

"""simple docstring"""






import argparse
import os
import re


snake_case_        =       '''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
snake_case_        =       re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
snake_case_        =       re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Tuple = False	):




					'''simple docstring'''






					with open(_UpperCAmelCase							,      "r"							,      encoding="utf-8"	) as f:
										UpperCAmelCase			:   Any      =				f.read()

					UpperCAmelCase			:   str      =				content.split("\n"	)
					UpperCAmelCase			:   Dict      =				[]
					UpperCAmelCase			:   List[Any]      =				0
					while line_idx < len(_UpperCAmelCase	):
										if _re_intro_mapping.search(lines[line_idx]	) is not None:
															UpperCAmelCase			:   Optional[Any]      =				len(re.search(R"^(\s*)\S"							,      lines[line_idx]	).groups()[0]	) + 8
															# Start of a new mapping!
															while not lines[line_idx].startswith(" " * indent + "("	):
																				new_lines.append(lines[line_idx]	)
																				line_idx += 1

															UpperCAmelCase			:   Any      =				[]
															while lines[line_idx].strip() != "]":
																				# Blocks either fit in one line or not
																				if lines[line_idx].strip() == "(":
																									UpperCAmelCase			:   str      =				line_idx
																									while not lines[line_idx].startswith(" " * indent + ")"	):
																														line_idx += 1
																									blocks.append("\n".join(lines[start_idx : line_idx + 1]	)	)
																				else:
																									blocks.append(lines[line_idx]	)
																				line_idx += 1

															# Sort blocks by their identifiers
															UpperCAmelCase			:   Any      =				sorted(_UpperCAmelCase							,      key=lambda _lowercase	: _re_identifier.search(_UpperCAmelCase	).groups()[0]	)
															new_lines += blocks
										else:
															new_lines.append(lines[line_idx]	)
															line_idx += 1

					if overwrite:
										with open(_UpperCAmelCase							,      "w"							,      encoding="utf-8"	) as f:
															f.write("\n".join(_UpperCAmelCase	)	)
					elif "\n".join(_UpperCAmelCase	) != content:
										return True



def   lowercase_	(    _lowercase       :		List[str] = False	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				[os.path.join(_UpperCAmelCase							,      _UpperCAmelCase	) for f in os.listdir(_UpperCAmelCase	) if f.endswith(".py"	)]
					UpperCAmelCase			:   int      =				[sort_auto_mapping(_UpperCAmelCase							,      overwrite=_UpperCAmelCase	) for fname in fnames]

					if not overwrite and any(_UpperCAmelCase	):
										UpperCAmelCase			:   Dict      =				[f for f, d in zip(_UpperCAmelCase							,      _UpperCAmelCase	) if d]
										raise ValueError(
										    F"""The following files have auto mappings that need sorting: {', '.join(_UpperCAmelCase	)}. Run `make style` to fix"""
										    " this."	)


if __name__ == "__main__":
	snake_case_        =       argparse.ArgumentParser()
	parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
	snake_case_        =       parser.parse_args()

	sort_all_auto_mappings(not args.check_only)





"""simple docstring"""






import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def   lowercase_	(    _lowercase       :		Optional[int]							,      _lowercase       :		int	):




					'''simple docstring'''






					assert isinstance(_lowercase							,      _lowercase	)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"							,      [False, True]	)
def   lowercase_	(    _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				tmp_path / "cache"
					UpperCAmelCase			:   str      =				{"text": "string"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCAmelCase			:   Tuple      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase							,      keep_in_memory=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



@pytest.mark.parametrize(
    "features"							,      [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,      )
def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				tmp_path / "cache"
					UpperCAmelCase			:   Any      =				{"text": "string"}
					UpperCAmelCase			:   List[str]      =				features.copy() if features else default_expected_features
					UpperCAmelCase			:   Union[str, Any]      =				(
					    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					UpperCAmelCase			:   str      =				TextDatasetReader(_lowercase							,      features=_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



@pytest.mark.parametrize("split"							,      [None, NamedSplit("train"	), "train", "test"]	)
def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Tuple							,      _lowercase       :		List[str]	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				tmp_path / "cache"
					UpperCAmelCase			:   Any      =				{"text": "string"}
					UpperCAmelCase			:   List[Any]      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase							,      split=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)
					assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"							,      [str, list]	)
def   lowercase_	(    _lowercase       :		Optional[int]							,      _lowercase       :		Any							,      _lowercase       :		Union[str, Any]	):




					'''simple docstring'''






					if issubclass(_lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				text_path
					elif issubclass(_lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[Any]      =				[text_path]
					UpperCAmelCase			:   Union[str, Any]      =				tmp_path / "cache"
					UpperCAmelCase			:   List[Any]      =				{"text": "string"}
					UpperCAmelCase			:   Tuple      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_dataset(_lowercase							,      _lowercase	)



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]=("train",)	):




					'''simple docstring'''






					assert isinstance(_lowercase							,      _lowercase	)
					for split in splits:
										UpperCAmelCase			:   str      =				dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"							,      [False, True]	)
def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		str							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				tmp_path / "cache"
					UpperCAmelCase			:   Union[str, Any]      =				{"text": "string"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										UpperCAmelCase			:   Tuple      =				TextDatasetReader({"train": text_path}							,      cache_dir=_lowercase							,      keep_in_memory=_lowercase	).read()
					_check_text_datasetdict(_lowercase							,      _lowercase	)



@pytest.mark.parametrize(
    "features"							,      [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]							,      )
def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				tmp_path / "cache"
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					UpperCAmelCase			:   List[str]      =				{"text": "string"}
					UpperCAmelCase			:   str      =				features.copy() if features else default_expected_features
					UpperCAmelCase			:   Optional[int]      =				(
					    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					UpperCAmelCase			:   Optional[int]      =				TextDatasetReader({"train": text_path}							,      features=_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_datasetdict(_lowercase							,      _lowercase	)




@pytest.mark.parametrize("split"							,      [None, NamedSplit("train"	), "train", "test"]	)
def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Tuple							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					if split:
										UpperCAmelCase			:   Optional[Any]      =				{split: text_path}
					else:
										UpperCAmelCase			:   str      =				"train"
										UpperCAmelCase			:   Union[str, Any]      =				{"train": text_path, "test": text_path}
					UpperCAmelCase			:   Any      =				tmp_path / "cache"
					UpperCAmelCase			:   List[Any]      =				{"text": "string"}
					UpperCAmelCase			:   Any      =				TextDatasetReader(_lowercase							,      cache_dir=_lowercase	).read()
					_check_text_datasetdict(_lowercase							,      _lowercase							,      splits=list(path.keys()	)	)
					assert all(dataset[split].split == split for split in path.keys()	)


