



"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


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


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

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

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

			return model


if __name__ == "__main__":
	__snake_case							:   Union[str, Any]      =    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							:   Dict      =    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 json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case							:   Optional[int]      =    50_000
__snake_case							:   Dict      =    5_000

__snake_case      ,		__snake_case							:   Union[str, Any]      =    os.path.split(__file__)
__snake_case							:   Any      =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			for i in range(0					,len(__snake_case	)					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       dataset[i : i + batch_size]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   str:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(0					,__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[int]												=       dataset[i : i + batch_size]


def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Optional[int]												=       {"num examples": SPEED_TEST_N_EXAMPLES}
			__lowerCAmelCase      :  Optional[int]												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
			    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
			    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]

			__lowerCAmelCase      :  Any												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"	)
						__lowerCAmelCase      :  int												=       datasets.Features(
						    {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
						__lowerCAmelCase      :  str												=       generate_example_dataset(
						    os.path.join(__snake_case					,"dataset.arrow"	)					,__snake_case					,num_examples=__snake_case					,seq_shapes={"list": (100,)}					,)
						print("first set of iterations"	)
						for func, kwargs in functions:
									print(func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  str												=       func(__snake_case					,**__snake_case	)

						print("shuffling dataset"	)
						__lowerCAmelCase      :  Optional[int]												=       dataset.shuffle()
						print("Second set of iterations (after shuffling"	)
						for func, kwargs in functions_shuffled:
									print("shuffled "					,func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  List[str]												=       func(
									    __snake_case					,**__snake_case	)

			with open(__snake_case					,"wb"	) as f:
						f.write(json.dumps(__snake_case	).encode("utf-8"	)	)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_iterating()