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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#

import math

import torch

from logging import getLogger

logger = getLogger()


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1. + math.erf(x / math.sqrt(2.))) / 2.

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [l, u], then translate to
        # [2l-1, 2u-1].
        tensor.uniform_(2 * l - 1, 2 * u - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
    # type: (Tensor, float, float, float, float) -> Tensor
    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


def apply_masks(x, masks):
    """
    :param x: tensor of shape [B (batch-size), N (num-patches), D (feature-dim)]
    :param masks: list of tensors containing indices of patches in [N] to keep
    """
    all_x = []
    for m in masks:
        mask_keep = m.unsqueeze(-1).repeat(1, 1, x.size(-1))
        all_x += [torch.gather(x, dim=1, index=mask_keep)]
    return torch.cat(all_x, dim=0)


def repeat_interleave_batch(x, B, repeat):
    N = len(x) // B
    x = torch.cat([
        torch.cat([x[i*B:(i+1)*B] for _ in range(repeat)], dim=0)
        for i in range(N)
    ], dim=0)
    return x

	# Copyright (c) Meta Platforms, Inc. and affiliates.
	# All rights reserved.
	#
	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.
	#

	import math

	import torch

	from logging import getLogger

	logger = getLogger()


	def _no_grad_trunc_normal_(tensor, mean, std, a, b):
	# Cut & paste from PyTorch official master until it's in a few official releases - RW
	# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
	def norm_cdf(x):
	# Computes standard normal cumulative distribution function
	return (1. + math.erf(x / math.sqrt(2.))) / 2.

	with torch.no_grad():
	# Values are generated by using a truncated uniform distribution and
	# then using the inverse CDF for the normal distribution.
	# Get upper and lower cdf values
	l = norm_cdf((a - mean) / std)
	u = norm_cdf((b - mean) / std)

	# Uniformly fill tensor with values from [l, u], then translate to
	# [2l-1, 2u-1].
	tensor.uniform_(2 * l - 1, 2 * u - 1)

	# Use inverse cdf transform for normal distribution to get truncated
	# standard normal
	tensor.erfinv_()

	# Transform to proper mean, std
	tensor.mul_(std * math.sqrt(2.))
	tensor.add_(mean)

	# Clamp to ensure it's in the proper range
	tensor.clamp_(min=a, max=b)
	return tensor


	def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
	# type: (Tensor, float, float, float, float) -> Tensor
	return _no_grad_trunc_normal_(tensor, mean, std, a, b)


	def apply_masks(x, masks):
	"""
	:param x: tensor of shape [B (batch-size), N (num-patches), D (feature-dim)]
	:param masks: list of tensors containing indices of patches in [N] to keep
	"""
	all_x = []
	for m in masks:
	mask_keep = m.unsqueeze(-1).repeat(1, 1, x.size(-1))
	all_x += [torch.gather(x, dim=1, index=mask_keep)]
	return torch.cat(all_x, dim=0)


	def repeat_interleave_batch(x, B, repeat):
	N = len(x) // B
	x = torch.cat([
	torch.cat([x[iB:(i+1)B] for _ in range(repeat)], dim=0)
	for i in range(N)
	], dim=0)
	return x