


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

A			:						Optional[int]        =  logging.getLogger()






def 					UpperCamelCase   (      )  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser()
		parser.add_argument("""-f"""	)
		lowercase__	     =						parser.parse_args()
		return args.f







class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[str]						)		->  None:




							"""simple docstring"""


							lowercase__	     =						logging.StreamHandler(sys.stdout						)
							logger.addHandler(_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Optional[int]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						get_gpu_count()

							if n_gpu > 1:
									pass
									# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
									# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
									# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
									# cmd = [sys.executable] + distributed_args + args
									# execute_subprocess_async(cmd, env=self.get_env())
									# XXX: test the results - need to save them first into .json file
							else:
									args.insert(0 ,   """run_glue_deebert.py"""						)
									with patch.object(_UpperCAmelCase ,   """argv""" ,   _UpperCAmelCase						):
											lowercase__	     =						run_glue_deebert.main()
											for value in result.values():
													self.assertGreaterEqual(_UpperCAmelCase ,   0.666						)


					@slow
					@require_torch_non_multi_gpu
					def     lowerCamelCase__	(self       :		int						)		->  str:




							"""simple docstring"""


							lowercase__	     =						"""
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
							self.run_and_check(_UpperCAmelCase						)

							lowercase__	     =						"""
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
							self.run_and_check(_UpperCAmelCase						)

							lowercase__	     =						"""
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
							self.run_and_check(_UpperCAmelCase						)





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

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

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  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			:						Any        =  parser.parse_args()
	A			:						str        =  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)

