



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       A_   (						__a  :					str		):

				"""simple docstring"""
				print(F'''Reading saved model from {model_file}'''		)
				a__					   =		torch.load(__a			,		map_location=lambda __a			,		__a		: default_restore_location(__a			,		"""cpu"""		)		)
				return CheckpointState(**__a		)

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_		):
											a__					   =		src_file


							def _a   (				self		):
											raise NotImplementedError







							@staticmethod
							def _a   (				a_	,	*a_	,	**a_		):
											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   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






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




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






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




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






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




def       A_   (						__a  :					str			,		__a  :					Path			,		__a  :					Path		):

				"""simple docstring"""
				a__					   =		Path(__a		)
				dest_dir.mkdir(exist_ok=__a		)

				a__					   =		DPRState.from_type(__a			,		src_file=__a		)
				a__					   =		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)






from __future__ import annotations

def       A_   (						__a  :					int		):

				"""simple docstring"""
				a__					   =		{1}
				a__					   =		[1]
				for _ in range(2			,		depth + 1		):
								a__					   =		[*previous_coefficients, 0]
								a__					   =		[0, *previous_coefficients]
								a__					   =		[]
								for x, y in zip(__a			,		__a		):
												coefficients.add(x + y		)
												previous_coefficients.append(x + y		)
				return coefficients

def       A_   (						__a  :					set[int]		):

				"""simple docstring"""
				a__					   =		set()
				for number in unique_coefficients:
								a__					   =		2
								a__					   =		number
								while divisor**2 <= copy_number:
												a__					   =		0
												while copy_number % divisor == 0:
																copy_number //= divisor
																multiplicity += 1
												if multiplicity >= 2:
																non_squarefrees.add(__a		)
																break
												divisor += 1

				return unique_coefficients.difference(__a		)

def       A_   (						__a  :					int = 51		):

				"""simple docstring"""
				a__					   =		get_pascal_triangle_unique_coefficients(__a		)
				a__					   =		get_squarefrees(__a		)
				return sum(__a		)


if __name__ == "__main__":
	print(f"""{solution() = }""")

