




# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig




def 							_UpperCAmelCase     (UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Any					,			UpperCamelCase_       :       List[Any]=0				):
				'''simple docstring'''
				# Format the message.
				if name is None:
								_lowerCAmelCase			:			Union[str, Any]        =					None
				else:
								_lowerCAmelCase			:			str        =					""".""" * max(0					,			spaces - 2				) + """# {:""" + str(50 - spaces				) + """s}"""
								_lowerCAmelCase			:			Optional[int]        =					fmt.format(UpperCamelCase_				)

				# Print and recurse (if needed).
				if isinstance(UpperCamelCase_					,			UpperCamelCase_				):
								if msg is not None:
												print(UpperCamelCase_				)
								for k in val.keys():
												recursive_print(UpperCamelCase_					,			val[k]					,			spaces + 2				)
				elif isinstance(UpperCamelCase_					,			torch.Tensor				):
								print(UpperCamelCase_					,			""":"""					,			val.size()				)
				else:
								print(UpperCamelCase_					,			""":"""					,			UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]					,			UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       Any					,			UpperCamelCase_       :       Tuple				):
				'''simple docstring'''
				# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
				# for compatibility with later versions of NVIDIA Megatron-LM.
				# The inverse operation is performed inside Megatron-LM to read checkpoints:
				# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
				# If param is the weight tensor of the self-attention block, the returned tensor
				# will have to be transposed one more time to be read by HuggingFace BERT.
				_lowerCAmelCase			:			Optional[int]        =					param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								_lowerCAmelCase			:			Dict        =					(num_heads, hidden_size, num_splits) + input_shape[1:]
								_lowerCAmelCase			:			Optional[Any]        =					param.view(*UpperCamelCase_				)
								_lowerCAmelCase			:			Union[str, Any]        =					param.transpose(0					,			2				)
								_lowerCAmelCase			:			Tuple        =					param.transpose(1					,			2				).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								_lowerCAmelCase			:			Dict        =					(num_heads, num_splits, hidden_size) + input_shape[1:]
								_lowerCAmelCase			:			List[Any]        =					param.view(*UpperCamelCase_				)
								_lowerCAmelCase			:			List[Any]        =					param.transpose(0					,			1				).contiguous()
				_lowerCAmelCase			:			Optional[Any]        =					param.view(*UpperCamelCase_				)
				return param




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Any					,			UpperCamelCase_       :       Any				):
				'''simple docstring'''
				# The converted output model.
				_lowerCAmelCase			:			Union[str, Any]        =					{}

				# old versions did not store training args
				_lowerCAmelCase			:			int        =					input_state_dict.get("""args"""					,			UpperCamelCase_				)
				if ds_args is not None:
								# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
								# from pprint import pprint
								# pprint(vars(ds_args))

								_lowerCAmelCase			:			Union[str, Any]        =					ds_args.tokenizer_type
								_lowerCAmelCase			:			str        =					ds_args.padded_vocab_size
								_lowerCAmelCase			:			Any        =					ds_args.max_position_embeddings
								_lowerCAmelCase			:			str        =					ds_args.hidden_size
								_lowerCAmelCase			:			Optional[Any]        =					ds_args.num_layers
								_lowerCAmelCase			:			Optional[Any]        =					ds_args.num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					ds_args.ffn_hidden_size if """ffn_hidden_size""" in ds_args else 4 * ds_args.hidden_size
								# pprint(config)

				# The number of heads.
				_lowerCAmelCase			:			List[str]        =					config.num_attention_heads
				# The hidden_size per head.
				_lowerCAmelCase			:			Any        =					config.hidden_size // heads
				# Megatron-LM checkpoint version
				if "checkpoint_version" in input_state_dict.keys():
								_lowerCAmelCase			:			Tuple        =					input_state_dict["""checkpoint_version"""]
				else:
								_lowerCAmelCase			:			str        =					0.0

				# The model.
				_lowerCAmelCase			:			List[str]        =					input_state_dict["""model"""]
				# The language model.
				_lowerCAmelCase			:			Any        =					model["""language_model"""]
				# The embeddings.
				_lowerCAmelCase			:			Optional[Any]        =					lm["""embedding"""]

				# The word embeddings.
				_lowerCAmelCase			:			Union[str, Any]        =					embeddings["""word_embeddings"""]["""weight"""]
				# Truncate the embedding table to vocab_size rows.
				_lowerCAmelCase			:			str        =					word_embeddings[: config.vocab_size, :]
				# Store the word embeddings.
				_lowerCAmelCase			:			Union[str, Any]        =					word_embeddings

				# The position embeddings.
				_lowerCAmelCase			:			Optional[int]        =					embeddings["""position_embeddings"""]["""weight"""]
				assert pos_embeddings.size(0				) == config.max_position_embeddings and pos_embeddings.size(1				) == config.hidden_size
				# Store the position embeddings.
				_lowerCAmelCase			:			Tuple        =					pos_embeddings

				# The token-type embeddings.
				_lowerCAmelCase			:			Tuple        =					embeddings["""tokentype_embeddings"""]["""weight"""]
				# Store the position embeddings.
				_lowerCAmelCase			:			int        =					tokentype_embeddings

				# The transformer.
				_lowerCAmelCase			:			Optional[Any]        =					lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

				# The regex to extract layer names.
				_lowerCAmelCase			:			Optional[int]        =					re.compile(R"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"""				)

				# The simple map of names for "automated" rules.
				_lowerCAmelCase			:			int        =					{
				    """attention.dense""": """.attention.output.dense.""",
				    """self_attention.dense""": """.attention.output.dense.""",
				    """mlp.dense_h_to_4h""": """.intermediate.dense.""",
				    """mlp.dense_4h_to_h""": """.output.dense.""",
				}

				# Keep track of the attention/query/value tensor.
				_lowerCAmelCase			:			Optional[int]        =					None

				# Extract the layers.
				for key, val in transformer.items():
								# Match the name.
								_lowerCAmelCase			:			List[str]        =					layer_re.match(UpperCamelCase_				)

								# Stop if that's not a layer
								if m is None:
												break

								# The index of the layer.
								_lowerCAmelCase			:			Any        =					int(m.group(1				)				)
								# The name of the operation.
								_lowerCAmelCase			:			Union[str, Any]        =					m.group(2				)
								# Is it a weight or a bias?
								_lowerCAmelCase			:			Optional[int]        =					m.group(3				)

								# The name of the layer.
								_lowerCAmelCase			:			List[Any]        =					F"bert.encoder.layer.{layer_idx}"

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith("""layernorm"""				):
												_lowerCAmelCase			:			Optional[int]        =					"""attention.ln""" if op_name.startswith("""input"""				) else """ln"""
												_lowerCAmelCase			:			Any        =					val

								# Transpose the QKV matrix.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "weight":
												# Make sure the QKV pointer is nil.
												assert attention_qkv_weight is None, ""

												_lowerCAmelCase			:			int        =					fix_query_key_value_ordering(UpperCamelCase_					,			UpperCamelCase_					,			3					,			UpperCamelCase_					,			UpperCamelCase_				)
												# Store the tensor as we need the bias as well to interleave QKV and biases.
												_lowerCAmelCase			:			List[Any]        =					out_val

								# Transpose the bias.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "bias":
												# Make sure we read the weight tensor.
												assert attention_qkv_weight is not None, ""

												# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
												_lowerCAmelCase			:			Union[str, Any]        =					attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
												_lowerCAmelCase			:			Union[str, Any]        =					attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
												_lowerCAmelCase			:			List[Any]        =					attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

												_lowerCAmelCase			:			Union[str, Any]        =					fix_query_key_value_ordering(UpperCamelCase_					,			UpperCamelCase_					,			3					,			UpperCamelCase_					,			UpperCamelCase_				)
												# Split the bias.
												_lowerCAmelCase			:			List[str]        =					out_val[0 * config.hidden_size : 1 * config.hidden_size]
												_lowerCAmelCase			:			Optional[Any]        =					out_val[1 * config.hidden_size : 2 * config.hidden_size]
												_lowerCAmelCase			:			str        =					out_val[2 * config.hidden_size : 3 * config.hidden_size]

												# Store.
												_lowerCAmelCase			:			str        =					q
												_lowerCAmelCase			:			int        =					q_bias
												_lowerCAmelCase			:			str        =					k
												_lowerCAmelCase			:			Dict        =					k_bias
												_lowerCAmelCase			:			int        =					v
												_lowerCAmelCase			:			Any        =					v_bias

												# Clear the stored tensor.
												_lowerCAmelCase			:			List[str]        =					None

								# Copy weights and biases as is.
								elif weight_or_bias in ["weight", "bias"]:
												_lowerCAmelCase			:			Any        =					megatron_to_transformers[op_name]
												_lowerCAmelCase			:			Any        =					val

    # The final layernorm.
				_lowerCAmelCase			:			int        =					transformer["""final_layernorm.weight"""]
				_lowerCAmelCase			:			Optional[Any]        =					transformer["""final_layernorm.bias"""]

				# The pooler.
				_lowerCAmelCase			:			str        =					lm["""pooler"""]

				# Store the matrix and the bias.
				_lowerCAmelCase			:			Optional[Any]        =					pooler["""dense.weight"""]
				_lowerCAmelCase			:			Optional[Any]        =					pooler["""dense.bias"""]

				# The LM head from Megatron (for RACE).
				_lowerCAmelCase			:			Any        =					model["""lm_head"""]

				# The transform matrix.
				_lowerCAmelCase			:			Dict        =					lm_head["""dense.weight"""]
				_lowerCAmelCase			:			int        =					lm_head["""dense.bias"""]

				# The transform LN.
				_lowerCAmelCase			:			List[Any]        =					lm_head["""layernorm.weight"""]
				_lowerCAmelCase			:			Optional[int]        =					lm_head["""layernorm.bias"""]

				# For the decoder, we replicate the weights.
				_lowerCAmelCase			:			Dict        =					word_embeddings
				_lowerCAmelCase			:			Union[str, Any]        =					lm_head["""bias"""]

				# The classifier from Megatron (for MLNI).
				_lowerCAmelCase			:			int        =					model["""binary_head"""]

				# Store the classifier.
				_lowerCAmelCase			:			Tuple        =					binary_head["""weight"""]
				_lowerCAmelCase			:			str        =					binary_head["""bias"""]

				# It should be done!
				return output_state_dict




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				# Create the argument parser.
				_lowerCAmelCase			:			List[str]        =					argparse.ArgumentParser()
				parser.add_argument("""--print-checkpoint-structure"""					,			action="""store_true"""				)
				parser.add_argument("""path_to_checkpoint"""					,			type=UpperCamelCase_					,			help="""Path to the ZIP file containing the checkpoint"""				)
				parser.add_argument(
				    """--config_file"""					,			default=""""""					,			type=UpperCamelCase_					,			help="""An optional config json file describing the pre-trained model."""					,			)
				_lowerCAmelCase			:			List[Any]        =					parser.parse_args()

				# Extract the basename.
				_lowerCAmelCase			:			List[Any]        =					os.path.dirname(args.path_to_checkpoint				)

				# Load the model.
				# the .zip is very optional, let's keep it for backward compatibility
				print(F"Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""				)
				if args.path_to_checkpoint.endswith(""".zip"""				):
								with zipfile.ZipFile(args.path_to_checkpoint					,			"""r"""				) as checkpoint:
												with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt"""				) as pytorch_dict:
																_lowerCAmelCase			:			Tuple        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				else:
								_lowerCAmelCase			:			int        =					torch.load(args.path_to_checkpoint					,			map_location="""cpu"""				)

				if args.config_file == "":
								# Default config of megatron-bert 345m
								_lowerCAmelCase			:			List[Any]        =					MegatronBertConfig()

								# different megatron-bert-*-345m models have different vocab sizes, so override the default
								# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
								_lowerCAmelCase			:			Optional[Any]        =					input_state_dict["""model"""]["""lm_head"""]["""bias"""].numel()
				else:
								_lowerCAmelCase			:			List[str]        =					MegatronBertConfig.from_json_file(args.config_file				)

				# Convert.
				print("""Converting"""				)
				_lowerCAmelCase			:			str        =					convert_megatron_checkpoint(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)

				# Print the structure of converted state dict.
				if args.print_checkpoint_structure:
								recursive_print(UpperCamelCase_					,			UpperCamelCase_				)

				# Store the config to file.
				print("""Saving config"""				)
				config.save_pretrained(UpperCamelCase_				)

				# Store the state_dict to file.
				_lowerCAmelCase			:			Dict        =					os.path.join(UpperCamelCase_					,			"""pytorch_model.bin"""				)
				print(F"Saving checkpoint to \"{output_checkpoint_file}\""				)
				torch.save(UpperCamelCase_					,			UpperCamelCase_				)


####################################################################################################

if __name__ == "__main__":
	main()

####################################################################################################









"""simple docstring"""


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 			snake_case  (A_      :Dict	,			A_      :Tuple      ):







  '''simple docstring'''

  assert isinstance(A_	,			A_      )
  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 			snake_case  (A_      :str	,			A_      :List[Any]	,			A_      :Optional[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			str				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a     :			Union[str, Any]				=   CsvDatasetReader(A_	,			cache_dir=A_	,			keep_in_memory=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




@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 			snake_case  (A_      :List[str]	,			A_      :Union[str, Any]	,			A_      :Union[str, Any]      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a     :			Optional[int]				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Dict				=   features.copy() if features else default_expected_features
  a     :			Dict				=   (
      Features({feature: Value(A_      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  a     :			Tuple				=   CsvDatasetReader(A_	,			features=A_	,			cache_dir=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			snake_case  (A_      :List[str]	,			A_      :str	,			A_      :int      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[str]				=   CsvDatasetReader(A_	,			cache_dir=A_	,			split=A_      ).read()
  _check_csv_dataset(A_	,			A_      )
  assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'	,			[str, list]      )
def 			snake_case  (A_      :int	,			A_      :List[Any]	,			A_      :str      ):







  '''simple docstring'''

  if issubclass(A_	,			A_      ):
    a     :			Dict				=   csv_path
  elif issubclass(A_	,			A_      ):
    a     :			Optional[Any]				=   [csv_path]
  a     :			Tuple				=   tmp_path / 'cache'
  a     :			str				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Any				=   CsvDatasetReader(A_	,			cache_dir=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




def 			snake_case  (A_      :List[str]	,			A_      :Dict	,			A_      :Optional[Any]=("train",)      ):







  '''simple docstring'''

  assert isinstance(A_	,			A_      )
  for split in splits:
    a     :			List[Any]				=   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 			snake_case  (A_      :str	,			A_      :Any	,			A_      :List[str]      ):







  '''simple docstring'''

  a     :			Any				=   tmp_path / 'cache'
  a     :			int				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a     :			str				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_	,			keep_in_memory=A_      ).read()
  _check_csv_datasetdict(A_	,			A_      )




@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 			snake_case  (A_      :Optional[int]	,			A_      :List[Any]	,			A_      :Tuple      ):







  '''simple docstring'''

  a     :			Dict				=   tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a     :			Any				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Tuple				=   features.copy() if features else default_expected_features
  a     :			Optional[int]				=   (
      Features({feature: Value(A_      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  a     :			Any				=   CsvDatasetReader({'train': csv_path}	,			features=A_	,			cache_dir=A_      ).read()
  _check_csv_datasetdict(A_	,			A_      )




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			snake_case  (A_      :List[str]	,			A_      :Optional[Any]	,			A_      :str      ):







  '''simple docstring'''

  if split:
    a     :			List[Any]				=   {split: csv_path}
  else:
    a     :			str				=   {'train': csv_path, 'test': csv_path}
  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[str]				=   CsvDatasetReader(A_	,			cache_dir=A_      ).read()
  _check_csv_datasetdict(A_	,			A_	,			splits=list(path.keys()      )      )
  assert all(dataset[split].split == split for split in path.keys()      )




def 			snake_case  (A_      :Dict      ):







  '''simple docstring'''

  with open(A_	,			encoding='utf-8'      ) as csvfile:
    yield from csv.reader(A_      )




def 			snake_case  (A_      :int	,			A_      :Optional[int]      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			List[str]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=1      ).write()

  a     :			int				=   iter_csv_file(A_      )
  a     :			Optional[Any]				=   iter_csv_file(A_      )

  for rowa, rowa in zip(A_	,			A_      ):
    assert rowa == rowa




def 			snake_case  (A_      :Tuple	,			A_      :Tuple      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			List[Any]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=2      ).write()

  a     :			Dict				=   iter_csv_file(A_      )
  a     :			List[Any]				=   iter_csv_file(A_      )

  for rowa, rowa in zip(A_	,			A_      ):
    assert rowa == rowa






def 			snake_case  (A_      :Union[str, Any]	,			A_      :List[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			Dict				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  with pytest.raises(A_      ):
    CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=0      )




