




import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


UpperCamelCase__						=		collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)






def    snake_case   (				A     )				->				CheckpointState:




	'''simple docstring'''


	print(f"Reading saved model from {model_file}"     )
	lowerCamelCase_				:						List[str]				  =				torch.load(A		,  map_location=lambda A		,  A     : default_restore_location(A		,  'cpu'     )     )
	return CheckpointState(**A     )


class 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				src_file

					def       lowercase_				(							self   ):







						'''simple docstring'''




						raise NotImplementedError





					@staticmethod
					def       lowercase_				(							__lowercase					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




						if comp_type.startswith('c'   ):
							return DPRContextEncoderState(*__lowercase					,			**__lowercase   )
						if comp_type.startswith('q'   ):
							return DPRQuestionEncoderState(*__lowercase					,			**__lowercase   )
						if comp_type.startswith('r'   ):
							return DPRReaderState(*__lowercase					,			**__lowercase   )
						else:
							raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
						print(f"Loading DPR biencoder from {self.src_file}"   )
						lowerCamelCase_				:						List[Any]				  =				load_states_from_checkpoint(self.src_file   )
						lowerCamelCase_  ,     lowerCamelCase_				:						Tuple				  =				model.ctx_encoder, 'ctx_model.'
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						lowerCamelCase_				:						Optional[int]				  =				{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
							if key.startswith(__lowercase   ):
								lowerCamelCase_				:						Tuple				  =				key[len(__lowercase   ) :]
								if not key.startswith('encode_proj.'   ):
									lowerCamelCase_				:						Any				  =				'bert_model.' + key
								lowerCamelCase_				:						List[str]				  =				value
						encoder.load_state_dict(__lowercase   )
						return model


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
						print(f"Loading DPR biencoder from {self.src_file}"   )
						lowerCamelCase_				:						str				  =				load_states_from_checkpoint(self.src_file   )
						lowerCamelCase_  ,     lowerCamelCase_				:						str				  =				model.question_encoder, 'question_model.'
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						lowerCamelCase_				:						Optional[Any]				  =				{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
							if key.startswith(__lowercase   ):
								lowerCamelCase_				:						str				  =				key[len(__lowercase   ) :]
								if not key.startswith('encode_proj.'   ):
									lowerCamelCase_				:						Optional[int]				  =				'bert_model.' + key
								lowerCamelCase_				:						str				  =				value
						encoder.load_state_dict(__lowercase   )
						return model


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
						print(f"Loading DPR reader from {self.src_file}"   )
						lowerCamelCase_				:						Union[str, Any]				  =				load_states_from_checkpoint(self.src_file   )
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						lowerCamelCase_				:						Optional[int]				  =				{
						    'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
						}
						for key, value in saved_state.model_dict.items():
							if key.startswith('encoder.'   ) and not key.startswith('encoder.encode_proj'   ):
								lowerCamelCase_				:						Any				  =				'encoder.bert_model.' + key[len('encoder.'   ) :]
							lowerCamelCase_				:						Union[str, Any]				  =				value
						model.span_predictor.load_state_dict(__lowercase   )
						return model






def    snake_case   (				A		,  A		,  A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						List[Any]				  =				Path(A     )
	dest_dir.mkdir(exist_ok=A     )

	lowerCamelCase_				:						Optional[Any]				  =				DPRState.from_type(A		,  src_file=A     )
	lowerCamelCase_				:						List[Any]				  =				dpr_state.load_dpr_model()
	model.save_pretrained(A     )
	model.from_pretrained(A     )  # sanity check


if __name__ == "__main__":
							UpperCamelCase__						=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
							)
							parser.add_argument(
							    '--src',
							    type=str,
							    help=(
							        'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
							        ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
							        ' \'retriever\' checkpoints.'
							    ),
							)
							parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
							UpperCamelCase__						=		parser.parse_args()

							UpperCamelCase__						=		Path(args.src)
							UpperCamelCase__						=		F'converted-{src_file.name}' if args.dest is None else args.dest
							UpperCamelCase__						=		Path(dest_dir)
							assert src_file.exists()
							assert (
							    args.type is not None
							), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
							convert(args.type, src_file, dest_dir)


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



