    layer_id: [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]
	    for layer_id in range(__A					)
	}

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

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

	for i in range(1							,    __A					):
		a		       =							(i - 1) // (config["""layers_per_block"""] + 1)
		a		       =							(i - 1) % (config["""layers_per_block"""] + 1)

		a		       =							[
		    key for key in input_blocks[i] if f'input_blocks.{i}.0' in key and f'input_blocks.{i}.0.op' not in key
		]
		a		       =							[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
		a		       =							[key for key in input_blocks[i] if f'input_blocks.{i}.2' in key]

		if f'input_blocks.{i}.op.weight' in unet_state_dict:
			a		       =							unet_state_dict.pop(
			    f'input_blocks.{i}.op.weight'					)
			a		       =							unet_state_dict.pop(
			    f'input_blocks.{i}.op.bias'					)

		a		       =							renew_resnet_paths(__A					)
		a		       =							{"""old""": f'input_blocks.{i}.0', """new""": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
		assign_to_checkpoint(
		    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

		a		       =							[key for key in resnets if """temopral_conv""" in key]
		a		       =							renew_temp_conv_paths(__A					)
		a		       =							{
		    """old""": f'input_blocks.{i}.0.temopral_conv',
		    """new""": f'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
		}
		assign_to_checkpoint(
		    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

		if len(__A					):
			a		       =							renew_attention_paths(__A					)
			a		       =							{"""old""": f'input_blocks.{i}.1', """new""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
			assign_to_checkpoint(
			    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

		if len(__A					):
			a		       =							renew_attention_paths(__A					)
			a		       =							{
			    """old""": f'input_blocks.{i}.2',
			    """new""": f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
			}
			assign_to_checkpoint(
			    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

	a		       =							middle_blocks[0]
	a		       =							[key for key in resnet_a if """temopral_conv""" in key]
	a		       =							middle_blocks[1]
	a		       =							middle_blocks[2]
	a		       =							middle_blocks[3]
	a		       =							[key for key in resnet_a if """temopral_conv""" in key]

	a		       =							renew_resnet_paths(__A					)
	a		       =							{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
	assign_to_checkpoint(
	    __A							,    __A							,    __A							,    config=__A							,    additional_replacements=[meta_path]					)

	a		       =							renew_temp_conv_paths(__A					)
	a		       =							{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
	assign_to_checkpoint(
	    __A							,    __A							,    __A							,    config=__A							,    additional_replacements=[meta_path]					)

	a		       =							renew_resnet_paths(__A					)
	a		       =							{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
	assign_to_checkpoint(
	    __A							,    __A							,    __A							,    config=__A							,    additional_replacements=[meta_path]					)

	a		       =							renew_temp_conv_paths(__A					)
	a		       =							{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
	assign_to_checkpoint(
	    __A							,    __A							,    __A							,    config=__A							,    additional_replacements=[meta_path]					)

	a		       =							renew_attention_paths(__A					)
	a		       =							{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
	assign_to_checkpoint(
	    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

	a		       =							renew_attention_paths(__A					)
	a		       =							{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
	assign_to_checkpoint(
	    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

	for i in range(__A					):
		a		       =							i // (config["""layers_per_block"""] + 1)
		a		       =							i % (config["""layers_per_block"""] + 1)
		a		       =							[shave_segments(__A							,    2					) for name in output_blocks[i]]
		a		       =							{}

		for layer in output_block_layers:
			a       ,				a		       =							layer.split("""."""					)[0], shave_segments(__A							,    1					)
			if layer_id in output_block_list:
				output_block_list[layer_id].append(__A					)
			else:
				a		       =							[layer_name]

		if len(__A					) > 1:
			a		       =							[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
			a		       =							[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
			a		       =							[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

			a		       =							renew_resnet_paths(__A					)
			a		       =							renew_resnet_paths(__A					)

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

			a		       =							[key for key in resnets if """temopral_conv""" in key]
			a		       =							renew_temp_conv_paths(__A					)
			a		       =							{
			    """old""": f'output_blocks.{i}.0.temopral_conv',
			    """new""": f'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
			}
			assign_to_checkpoint(
			    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

			a		       =							{k: sorted(__A					) for k, v in output_block_list.items()}
			if ["conv.bias", "conv.weight"] in output_block_list.values():
				a		       =							list(output_block_list.values()					).index(["""conv.bias""", """conv.weight"""]					)
				a		       =							unet_state_dict[
				    f'output_blocks.{i}.{index}.conv.weight'
				]
				a		       =							unet_state_dict[
				    f'output_blocks.{i}.{index}.conv.bias'
				]

				# Clear attentions as they have been attributed above.
				if len(__A					) == 2:
					a		       =							[]

			if len(__A					):
				a		       =							renew_attention_paths(__A					)
				a		       =							{
				    """old""": f'output_blocks.{i}.1',
				    """new""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
				}
				assign_to_checkpoint(
				    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)

			if len(__A					):
				a		       =							renew_attention_paths(__A					)
				a		       =							{
				    """old""": f'output_blocks.{i}.2',
				    """new""": f'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
				}
				assign_to_checkpoint(
				    __A							,    __A							,    __A							,    additional_replacements=[meta_path]							,    config=__A					)
		else:
			a		       =							renew_resnet_paths(__A							,    n_shave_prefix_segments=1					)
			for path in resnet_0_paths:
				a		       =							""".""".join(["""output_blocks""", str(__A					), path["""old"""]]					)
				a		       =							""".""".join(["""up_blocks""", str(__A					), """resnets""", str(__A					), path["""new"""]]					)
				a		       =							unet_state_dict[old_path]

			a		       =							[l for l in output_block_layers if """temopral_conv""" in l]
			for path in temopral_conv_paths:
				a		       =							path.split("""temopral_conv."""					)[-1]
				a		       =							""".""".join(["""output_blocks""", str(__A					), str(__A					), """temopral_conv""", pruned_path]					)
				a		       =							""".""".join(["""up_blocks""", str(__A					), """temp_convs""", str(__A					), pruned_path]					)
				a		       =							unet_state_dict[old_path]

	return new_checkpoint


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

		parser.add_argument(
		    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
		)
		parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
		__UpperCamelCase      : List[Any]					      =   parser.parse_args()

		__UpperCamelCase      : int					      =   torch.load(args.checkpoint_path, map_location="cpu")
		__UpperCamelCase      : int					      =   UNetaDConditionModel()

		__UpperCamelCase      : List[str]					      =   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

		__UpperCamelCase      : List[Any]					      =   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
		__UpperCamelCase      : List[str]					      =   set(converted_ckpt.keys()) - set(unet.state_dict().keys())

		assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

		# load state_dict
		unet.load_state_dict(converted_ckpt)

		unet.save_pretrained(args.dump_path)

		# -- finish converting the unet --








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			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Dict	,   __magic_name__		:Union[str, Any]=False       ):







								'''simple docstring'''







								a       ,				a       ,				a		       =							(
								    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(__magic_name__       )	,   """wb"""       ).close()
								a		       =							pa.Table.from_pydict({"""filename""": [Path(__magic_name__       ).name] * 100}       )
								if take == -1:
									a		       =							len(__magic_name__       ) - skip
								if skip is not None and take is not None:
									a		       =							pa_table.slice(__magic_name__	,   __magic_name__       )
								return pa_table







class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							[train_info, test_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

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

									a		       =							["""train""", """test[:33%]"""]
									a		       =							[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
									for instructions in [instructionsa, instructionsa]:
										a		       =							[reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       ) for instr in instructions]
										a       ,				a		       =							(Dataset(**__magic_name__       ) 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	,   __magic_name__       )
										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	,   __magic_name__       )
										self.assertEqual(str(test_dset.split       )	,   """test[:33%]"""       )
										del train_dset, test_dset
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=1000	,   shard_lengths=[100] * 10       )
								a		       =							[train_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""train[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									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		:List[str]       ):







								'''simple docstring'''







								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							[
									    {"""filename""": os.path.join(__magic_name__	,   """train"""       )},
									    {"""filename""": os.path.join(__magic_name__	,   """test"""       ), """skip""": 10, """take""": 10},
									]
									a		       =							Dataset(**reader.read_files(__magic_name__	,   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 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							ArrowReader.read_table(__lowerCamelCase							,    in_memory=__lowerCamelCase					)
	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 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							arrow_file
	a		       =							ArrowReader(""""""							,    __lowerCamelCase					)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							reader.read_files([{"""filename""": filename}]							,    in_memory=__lowerCamelCase					)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	a		       =							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 	__A	(			)   -> int:
	assert ReadInstruction("""train"""							,    to=10							,    unit="""abs"""					).to_spec() == "train[:10]"
	assert ReadInstruction("""train"""							,    from_=-80							,    to=10							,    unit="""%"""					).to_spec() == "train[-80%:10%]"

	a		       =							"""train+test"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_test

	a		       =							"""train[2:10]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_abs

	a		       =							"""train[15%:-20%]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct

	a		       =							"""train[:10%](closest)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == "train[:10%]"

	a		       =							"""train[:10%](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct_rounding

	a		       =							"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_test_pct_rounding





def 	__A	(			)   -> Any:
	a		       =							"""dummy"""
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train[:33%]"""
	a		       =							"""arrow"""
	a		       =							"""prefix"""

	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train.arrow'					), "skip": 0, "take": 33}
	]

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





@pytest.mark.parametrize("""name, expected_exception"""							,    [(None, TypeError), ("""""", ValueError)]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> int:
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train"""
	a		       =							"""arrow"""
	a		       =							"""prefix_path"""
	with pytest.raises(__lowerCamelCase					):
		a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)

