



"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__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      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					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.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__snake_case							:     str   =					pytest.mark.integration


@pytest.mark.parametrize("path"      ,["paws", "csv"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			inspect_dataset(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"      ,["accuracy"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			inspect_metric(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.parametrize(
    "path, config_name, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						str				= get_dataset_config_info(__snake_case      ,config_name=__snake_case			)
			assert info.config_name == config_name
			assert list(info.splits.keys()			) == expected_splits


@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			with pytest.raises(__snake_case			):
						get_dataset_config_info(__snake_case      ,config_name=__snake_case			)


@pytest.mark.parametrize(
    "path, expected"      ,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						Dict				= get_dataset_config_names(__snake_case			)
			assert expected in config_names


@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"      ,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[Any]:
			__lowerCAmelCase   :						List[Any]				= get_dataset_infos(__snake_case			)
			assert list(infos.keys()			) == expected_configs
			__lowerCAmelCase   :						List[Any]				= expected_configs[0]
			assert expected_config in infos
			__lowerCAmelCase   :						Tuple				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits_in_first_config


@pytest.mark.parametrize(
    "path, expected_config, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						Tuple				= get_dataset_infos(__snake_case			)
			assert expected_config in infos
			__lowerCAmelCase   :						int				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			with pytest.raises(__snake_case			):
						get_dataset_split_names(__snake_case      ,config_name=__snake_case			)