


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict






@pytest.mark.parametrize(
    "files"      ,						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]      ,						)
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				tmp_path_factory.mktemp("dset_infos_dir"	)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md"      ,						"w"	) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"	)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md"      ,						"w"	) as f:
      f.write(""	)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json"      ,						"w"	) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"	)
  lowerCamelCase :					int    =				DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE_	)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42






@pytest.mark.parametrize(
    "dataset_info"      ,						[
        DatasetInfo(),
        DatasetInfo(
            description="foo"      ,						features=Features({"a": Value("int32"	)}	)      ,						builder_name="builder"      ,						config_name="config"      ,						version="1.0.0"      ,						splits=[{"name": "train"}]      ,						download_size=42      ,						),
    ]      ,						)
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				str(SCREAMING_SNAKE_CASE_	)
  dataset_info.write_to_directory(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					int    =				DatasetInfo.from_directory(SCREAMING_SNAKE_CASE_	)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_      ,						"dataset_info.json"	)	)






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				DatasetInfo(
      description="foo"      ,						citation="bar"      ,						homepage="https://foo.bar"      ,						license="CC0"      ,						features=Features({"a": Value("int32"	)}	)      ,						post_processed={}      ,						supervised_keys=()      ,						task_templates=[]      ,						builder_name="builder"      ,						config_name="config"      ,						version="1.0.0"      ,						splits=[{"name": "train", "num_examples": 42}]      ,						download_checksums={}      ,						download_size=1337      ,						post_processing_size=442      ,						dataset_size=1234      ,						size_in_bytes=1337 + 442 + 1234      ,						)
  lowerCamelCase :					Optional[Any]    =				dataset_info._to_yaml_dict()
  assert sorted(SCREAMING_SNAKE_CASE_	) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML	)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]      ,						(list, dict, int, str)	)
  lowerCamelCase :					Tuple    =				yaml.safe_dump(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				yaml.safe_load(SCREAMING_SNAKE_CASE_	)
  assert dataset_info_yaml_dict == reloaded






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				DatasetInfo()
  lowerCamelCase :					Tuple    =				dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    "dataset_infos_dict"      ,						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}	),
        DatasetInfosDict({"my_config_name": DatasetInfo()}	),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"      ,						features=Features({"a": Value("int32"	)}	)      ,						builder_name="builder"      ,						config_name="config"      ,						version="1.0.0"      ,						splits=[{"name": "train"}]      ,						download_size=42      ,						)
            }	),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42	),
                "v2": DatasetInfo(dataset_size=1337	),
            }	),
    ]      ,						)
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				str(SCREAMING_SNAKE_CASE_	)
  dataset_infos_dict.write_to_directory(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					int    =				DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE_	)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    lowerCamelCase :					List[Any]    =				config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    lowerCamelCase :					Optional[int]    =				DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()	)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_      ,						"README.md"	)	)







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

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

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

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

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     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.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



