
'''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 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path / "cache"
		_UpperCamelCase						    =	{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path / "cache"
		_UpperCamelCase						    =	{"text": "string"}
		_UpperCamelCase						    =	features.copy() if features else default_expected_features
		_UpperCamelCase						    =	(
		    Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
		)
		_UpperCamelCase						    =	TextDatasetReader(_lowercase						,  features=_lowercase						,  cache_dir=_lowercase ).read()
		_check_text_dataset(_lowercase						,  _lowercase )


@pytest.mark.parametrize("""split"""						,  [None, NamedSplit("""train""" ), """train""", """test"""] )
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path / "cache"
		_UpperCamelCase						    =	{"text": "string"}
		_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






		if issubclass(_lowercase						,  _lowercase ):
				_UpperCamelCase						    =	text_path
		elif issubclass(_lowercase						,  _lowercase ):
				_UpperCamelCase						    =	[text_path]
		_UpperCamelCase						    =	tmp_path / "cache"
		_UpperCamelCase						    =	{"text": "string"}
		_UpperCamelCase						    =	TextDatasetReader(_lowercase						,  cache_dir=_lowercase ).read()
		_check_text_dataset(_lowercase						,  _lowercase )


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=("train",) )		->				List[str]:

		"""simple docstring"""






		assert isinstance(_lowercase						,  _lowercase )
		for split in splits:
				_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path / "cache"
		_UpperCamelCase						    =	{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		_UpperCamelCase						    =	{"text": "string"}
		_UpperCamelCase						    =	features.copy() if features else default_expected_features
		_UpperCamelCase						    =	(
		    Features({feature: Value(_lowercase ) for feature, dtype in features.items()} ) if features is not None else None
		)
		_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		if split:
				_UpperCamelCase						    =	{split: text_path}
		else:
				_UpperCamelCase						    =	"train"
				_UpperCamelCase						    =	{"train": text_path, "test": text_path}
		_UpperCamelCase						    =	tmp_path / "cache"
		_UpperCamelCase						    =	{"text": "string"}
		_UpperCamelCase						    =	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() )





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		if "model" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""model."""						,  """""" )
		if "norm1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm1"""						,  """attention.output.LayerNorm""" )
		if "norm2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm2"""						,  """output.LayerNorm""" )
		if "norm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm"""						,  """LayerNorm""" )
		if "transformer" in orig_key:
				_UpperCamelCase						    =	orig_key.split(""".""" )[0].split("""_""" )[-1]
				_UpperCamelCase						    =	orig_key.replace(F'transformer_{layer_num}'						,  F'encoder.layer.{layer_num}' )
		if "mha.attn" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha.attn"""						,  """attention.self""" )
		if "mha" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha"""						,  """attention""" )
		if "W_q" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_q"""						,  """self.query""" )
		if "W_k" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_k"""						,  """self.key""" )
		if "W_v" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_v"""						,  """self.value""" )
		if "ff1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff1"""						,  """intermediate.dense""" )
		if "ff2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff2"""						,  """output.dense""" )
		if "ff" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff"""						,  """output.dense""" )
		if "mlm_class" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm.mlm_class"""						,  """cls.predictions.decoder""" )
		if "mlm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm"""						,  """cls.predictions.transform""" )
		if "cls" not in orig_key:
				_UpperCamelCase						    =	"""yoso.""" + orig_key

		return orig_key


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						_UpperCamelCase						    =	val

		_UpperCamelCase						    =	orig_state_dict["""cls.predictions.decoder.bias"""]
		_UpperCamelCase						    =	torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )["""model_state_dict"""]
		_UpperCamelCase						    =	YosoConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	YosoForMaskedLM(lowerCAmelCase )

		_UpperCamelCase						    =	convert_checkpoint_helper(config.max_position_embeddings						,  lowerCAmelCase )

		print(model.load_state_dict(lowerCAmelCase ) )
		model.eval()
		model.save_pretrained(lowerCAmelCase )

		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



