# Buggy code
def read(cls, filepath_or_buffer, **kwargs):
    if isinstance(filepath_or_buffer, str):
        if not cls.file_exists(filepath_or_buffer):
            return cls.single_worker_read(filepath_or_buffer, **kwargs)
        filepath_or_buffer = cls.get_path(filepath_or_buffer)
    elif not pathlib_or_pypath(filepath_or_buffer):
        return cls.single_worker_read(filepath_or_buffer, **kwargs)
    compression_type = cls.infer_compression(
        filepath_or_buffer, kwargs.get("compression")
    )
    if compression_type is not None:
        if (
            compression_type == "gzip"
            or compression_type == "bz2"
            or compression_type == "xz"
        ):
            kwargs["compression"] = compression_type
        elif (
            compression_type == "zip"
            and sys.version_info[0] == 3
            and sys.version_info[1] >= 7
        ):
            # need python3.7 to .seek and .tell ZipExtFile
            kwargs["compression"] = compression_type
        else:
            return cls.single_worker_read(filepath_or_buffer, **kwargs)

    chunksize = kwargs.get("chunksize")
    if chunksize is not None:
        return cls.single_worker_read(filepath_or_buffer, **kwargs)

    skiprows = kwargs.get("skiprows")
    if skiprows is not None and not isinstance(skiprows, int):
        return cls.single_worker_read(filepath_or_buffer, **kwargs)
    # TODO: replace this by reading lines from file.
    if kwargs.get("nrows") is not None:
        return cls.single_worker_read(filepath_or_buffer, **kwargs)
    names = kwargs.get("names", None)
    index_col = kwargs.get("index_col", None)
    if names is None:
        # For the sake of the empty df, we assume no `index_col` to get the correct
        # column names before we build the index. Because we pass `names` in, this
        # step has to happen without removing the `index_col` otherwise it will not
        # be assigned correctly
        names = pandas.read_csv(
            filepath_or_buffer, **dict(kwargs, nrows=0, skipfooter=0, index_col=None)
        ).columns
    empty_pd_df = pandas.read_csv(
        filepath_or_buffer, **dict(kwargs, nrows=0, skipfooter=0)
    )
    column_names = empty_pd_df.columns
    skipfooter = kwargs.get("skipfooter", None)
    skiprows = kwargs.pop("skiprows", None)
    usecols = kwargs.get("usecols", None)
    usecols_md = _validate_usecols_arg(usecols)
    if usecols is not None and usecols_md[1] != "integer":
        del kwargs["usecols"]
        all_cols = pandas.read_csv(
            cls.file_open(filepath_or_buffer, "rb"),
            **dict(kwargs, nrows=0, skipfooter=0),
        ).columns
        usecols = all_cols.get_indexer_for(list(usecols_md[0]))
    parse_dates = kwargs.pop("parse_dates", False)
    partition_kwargs = dict(
        kwargs,
        header=None,
        names=names,
        skipfooter=0,
        skiprows=None,
        parse_dates=parse_dates,
        usecols=usecols,
    )
    encoding = kwargs.get("encoding", None)
    quotechar = kwargs.get("quotechar", '"').encode(
        encoding if encoding is not None else "UTF-8"
    )
    with cls.file_open(filepath_or_buffer, "rb", compression_type) as f:
        # Skip the header since we already have the header information and skip the
        # rows we are told to skip.
        if isinstance(skiprows, int) or skiprows is None:
            if skiprows is None:
                skiprows = 0
            header = kwargs.get("header", "infer")
            if header == "infer" and kwargs.get("names", None) is None:
                skiprows += 1
            elif isinstance(header, int):
                skiprows += header + 1
            elif hasattr(header, "__iter__") and not isinstance(header, str):
                skiprows += max(header) + 1
            for _ in range(skiprows):
                f.readline()
        if kwargs.get("encoding", None) is not None:
            partition_kwargs["skiprows"] = 1
        # Launch tasks to read partitions
        partition_ids = []
        index_ids = []
        dtypes_ids = []
        total_bytes = cls.file_size(f)
        # Max number of partitions available
        from modin.pandas import DEFAULT_NPARTITIONS

        num_partitions = DEFAULT_NPARTITIONS
        # This is the number of splits for the columns
        num_splits = min(len(column_names), num_partitions)
        # This is the chunksize each partition will read
        chunk_size = max(1, (total_bytes - f.tell()) // num_partitions)

        # Metadata
        column_chunksize = compute_chunksize(empty_pd_df, num_splits, axis=1)
        if column_chunksize > len(column_names):
            column_widths = [len(column_names)]
            # This prevents us from unnecessarily serializing a bunch of empty
            # objects.
            num_splits = 1
        else:
            column_widths = [
                column_chunksize
                if len(column_names) > (column_chunksize * (i + 1))
                else 0
                if len(column_names) < (column_chunksize * i)
                else len(column_names) - (column_chunksize * i)
                for i in range(num_splits)
            ]

        while f.tell() < total_bytes:
            args = {
                "fname": filepath_or_buffer,
                "num_splits": num_splits,
                **partition_kwargs,
            }
            partition_id = cls.call_deploy(
                f, chunk_size, num_splits + 2, args, quotechar=quotechar
            )
            partition_ids.append(partition_id[:-2])
            index_ids.append(partition_id[-2])
            dtypes_ids.append(partition_id[-1])

    # Compute the index based on a sum of the lengths of each partition (by default)
    # or based on the column(s) that were requested.
    if index_col is None:
        row_lengths = cls.materialize(index_ids)
        new_index = pandas.RangeIndex(sum(row_lengths))
        # pandas has a really weird edge case here.
        if kwargs.get("names", None) is not None and skiprows > 1:
            new_index = pandas.RangeIndex(skiprows - 1, new_index.stop + skiprows - 1)
    else:
        index_objs = cls.materialize(index_ids)
        row_lengths = [len(o) for o in index_objs]
        new_index = index_objs[0].append(index_objs[1:])
        new_index.name = empty_pd_df.index.name

    # Compute dtypes by getting collecting and combining all of the partitions. The
    # reported dtypes from differing rows can be different based on the inference in
    # the limited data seen by each worker. We use pandas to compute the exact dtype
    # over the whole column for each column. The index is set below.
    dtypes = cls.get_dtypes(dtypes_ids)

    partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths)
    # If parse_dates is present, the column names that we have might not be
    # the same length as the returned column names. If we do need to modify
    # the column names, we remove the old names from the column names and
    # insert the new one at the front of the Index.
    if parse_dates is not None:
        # We have to recompute the column widths if `parse_dates` is set because
        # we are not guaranteed to have the correct information regarding how many
        # columns are on each partition.
        column_widths = None
        # Check if is list of lists
        if isinstance(parse_dates, list) and isinstance(parse_dates[0], list):
            for group in parse_dates:
                new_col_name = "_".join(group)
                column_names = column_names.drop(group).insert(0, new_col_name)
        # Check if it is a dictionary
        elif isinstance(parse_dates, dict):
            for new_col_name, group in parse_dates.items():
                column_names = column_names.drop(group).insert(0, new_col_name)
    # Set the index for the dtypes to the column names
    if isinstance(dtypes, pandas.Series):
        dtypes.index = column_names
    else:
        dtypes = pandas.Series(dtypes, index=column_names)
    new_frame = cls.frame_cls(
        partition_ids,
        new_index,
        column_names,
        row_lengths,
        column_widths,
        dtypes=dtypes,
    )
    new_query_compiler = cls.query_compiler_cls(new_frame)

    if skipfooter:
        new_query_compiler = new_query_compiler.drop(
            new_query_compiler.index[-skipfooter:]
        )
    if kwargs.get("squeeze", False) and len(new_query_compiler.columns) == 1:
        return new_query_compiler[new_query_compiler.columns[0]]
    if index_col is None:
        new_query_compiler._modin_frame._apply_index_objs(axis=0)
    return new_query_compiler


# Traceback (ValueError)
Traceback (most recent call last):
File "code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 2, in <module>
File "/modin/pandas/io.py", line 143, in read_json
return DataFrame(query_compiler=BaseFactory.read_json(**kwargs))
File "/modin/data_management/factories.py", line 60, in read_json
return cls._determine_engine()._read_json(**kwargs)
File "/modin/data_management/factories.py", line 64, in _read_json
return cls.io_cls.read_json(**kwargs)
File "/modin/engines/base/io/text/json_reader.py", line 13, in read
return cls.single_worker_read(path_or_buf, **kwargs)
File "/modin/backends/pandas/parsers.py", line 57, in single_worker_read
pandas_frame = cls.parse(fname, **kwargs)
File "/modin/backends/pandas/parsers.py", line 117, in parse
return pandas.read_json(fname, **kwargs)
File "/pandas/util/_decorators.py", line 186, in wrapper
return func(*args, **kwargs)
File "/pandas/io/json/_json.py", line 608, in read_json
result = json_reader.read()
File "/pandas/io/json/_json.py", line 731, in read
obj = self._get_object_parser(self.data)
File "/pandas/io/json/_json.py", line 753, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/pandas/io/json/_json.py", line 857, in parse
self._parse_no_numpy()
File "/pandas/io/json/_json.py", line 1089, in _parse_no_numpy
loads(json, precise_float=self.precise_float), dtype=None
ValueError: Expected object or value

# Fixed code
def read(cls, filepath_or_buffer, **kwargs):
    if isinstance(filepath_or_buffer, str):
        if not cls.file_exists(filepath_or_buffer):
            return cls.single_worker_read(filepath_or_buffer, **kwargs)
        filepath_or_buffer = cls.get_path(filepath_or_buffer)
    elif not cls.pathlib_or_pypath(filepath_or_buffer):
        return cls.single_worker_read(filepath_or_buffer, **kwargs)
    compression_type = cls.infer_compression(
        filepath_or_buffer, kwargs.get("compression")
    )
    if compression_type is not None:
        if (
            compression_type == "gzip"
            or compression_type == "bz2"
            or compression_type == "xz"
        ):
            kwargs["compression"] = compression_type
        elif (
            compression_type == "zip"
            and sys.version_info[0] == 3
            and sys.version_info[1] >= 7
        ):
            # need python3.7 to .seek and .tell ZipExtFile
            kwargs["compression"] = compression_type
        else:
            return cls.single_worker_read(filepath_or_buffer, **kwargs)

    chunksize = kwargs.get("chunksize")
    if chunksize is not None:
        return cls.single_worker_read(filepath_or_buffer, **kwargs)

    skiprows = kwargs.get("skiprows")
    if skiprows is not None and not isinstance(skiprows, int):
        return cls.single_worker_read(filepath_or_buffer, **kwargs)
    # TODO: replace this by reading lines from file.
    if kwargs.get("nrows") is not None:
        return cls.single_worker_read(filepath_or_buffer, **kwargs)
    names = kwargs.get("names", None)
    index_col = kwargs.get("index_col", None)
    if names is None:
        # For the sake of the empty df, we assume no `index_col` to get the correct
        # column names before we build the index. Because we pass `names` in, this
        # step has to happen without removing the `index_col` otherwise it will not
        # be assigned correctly
        names = pandas.read_csv(
            filepath_or_buffer, **dict(kwargs, nrows=0, skipfooter=0, index_col=None)
        ).columns
    empty_pd_df = pandas.read_csv(
        filepath_or_buffer, **dict(kwargs, nrows=0, skipfooter=0)
    )
    column_names = empty_pd_df.columns
    skipfooter = kwargs.get("skipfooter", None)
    skiprows = kwargs.pop("skiprows", None)
    usecols = kwargs.get("usecols", None)
    usecols_md = _validate_usecols_arg(usecols)
    if usecols is not None and usecols_md[1] != "integer":
        del kwargs["usecols"]
        all_cols = pandas.read_csv(
            cls.file_open(filepath_or_buffer, "rb"),
            **dict(kwargs, nrows=0, skipfooter=0),
        ).columns
        usecols = all_cols.get_indexer_for(list(usecols_md[0]))
    parse_dates = kwargs.pop("parse_dates", False)
    partition_kwargs = dict(
        kwargs,
        header=None,
        names=names,
        skipfooter=0,
        skiprows=None,
        parse_dates=parse_dates,
        usecols=usecols,
    )
    encoding = kwargs.get("encoding", None)
    quotechar = kwargs.get("quotechar", '"').encode(
        encoding if encoding is not None else "UTF-8"
    )
    with cls.file_open(filepath_or_buffer, "rb", compression_type) as f:
        # Skip the header since we already have the header information and skip the
        # rows we are told to skip.
        if isinstance(skiprows, int) or skiprows is None:
            if skiprows is None:
                skiprows = 0
            header = kwargs.get("header", "infer")
            if header == "infer" and kwargs.get("names", None) is None:
                skiprows += 1
            elif isinstance(header, int):
                skiprows += header + 1
            elif hasattr(header, "__iter__") and not isinstance(header, str):
                skiprows += max(header) + 1
            for _ in range(skiprows):
                f.readline()
        if kwargs.get("encoding", None) is not None:
            partition_kwargs["skiprows"] = 1
        # Launch tasks to read partitions
        partition_ids = []
        index_ids = []
        dtypes_ids = []
        total_bytes = cls.file_size(f)
        # Max number of partitions available
        from modin.pandas import DEFAULT_NPARTITIONS

        num_partitions = DEFAULT_NPARTITIONS
        # This is the number of splits for the columns
        num_splits = min(len(column_names), num_partitions)
        # This is the chunksize each partition will read
        chunk_size = max(1, (total_bytes - f.tell()) // num_partitions)

        # Metadata
        column_chunksize = compute_chunksize(empty_pd_df, num_splits, axis=1)
        if column_chunksize > len(column_names):
            column_widths = [len(column_names)]
            # This prevents us from unnecessarily serializing a bunch of empty
            # objects.
            num_splits = 1
        else:
            column_widths = [
                column_chunksize
                if len(column_names) > (column_chunksize * (i + 1))
                else 0
                if len(column_names) < (column_chunksize * i)
                else len(column_names) - (column_chunksize * i)
                for i in range(num_splits)
            ]

        while f.tell() < total_bytes:
            args = {
                "fname": filepath_or_buffer,
                "num_splits": num_splits,
                **partition_kwargs,
            }
            partition_id = cls.call_deploy(
                f, chunk_size, num_splits + 2, args, quotechar=quotechar
            )
            partition_ids.append(partition_id[:-2])
            index_ids.append(partition_id[-2])
            dtypes_ids.append(partition_id[-1])

    # Compute the index based on a sum of the lengths of each partition (by default)
    # or based on the column(s) that were requested.
    if index_col is None:
        row_lengths = cls.materialize(index_ids)
        new_index = pandas.RangeIndex(sum(row_lengths))
        # pandas has a really weird edge case here.
        if kwargs.get("names", None) is not None and skiprows > 1:
            new_index = pandas.RangeIndex(skiprows - 1, new_index.stop + skiprows - 1)
    else:
        index_objs = cls.materialize(index_ids)
        row_lengths = [len(o) for o in index_objs]
        new_index = index_objs[0].append(index_objs[1:])
        new_index.name = empty_pd_df.index.name

    # Compute dtypes by getting collecting and combining all of the partitions. The
    # reported dtypes from differing rows can be different based on the inference in
    # the limited data seen by each worker. We use pandas to compute the exact dtype
    # over the whole column for each column. The index is set below.
    dtypes = cls.get_dtypes(dtypes_ids)

    partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths)
    # If parse_dates is present, the column names that we have might not be
    # the same length as the returned column names. If we do need to modify
    # the column names, we remove the old names from the column names and
    # insert the new one at the front of the Index.
    if parse_dates is not None:
        # We have to recompute the column widths if `parse_dates` is set because
        # we are not guaranteed to have the correct information regarding how many
        # columns are on each partition.
        column_widths = None
        # Check if is list of lists
        if isinstance(parse_dates, list) and isinstance(parse_dates[0], list):
            for group in parse_dates:
                new_col_name = "_".join(group)
                column_names = column_names.drop(group).insert(0, new_col_name)
        # Check if it is a dictionary
        elif isinstance(parse_dates, dict):
            for new_col_name, group in parse_dates.items():
                column_names = column_names.drop(group).insert(0, new_col_name)
    # Set the index for the dtypes to the column names
    if isinstance(dtypes, pandas.Series):
        dtypes.index = column_names
    else:
        dtypes = pandas.Series(dtypes, index=column_names)
    new_frame = cls.frame_cls(
        partition_ids,
        new_index,
        column_names,
        row_lengths,
        column_widths,
        dtypes=dtypes,
    )
    new_query_compiler = cls.query_compiler_cls(new_frame)

    if skipfooter:
        new_query_compiler = new_query_compiler.drop(
            new_query_compiler.index[-skipfooter:]
        )
    if kwargs.get("squeeze", False) and len(new_query_compiler.columns) == 1:
        return new_query_compiler[new_query_compiler.columns[0]]
    if index_col is None:
        new_query_compiler._modin_frame._apply_index_objs(axis=0)
    return new_query_compiler