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 numpy as np
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=5       ):
		_lowercase   =depth
		_lowercase   =0
		_lowercase   =None
		_lowercase   =None
		_lowercase   =min_leaf_size
		_lowercase   =None


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if labels.ndim != 1:
			print("Error: Input labels must be one dimensional"       )

		return np.mean((labels - prediction) ** 2       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if x.ndim != 1:
			print("Error: Input data set must be one dimensional"       )
			return
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			print("Error: X and y have different lengths"       )
			return
		if y.ndim != 1:
			print("Error: Data set labels must be one dimensional"       )
			return

		if len(lowerCAmelCase_       ) < 2 * self.min_leaf_size:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		if self.depth == 1:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		_lowercase   =0
		_lowercase   =self.mean_squared_error(lowerCAmelCase_       ,  np.mean(lowerCAmelCase_       )       ) * 2
		for i in range(len(lowerCAmelCase_       )       ):
			if len(x[:i]       ) < self.min_leaf_size:
				continue
			elif len(x[i:]       ) < self.min_leaf_size:
				continue
			else:
				_lowercase   =self.mean_squared_error(x[:i]       ,  np.mean(y[:i]       )       )
				_lowercase   =self.mean_squared_error(x[i:]       ,  np.mean(y[i:]       )       )
				_lowercase   =error_left + error_right
				if error < min_error:
					_lowercase   =i
					_lowercase   =error

		if best_split != 0:
			_lowercase   =x[:best_split]
			_lowercase   =y[:best_split]
			_lowercase   =x[best_split:]
			_lowercase   =y[best_split:]

			_lowercase   =x[best_split]
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			self.left.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
			self.right.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =np.mean(lowerCAmelCase_       )

		return


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		if self.prediction is not None:
			return self.prediction
		elif self.left or self.right is not None:
			if x >= self.decision_boundary:
				return self.right.predict(lowerCAmelCase_       )
			else:
				return self.left.predict(lowerCAmelCase_       )
		else:
			print("Error: Decision tree not yet trained"       )
			return None

class 	_a  :



	"""simple docstring"""



	@staticmethod
	def __lowerCAmelCase      (   lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =float(0       )
		for label in labels:
			squared_error_sum += (label - prediction) ** 2

		return float(squared_error_sum / labels.size       )

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =np.arange(-1.0      ,							1.0      ,							0.005		)
	_lowercase   =np.sin(__a		)

	_lowercase   =DecisionTree(depth=10      ,							min_leaf_size=10		)
	tree.train(__a      ,							__a		)

	_lowercase   =(np.random.rand(10		) * 2) - 1
	_lowercase   =np.array([tree.predict(__a		) for x in test_cases]		)
	_lowercase   =np.mean((predictions - test_cases) ** 2		)

	print("Test values: " + str(__a		)		)
	print("Predictions: " + str(__a		)		)
	print("Average error: " + str(__a		)		)


if __name__ == "__main__":
						main()
						import doctest

						doctest.testmod(name="mean_squarred_error", verbose=True)




