code: 
  
  
  '''simple docstring'''
  
  
  
  
  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
  
  
  __lowercase  :      Optional[Any]      		=				collections.namedtuple(
      """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
  )
  
  
  
  
  
  def 							a_       (						lowercase    )			->Tuple:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				print(F'Reading saved model from {model_file}'    )
  				__lowerCamelCase		:      str											=					torch.load(lowercase_							,						map_location=lambda lowercase							,						lowercase    : default_restore_location(lowercase_							,						"cpu"    )    )
  				return CheckpointState(**lowercase_    )
  
  
  
  
  
  class _snake_case   :
  							"""simple docstring"""
  
  
  
  
  
  							def __init__(   self			,				__A	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					src_file
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											raise NotImplementedError
  
  
  
  
  
  
  
  							@staticmethod
  							def 		__SCREAMING_SNAKE_CASE(   __A			,				*__A			,				**__A	)     ->"DPRState":
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if comp_type.startswith("c"	):
  															return DPRContextEncoderState(*__A			,				**__A	)
  											if comp_type.startswith("q"	):
  															return DPRQuestionEncoderState(*__A			,				**__A	)
  											if comp_type.startswith("r"	):
  															return DPRReaderState(*__A			,				**__A	)
  											else:
  															raise ValueError("Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'."	)
  
  
  
  
  
  class _snake_case   ( a_   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"	)[0]	)	)
  											print(f'Loading DPR biencoder from {self.src_file}'	)
  											__lowerCamelCase		:      Optional[int]											=					load_states_from_checkpoint(self.src_file	)
  											__lowerCamelCase		:      int											=					model.ctx_encoder, 'ctx_model.'
  											# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  											__lowerCamelCase		:      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(__A	):
  																			__lowerCamelCase		:      Dict											=					key[len(__A	) :]
  																			if not key.startswith("encode_proj."	):
  																							__lowerCamelCase		:      int											=					'bert_model.' + key
  																			__lowerCamelCase		:      int											=					value
  											encoder.load_state_dict(__A	)
  											return model
  
  
  
  
  
  class _snake_case   ( a_   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"	)[0]	)	)
  											print(f'Loading DPR biencoder from {self.src_file}'	)
  											__lowerCamelCase		:      Tuple											=					load_states_from_checkpoint(self.src_file	)
  											__lowerCamelCase		:      int											=					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(__A	):
  																			__lowerCamelCase		:      Optional[Any]											=					key[len(__A	) :]
  																			if not key.startswith("encode_proj."	):
  																							__lowerCamelCase		:      List[str]											=					'bert_model.' + key
  																			__lowerCamelCase		:      Optional[int]											=					value
  											encoder.load_state_dict(__A	)
  											return model
  
  
  
  
  
  class _snake_case   ( a_   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"	)[0]	)	)
  											print(f'Loading DPR reader from {self.src_file}'	)
  											__lowerCamelCase		:      Optional[int]											=					load_states_from_checkpoint(self.src_file	)
  											# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  											__lowerCamelCase		:      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		:      str											=					'encoder.bert_model.' + key[len("encoder."	) :]
  															__lowerCamelCase		:      List[str]											=					value
  											model.span_predictor.load_state_dict(__A	)
  											return model
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Any:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      List[str]											=					Path(lowercase_    )
  				dest_dir.mkdir(exist_ok=lowercase_    )
  
  				__lowerCamelCase		:      List[Any]											=					DPRState.from_type(lowercase_							,						src_file=lowercase_    )
  				__lowerCamelCase		:      Dict											=					dpr_state.load_dpr_model()
  				model.save_pretrained(lowercase_    )
  				model.from_pretrained(lowercase_    )  # sanity check
  
  
  if __name__ == "__main__":
  						__lowercase  :      Any      		=				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.""")
  						__lowercase  :      Any      		=				parser.parse_args()
  
  						__lowercase  :      Dict      		=				Path(args.src)
  						__lowercase  :      Tuple      		=				f"converted-{src_file.name}" if args.dest is None else args.dest
  						__lowercase  :      Union[str, Any]      		=				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)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import unittest
  from functools import partial
  
  import torch
  
  from accelerate import Accelerator, debug_launcher
  from accelerate.state import AcceleratorState, GradientState
  from accelerate.test_utils import require_cpu, require_huggingface_suite
  from accelerate.utils import GradientAccumulationPlugin
  
  
  
  
  
  def 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Optional[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      List[str]											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
  				__lowerCamelCase		:      Union[str, Any]											=					torch.nn.Linear(2							,						4    )
  				__lowerCamelCase		:      str											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
  				__lowerCamelCase		:      List[str]											=					torch.optim.lr_scheduler.OneCycleLR(lowercase							,						max_lr=0.0_1							,						steps_per_epoch=2							,						epochs=1    )
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  				# Optimizer has stepped
  				scheduler.step()
  				if step_scheduler_with_optimizer or (num_processes == 1):
  								assert (
  								    scheduler.scheduler.last_epoch == num_processes
  								), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
  				else:
  								assert (
  								    scheduler.scheduler.last_epoch != num_processes
  								), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'
  
  
  
  
  
  def 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Dict											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
  				__lowerCamelCase		:      Tuple											=					torch.nn.Linear(2							,						4    )
  				__lowerCamelCase		:      Optional[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
  				__lowerCamelCase		:      Optional[Any]											=					torch.optim.lr_scheduler.LambdaLR(lowercase							,						lr_lambda=lambda lowercase    : 1 - n / 10    )
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  				# Optimizer has stepped
  				__lowerCamelCase		:      List[Any]											=					False
  				scheduler.step()
  				__lowerCamelCase		:      List[Any]											=					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  				assert (
  				    scheduler.get_last_lr()[0] == expected_lr
  				), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  				# Optimizer has not stepped
  				__lowerCamelCase		:      Any											=					True
  				scheduler.step()
  				if not step_scheduler_with_optimizer:
  								__lowerCamelCase		:      int											=					1 - 2 / 10
  				assert (
  				    scheduler.get_last_lr()[0] == expected_lr
  				), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  
  
  
  
  def 							a_       (						lowercase = 2    )			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				from transformers import get_linear_schedule_with_warmup
  
  				__lowerCamelCase		:      str											=					[1, 2, 4]
  				for num_steps in steps:
  								__lowerCamelCase		:      List[Any]											=					GradientAccumulationPlugin(num_steps=lowercase							,						adjust_scheduler=num_steps > 1    )
  								__lowerCamelCase		:      Dict											=					Accelerator(gradient_accumulation_plugin=lowercase    )
  								__lowerCamelCase		:      Any											=					torch.nn.Linear(2							,						4    )
  								__lowerCamelCase		:      List[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1_0.0    )
  								__lowerCamelCase		:      Optional[Any]											=					get_linear_schedule_with_warmup(optimizer=lowercase							,						num_warmup_steps=0							,						num_training_steps=20    )
  
  								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  								for i in range(10 * num_steps    ):
  												with accelerator.accumulate(lowercase    ):
  																optimizer.step()
  																scheduler.step()
  
  												if i == (10 * num_steps - 2):
  																assert (
  																    scheduler.get_last_lr()[0] != 0
  																), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
  								assert (
  								    scheduler.get_last_lr()[0] == 0
  								), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
  								GradientState._reset_state()
  
  
  
  
  
  @require_cpu
  class _snake_case   ( unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1	)			,				num_processes=1	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1	)			,				num_processes=1	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1			,				step_scheduler_with_optimizer=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1			,				step_scheduler_with_optimizer=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(__A	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(__A	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				step_scheduler_with_optimizer=__A	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				step_scheduler_with_optimizer=__A	)	)
  
  
  
  
  
  
  
  							@require_huggingface_suite
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				num_processes=1	)	)
  											debug_launcher(__A	)
  
  
  
  
  
  