






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
	import torch


UpperCAmelCase_							=      logging.get_logger(__name__)


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=21						,      __UpperCAmelCase=[]						,      __UpperCAmelCase=None    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  model
							lowerCAmelCase__	 =  trace_batch_nums
							lowerCAmelCase__	 =  abort_after_batch_num

							# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
							lowerCAmelCase__	 =  collections.deque([]						,      __UpperCAmelCase    )
							lowerCAmelCase__	 =  []
							lowerCAmelCase__	 =  0
							lowerCAmelCase__	 =  0
							lowerCAmelCase__	 =  False
							lowerCAmelCase__	 =  "                 "

							self.analyse_model()

							self.register_forward_hook()

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None    )->   int:







							'''simple docstring'''





							if frame is not None:
													self.expand_frame(__UpperCAmelCase    )
							self.frames.append("\n".join(self.frame    )    )
							lowerCAmelCase__	 =  []  # start a new frame

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Union[str, Any]:







							'''simple docstring'''





							self.frame.append(__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							print("\n".join(self.frames    )    )
							lowerCAmelCase__	 =  []

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  []

	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							print(F"\nDetected inf/nan during batch_number={self.batch_number}"    )
							print(F"Last {len(self.frames    )} forward frames:"    )
							print(F"{'abs min':8} {'abs max':8} metadata"    )
							print("\n".join(self.frames    )    )
							print("\n\n"    )
							lowerCAmelCase__	 =  []

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  {m: name for name, m in self.model.named_modules()}
							# self.longest_module_name = max(len(v) for v in self.module_names.values())

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if torch.is_tensor(__UpperCAmelCase    ):
													self.expand_frame(get_abs_min_max(__UpperCAmelCase						,      __UpperCAmelCase    )    )
													if detect_overflow(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  True
							elif var is None:
													self.expand_frame(F"{'None':>17} {ctx}"    )
							else:
													self.expand_frame(F"{'not a tensor':>17} {ctx}"    )

	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"    )
							self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"    )

	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"    )

							# params
							for name, p in module.named_parameters(recurse=__UpperCAmelCase    ):
													self.analyse_variable(__UpperCAmelCase						,      __UpperCAmelCase    )

							# inputs
							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													for i, x in enumerate(__UpperCAmelCase    ):
																			self.analyse_variable(__UpperCAmelCase						,      F"input[{i}]"    )
							else:
													self.analyse_variable(__UpperCAmelCase						,      "input"    )

							# outputs
							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													for i, x in enumerate(__UpperCAmelCase    ):
																			# possibly a tuple of tuples
																			if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																									for j, y in enumerate(__UpperCAmelCase    ):
																															self.analyse_variable(__UpperCAmelCase						,      F"output[{i}][{j}]"    )
																			else:
																									self.analyse_variable(__UpperCAmelCase						,      F"output[{i}]"    )
							else:
													self.analyse_variable(__UpperCAmelCase						,      "output"    )

							self.save_frame()

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							self.model.apply(self._register_forward_hook    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							module.register_forward_hook(self.forward_hook    )


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  False

							lowerCAmelCase__	 =  True if self.batch_number in self.trace_batch_nums else False
							if trace_mode:
													self.reset_saved_frames()

							if self.total_calls == 0:
													self.batch_start_frame()
							self.total_calls += 1

							# count batch numbers - the very first forward hook of the batch will be called when the
							# batch completes - i.e. it gets called very last - we know this batch has finished
							if module == self.model:
													self.batch_number += 1
													lowerCAmelCase__	 =  True

							self.create_frame(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

							# if last_frame_of_batch:
							#     self.batch_end_frame()

							if trace_mode:
													self.trace_frames()

							if last_frame_of_batch:
													self.batch_start_frame()

							if self.detected_overflow and not trace_mode:
													self.dump_saved_frames()

													# now we can abort, as it's pointless to continue running
													raise ValueError(
													    "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
													    "Please scroll up above this traceback to see the activation values prior to this event."    )

							# abort after certain batch if requested to do so
							if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
													raise ValueError(
													    F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
													    F" `abort_after_batch_num={self.abort_after_batch_num}` arg"    )





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







						lowerCAmelCase__	 =  var.abs()
						return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"


def     _a					(       UpperCamelCase_   :			Optional[Any]      ,				UpperCamelCase_   :			int				)	->				Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__	 =  False
						if torch.isnan(UpperCamelCase_				).any().item():
												lowerCAmelCase__	 =  True
												print(F"{ctx} has nans"				)
						if torch.isinf(UpperCamelCase_				).any().item():
												lowerCAmelCase__	 =  True
												print(F"{ctx} has infs"				)

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												lowerCAmelCase__	 =  var[torch.ge(var.abs()      ,				100				)]
												if naaa.numel() > 0:
																		print(F"{ctx}:  n100={naaa.numel()}"				)
												lowerCAmelCase__	 =  var[torch.ge(var.abs()      ,				1_000				)]
												if naaaa.numel() > 0:
																		print(F"{ctx}: n1000={naaaa.numel()}"				)
												lowerCAmelCase__	 =  var[torch.ge(var.abs()      ,				10_000				)]
												if naaaaa.numel() > 0:
																		print(F"{ctx}: n10000={naaaaa.numel()}"				)

						if 0:
												print(F"min={var.min():9.2e} max={var.max():9.2e}"				)

						if 0:
												print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"				)

						return detected





class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""underflow_overflow"""
	a_             ="""tpu_metrics_debug"""














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


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


def     _a					(       UpperCamelCase_   :			str				)	->				CheckpointState:
						"""simple docstring"""







						print(F"Reading saved model from {model_file}"				)
						lowerCAmelCase__	 =  torch.load(UpperCamelCase_      ,				map_location=lambda UpperCamelCase_      ,				UpperCamelCase_				: default_restore_location(UpperCamelCase_      ,				"cpu"				)				)
						return CheckpointState(**UpperCamelCase_				)


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  src_file

	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							raise NotImplementedError


	@staticmethod
	def 						UpperCAmelCase					(							__UpperCAmelCase						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   "DPRState":







							'''simple docstring'''





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





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"    )[0]    )    )
							print(F"Loading DPR biencoder from {self.src_file}"    )
							lowerCAmelCase__	 =  load_states_from_checkpoint(self.src_file    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  model.ctx_encoder, "ctx_model."
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							lowerCAmelCase__	 =  {"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(__UpperCAmelCase    ):
																			lowerCAmelCase__	 =  key[len(__UpperCAmelCase    ) :]
																			if not key.startswith("encode_proj."    ):
																									lowerCAmelCase__	 =  "bert_model." + key
																			lowerCAmelCase__	 =  value
							encoder.load_state_dict(__UpperCAmelCase    )
							return model





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"    )[0]    )    )
							print(F"Loading DPR biencoder from {self.src_file}"    )
							lowerCAmelCase__	 =  load_states_from_checkpoint(self.src_file    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  model.question_encoder, "question_model."
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							lowerCAmelCase__	 =  {"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(__UpperCAmelCase    ):
																			lowerCAmelCase__	 =  key[len(__UpperCAmelCase    ) :]
																			if not key.startswith("encode_proj."    ):
																									lowerCAmelCase__	 =  "bert_model." + key
																			lowerCAmelCase__	 =  value
							encoder.load_state_dict(__UpperCAmelCase    )
							return model





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"    )[0]    )    )
							print(F"Loading DPR reader from {self.src_file}"    )
							lowerCAmelCase__	 =  load_states_from_checkpoint(self.src_file    )
							# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
							lowerCAmelCase__	 =  {
							    "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__	 =  "encoder.bert_model." + key[len("encoder."    ) :]
													lowerCAmelCase__	 =  value
							model.span_predictor.load_state_dict(__UpperCAmelCase    )
							return model





def     _a					(       UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Path      ,				UpperCamelCase_   :			Path				)	->				Any:
						"""simple docstring"""







						lowerCAmelCase__	 =  Path(UpperCamelCase_				)
						dest_dir.mkdir(exist_ok=UpperCamelCase_				)

						lowerCAmelCase__	 =  DPRState.from_type(UpperCamelCase_      ,				src_file=UpperCamelCase_				)
						lowerCAmelCase__	 =  dpr_state.load_dpr_model()
						model.save_pretrained(UpperCamelCase_				)
						model.from_pretrained(UpperCamelCase_				)  # sanity check


if __name__ == "__main__":
	a_							=      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.''')
	a_							=      parser.parse_args()

	a_							=      Path(args.src)
	a_							=      F"converted-{src_file.name}" if args.dest is None else args.dest
	a_							=      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)






