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import json, copy
from easydict import EasyDict

import torch.nn as nn

class DictX(dict):
    def __getattr__(self, key):
        try:
            return self[key]
        except KeyError as k:
            raise AttributeError(k)

    def __setattr__(self, key, value):
        self[key] = value

    def __delattr__(self, key):
        try:
            del self[key]
        except KeyError as k:
            raise AttributeError(k)

    def __repr__(self):
        return '<DictX ' + dict.__repr__(self) + '>'


def load_hparams(file_path):
    hparams = EasyDict()
    with open(file_path, 'r') as f:
        hparams = json.load(f)
    return hparams


def deleteEncodingLayers(model, num_layers_to_keep):  # must pass in the full bert model
    oldModuleList = model.encoder.layer
    newModuleList = nn.ModuleList()

    # Now iterate over all layers, only keepign only the relevant layers.
    for i in range(num_layers_to_keep):
        newModuleList.append(oldModuleList[i])

    # create a copy of the model, modify it with the new list, and return
    copyOfModel = copy.deepcopy(model)
    copyOfModel.encoder.layer = newModuleList

    return copyOfModel

	import json, copy
	from easydict import EasyDict

	import torch.nn as nn

	class DictX(dict):
	def __getattr__(self, key):
	try:
	return self[key]
	except KeyError as k:
	raise AttributeError(k)

	def __setattr__(self, key, value):
	self[key] = value

	def __delattr__(self, key):
	try:
	del self[key]
	except KeyError as k:
	raise AttributeError(k)

	def __repr__(self):
	return '<DictX ' + dict.__repr__(self) + '>'


	def load_hparams(file_path):
	hparams = EasyDict()
	with open(file_path, 'r') as f:
	hparams = json.load(f)
	return hparams


	def deleteEncodingLayers(model, num_layers_to_keep): # must pass in the full bert model
	oldModuleList = model.encoder.layer
	newModuleList = nn.ModuleList()

	# Now iterate over all layers, only keepign only the relevant layers.
	for i in range(num_layers_to_keep):
	newModuleList.append(oldModuleList[i])

	# create a copy of the model, modify it with the new list, and return
	copyOfModel = copy.deepcopy(model)
	copyOfModel.encoder.layer = newModuleList

	return copyOfModel