



'''simple docstring'''






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Tuple:
							UpperCAmelCase				: Tuple											=	[
							    '''decoder.version''',
							    '''decoder.output_projection.weight''',
							    '''_float_tensor''',
							    '''decoder.embed_positions._float_tensor''',
							]
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase     ,   _lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     int:
							UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	emb.weight.shape
							UpperCAmelCase				: str											=	nn.Linear(_lowerCAmelCase     ,   _lowerCAmelCase     ,   bias=_lowerCAmelCase						)
							UpperCAmelCase				: Tuple											=	emb.weight.data
							return lin_layer


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     Union[str, Any]:
							UpperCAmelCase				: Union[str, Any]											=	torch.load(_lowerCAmelCase     ,   map_location='''cpu'''						)
							UpperCAmelCase				: List[Any]											=	Namespace(**checkpoint['''cfg''']['''model''']						)
							UpperCAmelCase				: Any											=	checkpoint['''model''']
							remove_ignore_keys_(_lowerCAmelCase						)
							UpperCAmelCase				: Any											=	state_dict['''decoder.embed_tokens.weight'''].shape[0]

							UpperCAmelCase				: Union[str, Any]											=	{key.replace('''decoder'''     ,   '''model'''						): val for key, val in state_dict.items()}

							UpperCAmelCase				: Optional[Any]											=	XGLMConfig(
							    vocab_size=_lowerCAmelCase     ,   max_position_embeddings=args.max_target_positions     ,   num_layers=args.decoder_layers     ,   attention_heads=args.decoder_attention_heads     ,   ffn_dim=args.decoder_ffn_embed_dim     ,   d_model=args.decoder_embed_dim     ,   layerdrop=args.decoder_layerdrop     ,   dropout=args.dropout     ,   attention_dropout=args.attention_dropout     ,   activation_dropout=args.activation_dropout     ,   activation_function='''gelu'''     ,   scale_embedding=not args.no_scale_embedding     ,   tie_word_embeddings=args.share_decoder_input_output_embed     ,   )

							UpperCAmelCase				: str											=	XGLMForCausalLM(_lowerCAmelCase						)
							UpperCAmelCase				: Tuple											=	model.load_state_dict(_lowerCAmelCase     ,   strict=_lowerCAmelCase						)
							print(_lowerCAmelCase						)
							UpperCAmelCase				: List[Any]											=	make_linear_from_emb(model.model.embed_tokens						)

							return model


if __name__ == "__main__":
						UpperCamelCase__:     Optional[int]										=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
						parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						UpperCamelCase__:     Optional[Any]										=    parser.parse_args()
						UpperCamelCase__:     str										=    convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)










'''simple docstring'''






import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Tuple						)   ->     str:
							assert isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						)
							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


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''     ,   [False, True]						)
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[str]:
							UpperCAmelCase				: int											=	tmp_path / '''cache'''
							UpperCAmelCase				: Dict											=	{'''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				: Any											=	SqlDatasetReader(
														    '''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase     ,   keep_in_memory=_lowerCAmelCase						).read()
							_check_sql_dataset(_lowerCAmelCase     ,   _lowerCAmelCase						)


@require_sqlalchemy
@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 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		str						)   ->     Optional[int]:
							UpperCAmelCase				: Union[str, Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Union[str, Any]											=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase				: str											=	features.copy() if features else default_expected_features
							UpperCAmelCase				: Optional[Any]											=	(
							    Features({feature: Value(_lowerCAmelCase						) for feature, dtype in features.items()}						) if features is not None else None
							)
							UpperCAmelCase				: Tuple											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   features=_lowerCAmelCase     ,   cache_dir=_lowerCAmelCase						).read()
							_check_sql_dataset(_lowerCAmelCase     ,   _lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Dict:
							with contextlib.closing(sqlitea.connect(_lowerCAmelCase						)						) as con:
														UpperCAmelCase				: Any											=	con.cursor()
														cur.execute('''SELECT * FROM dataset'''						)
														for row in cur:
																					yield row


@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Any						)   ->     Optional[int]:
							UpperCAmelCase				: Optional[int]											=	tmp_path / '''cache'''
							UpperCAmelCase				: int											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: Any											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=1						).write()

							UpperCAmelCase				: List[Any]											=	iter_sql_file(_lowerCAmelCase						)
							UpperCAmelCase				: List[Any]											=	iter_sql_file(_lowerCAmelCase						)

							for rowa, rowa in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														assert rowa == rowa


@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     int:
							UpperCAmelCase				: Optional[Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Any											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: List[str]											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=2						).write()

							UpperCAmelCase				: List[str]											=	iter_sql_file(_lowerCAmelCase						)
							UpperCAmelCase				: Any											=	iter_sql_file(_lowerCAmelCase						)

							for rowa, rowa in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														assert rowa == rowa




@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							UpperCAmelCase				: Union[str, Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Tuple											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: Optional[int]											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							with pytest.raises(_lowerCAmelCase						):
														SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=0						).write()





