




"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_       							=    {
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["BloomTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BloomForCausalLM",
		    "BloomModel",
		    "BloomPreTrainedModel",
		    "BloomForSequenceClassification",
		    "BloomForTokenClassification",
		    "BloomForQuestionAnswering",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

				if finetuned:
								_A					  =			make_linear_from_emb(model.model.shared   )

				return model


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a 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.")
		parser.add_argument(
		    "--hf_config",
		    default="facebook/mbart-large-cc25",
		    type=str,
		    help="Which huggingface architecture to use: mbart-large",
		)
		parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		a_       							=    parser.parse_args()
		a_       							=    convert_fairseq_mbart_checkpoint_from_disk(
		    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
		)
		model.save_pretrained(args.pytorch_dump_folder_path)