



'''simple docstring'''
import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						assert isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)
						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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  tmp_path / 'cache'
						_UpperCamelCase   :						Union[str, Any]												=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCamelCase   :						int												=  JsonDatasetReader(UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_    ,    keep_in_memory=UpperCAmelCase_				).read()
						_check_json_dataset(UpperCAmelCase_    ,    UpperCAmelCase_				)






@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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Tuple												=  tmp_path / 'cache'
						_UpperCamelCase   :						List[Any]												=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
						_UpperCamelCase   :						Optional[Any]												=  features.copy() if features else default_expected_features
						_UpperCamelCase   :						Union[str, Any]												=  (
						    Features({feature: Value(UpperCAmelCase_				) for feature, dtype in features.items()}				) if features is not None else None
						)
						_UpperCamelCase   :						Optional[int]												=  JsonDatasetReader(UpperCAmelCase_    ,    features=UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_				).read()
						_check_json_dataset(UpperCAmelCase_    ,    UpperCAmelCase_				)






@pytest.mark.parametrize(
    'features'    ,    [
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ]    ,    )
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  tmp_path / 'cache'
						_UpperCamelCase   :						Union[str, Any]												=  {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
						_UpperCamelCase   :						Dict												=  features.copy() if features else default_expected_features
						_UpperCamelCase   :						Optional[int]												=  (
						    Features({feature: Value(UpperCAmelCase_				) for feature, dtype in features.items()}				) if features is not None else None
						)
						_UpperCamelCase   :						str												=  JsonDatasetReader(UpperCAmelCase_    ,    features=UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_				).read()
						assert isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						_UpperCamelCase   :						List[Any]												=  {'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
						_UpperCamelCase   :						Optional[Any]												=  features.copy()
						_UpperCamelCase   :						Any												=  (
						    Features({feature: Value(UpperCAmelCase_				) for feature, dtype in features.items()}				) if features is not None else None
						)
						_UpperCamelCase   :						Union[str, Any]												=  tmp_path / 'cache'
						_UpperCamelCase   :						List[Any]												=  JsonDatasetReader(UpperCAmelCase_    ,    features=UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_				).read()
						assert isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('split'    ,    [None, NamedSplit('train'				), 'train', 'test']				)
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Union[str, Any]												=  tmp_path / 'cache'
						_UpperCamelCase   :						Optional[int]												=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
						_UpperCamelCase   :						str												=  JsonDatasetReader(UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_    ,    split=UpperCAmelCase_				).read()
						_check_json_dataset(UpperCAmelCase_    ,    UpperCAmelCase_				)
						assert dataset.split == split if split else "train"






@pytest.mark.parametrize('path_type'    ,    [str, list]				)
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						if issubclass(UpperCAmelCase_    ,    UpperCAmelCase_				):
												_UpperCamelCase   :						Any												=  jsonl_path
						elif issubclass(UpperCAmelCase_    ,    UpperCAmelCase_				):
												_UpperCamelCase   :						str												=  [jsonl_path]
						_UpperCamelCase   :						Optional[Any]												=  tmp_path / 'cache'
						_UpperCamelCase   :						Union[str, Any]												=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
						_UpperCamelCase   :						int												=  JsonDatasetReader(UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_				).read()
						_check_json_dataset(UpperCAmelCase_    ,    UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=("train",)				):
						assert isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)
						for split in splits:
												_UpperCamelCase   :						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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[str]												=  tmp_path / 'cache'
						_UpperCamelCase   :						Union[str, Any]												=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCamelCase   :						List[str]												=  JsonDatasetReader({'train': jsonl_path}    ,    cache_dir=UpperCAmelCase_    ,    keep_in_memory=UpperCAmelCase_				).read()
						_check_json_datasetdict(UpperCAmelCase_    ,    UpperCAmelCase_				)






@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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Optional[int]												=  tmp_path / 'cache'
						_UpperCamelCase   :						List[Any]												=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
						_UpperCamelCase   :						str												=  features.copy() if features else default_expected_features
						_UpperCamelCase   :						List[str]												=  (
						    Features({feature: Value(UpperCAmelCase_				) for feature, dtype in features.items()}				) if features is not None else None
						)
						_UpperCamelCase   :						Optional[Any]												=  JsonDatasetReader({'train': jsonl_path}    ,    features=UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_				).read()
						_check_json_datasetdict(UpperCAmelCase_    ,    UpperCAmelCase_				)






@pytest.mark.parametrize('split'    ,    [None, NamedSplit('train'				), 'train', 'test']				)
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						if split:
												_UpperCamelCase   :						Union[str, Any]												=  {split: jsonl_path}
						else:
												_UpperCamelCase   :						Dict												=  'train'
												_UpperCamelCase   :						Any												=  {'train': jsonl_path, 'test': jsonl_path}
						_UpperCamelCase   :						List[str]												=  tmp_path / 'cache'
						_UpperCamelCase   :						Any												=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
						_UpperCamelCase   :						List[Any]												=  JsonDatasetReader(UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_				).read()
						_check_json_datasetdict(UpperCAmelCase_    ,    UpperCAmelCase_    ,    splits=list(path.keys()				)				)
						assert all(dataset[split].split == split for split in path.keys()				)






def A__       (	UpperCAmelCase_				):
						return json.load(UpperCAmelCase_				)






def A__       (	UpperCAmelCase_				):
						return [json.loads(UpperCAmelCase_				) for line in buffer]






class 				lowercase__      :





	@pytest.mark.parametrize('lines, load_json_function'					,[(True, load_json_lines), (False, load_json)]					)
	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							with io.BytesIO() as buffer:
													JsonDatasetWriter(lowerCamelCase__					,lowerCamelCase__					,lines=lowerCamelCase__					).write()
													buffer.seek(0					)
													_UpperCamelCase   :						Union[str, Any]												=  load_json_function(lowerCamelCase__					)
							assert isinstance(lowerCamelCase__					,lowerCamelCase__					)
							assert isinstance(exported_content[0]					,lowerCamelCase__					)
							assert len(lowerCamelCase__					) == 10





	@pytest.mark.parametrize(
	    'orient, container, keys, len_at'					,[
	        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
	        ('split', dict, {'columns', 'data'}, 'data'),
	        ('index', dict, set('0123456789'					), None),
	        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
	        ('values', list, None, None),
	        ('table', dict, {'schema', 'data'}, 'data'),
	    ]					,)
	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Optional[Any]					):







							'''simple docstring'''
							with io.BytesIO() as buffer:
													JsonDatasetWriter(lowerCamelCase__					,lowerCamelCase__					,lines=lowerCamelCase__					,orient=lowerCamelCase__					).write()
													buffer.seek(0					)
													_UpperCamelCase   :						Dict												=  load_json(lowerCamelCase__					)
							assert isinstance(lowerCamelCase__					,lowerCamelCase__					)
							if keys:
													if container is dict:
																			assert exported_content.keys() == keys
													else:
																			assert exported_content[0].keys() == keys
							else:
													assert not hasattr(lowerCamelCase__					,'keys'					) and not hasattr(exported_content[0]					,'keys'					)
							if len_at:
													assert len(exported_content[len_at]					) == 10
							else:
													assert len(lowerCamelCase__					) == 10





	@pytest.mark.parametrize('lines, load_json_function'					,[(True, load_json_lines), (False, load_json)]					)
	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Optional[int]					,lowerCamelCase__   :							List[Any]					):







							'''simple docstring'''
							with io.BytesIO() as buffer:
													JsonDatasetWriter(lowerCamelCase__					,lowerCamelCase__					,lines=lowerCamelCase__					,num_proc=2					).write()
													buffer.seek(0					)
													_UpperCamelCase   :						Tuple												=  load_json_function(lowerCamelCase__					)
							assert isinstance(lowerCamelCase__					,lowerCamelCase__					)
							assert isinstance(exported_content[0]					,lowerCamelCase__					)
							assert len(lowerCamelCase__					) == 10





	@pytest.mark.parametrize(
	    'orient, container, keys, len_at'					,[
	        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
	        ('split', dict, {'columns', 'data'}, 'data'),
	        ('index', dict, set('0123456789'					), None),
	        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
	        ('values', list, None, None),
	        ('table', dict, {'schema', 'data'}, 'data'),
	    ]					,)
	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							with io.BytesIO() as buffer:
													JsonDatasetWriter(lowerCamelCase__					,lowerCamelCase__					,lines=lowerCamelCase__					,orient=lowerCamelCase__					,num_proc=2					).write()
													buffer.seek(0					)
													_UpperCamelCase   :						Any												=  load_json(lowerCamelCase__					)
							assert isinstance(lowerCamelCase__					,lowerCamelCase__					)
							if keys:
													if container is dict:
																			assert exported_content.keys() == keys
													else:
																			assert exported_content[0].keys() == keys
							else:
													assert not hasattr(lowerCamelCase__					,'keys'					) and not hasattr(exported_content[0]					,'keys'					)
							if len_at:
													assert len(exported_content[len_at]					) == 10
							else:
													assert len(lowerCamelCase__					) == 10





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Any					):







							'''simple docstring'''
							with pytest.raises(lowerCamelCase__					):
													with io.BytesIO() as buffer:
																			JsonDatasetWriter(lowerCamelCase__					,lowerCamelCase__					,num_proc=0					)





	@pytest.mark.parametrize('compression, extension'					,[('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]					)
	def       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							str					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  tmp_path_factory.mktemp('data'					) / F'test.json.{extension}'
							_UpperCamelCase   :						Optional[Any]												=  str(shared_datadir / F'test_file.json.{extension}'					)
							JsonDatasetWriter(lowerCamelCase__					,lowerCamelCase__					,compression=lowerCamelCase__					).write()

							with fsspec.open(lowerCamelCase__					,'rb'					,compression='infer'					) as f:
													_UpperCamelCase   :						Optional[int]												=  f.read()
							with fsspec.open(lowerCamelCase__					,'rb'					,compression='infer'					) as f:
													_UpperCamelCase   :						List[str]												=  f.read()
							assert exported_content == original_content







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






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

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

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

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


if __name__ == "__main__":
					snake_case_					:      int    			=    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_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


