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


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

def 	__lowerCamelCase							(				__a    : str		)      ->       CheckpointState:
	print(f'''Reading saved model from {model_file}'''		)
	_lowercase   =torch.load(__a      ,							map_location=lambda __a      ,							__a		: default_restore_location(__a      ,							"cpu"		)		)
	return CheckpointState(**__a		)
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ):
		_lowercase   =src_file


	def __lowerCAmelCase      (   self       ):
		raise NotImplementedError


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

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



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

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



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

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



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

def 	__lowerCamelCase							(				__a    : str      ,							__a    : Path      ,							__a    : Path		)      ->       int:
	_lowercase   =Path(__a		)
	dest_dir.mkdir(exist_ok=__a		)

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


if __name__ == "__main__":
						lowerCAmelCase__  	=      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.")
						lowerCAmelCase__  	=      parser.parse_args()

						lowerCAmelCase__  	=      Path(args.src)
						lowerCAmelCase__  	=      F'''converted-{src_file.name}''' if args.dest is None else args.dest
						lowerCAmelCase__  	=      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 bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

            a_     :  Any				      =     0
            for idx in self.sampler:
                 a_     :  str				      =     self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase_     )
                 samples_per_group[group_id].append(lowerCamelCase_     )
                 if len(buffer_per_group[group_id]     ) == self.batch_size:
                      yield buffer_per_group[group_id]  # TODO
                      num_batches += 1
                      del buffer_per_group[group_id]
                 assert len(buffer_per_group[group_id]     ) < self.batch_size

            # now we have run out of elements that satisfy
            # the group criteria, let's return the remaining
            # elements so that the size of the sampler is
            # deterministic
            a_     :  Optional[Any]				      =     len(self     )
            a_     :  Any				      =     expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 a_     :  str				      =     []
                 for group_id, idxs in sorted(buffer_per_group.items()   ,				key=lambda lowerCamelCase_     : x[0]     ):
                      batch_idx.extend(lowerCamelCase_     )
                      if len(lowerCamelCase_     ) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           a_     :  Union[str, Any]				      =     batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(lowerCamelCase_     ) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0

       def __len__( self   :  List[Any]     ):
            return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size



