





"""simple docstring"""





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "decoder.version",
		    "decoder.output_projection.weight",
		    "_float_tensor",
		    "decoder.embed_positions._float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	Namespace(**checkpoint["cfg"]["model"]    )
		UpperCamelCase   =	checkpoint["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	{key.replace("decoder"  ,	"model"    ): val for key, val in state_dict.items()}

		UpperCamelCase   =	XGLMConfig(
		    vocab_size=_SCREAMING_SNAKE_CASE  ,	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   =	XGLMForCausalLM(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		print(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			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.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)













"""simple docstring"""





import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows


def       a__					(					_SCREAMING_SNAKE_CASE    ):  # picklable for multiprocessing




		"""simple docstring"""


		return i + 1


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def       a__					(					):




		"""simple docstring"""


		with parallel_backend("spark"    ):
				assert ParallelBackendConfig.backend_name == "spark"

		UpperCamelCase   =	[1, 2, 3]
		with pytest.raises(_SCREAMING_SNAKE_CASE    ):
				with parallel_backend("unsupported backend"    ):
						map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=2    )

		with pytest.raises(_SCREAMING_SNAKE_CASE    ):
				with parallel_backend("unsupported backend"    ):
						map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=-1    )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"  ,	[2, -1]    )
def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[1, 2]
		UpperCamelCase   =	{"a": 1, "b": 2}
		UpperCamelCase   =	{"a": [1, 2], "b": [3, 4]}
		UpperCamelCase   =	{"a": {"1": 1}, "b": 2}
		UpperCamelCase   =	{"a": 1, "b": 2, "c": 3, "d": 4}
		UpperCamelCase   =	[2, 3]
		UpperCamelCase   =	{"a": 2, "b": 3}
		UpperCamelCase   =	{"a": [2, 3], "b": [4, 5]}
		UpperCamelCase   =	{"a": {"1": 2}, "b": 3}
		UpperCamelCase   =	{"a": 2, "b": 3, "c": 4, "d": 5}

		with parallel_backend("spark"    ):
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
				assert map_nested(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	num_proc=_SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa






