Problem:


Score : 300 points

Problem Statement

You are given a string S consisting of lowercase English letters.
Another string T is initially empty.
Determine whether it is possible to obtain S = T by performing the following operation an arbitrary number of times:

Append one of the following at the end of T: dream, dreamer, erase and eraser.

Constraints

1≦|S|≦10^5

S consists of lowercase English letters.

Input

The input is given from Standard Input in the following format:

S

Output

If it is possible to obtain S = T, print YES. Otherwise, print NO.

Sample Input 1

erasedream

Sample Output 1

YES

Append erase and dream at the end of T in this order, to obtain S = T.

Sample Input 2

dreameraser

Sample Output 2

YES

Append dream and eraser at the end of T in this order, to obtain S = T.

Sample Input 3

dreamerer

Sample Output 3

NO



Sample input:
erasedream


Sample output:
YES

Submission status: Accepted

Solution code:
import subprocess
import gzip, base64, os
import stat

gz = b'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'
bin = gzip.decompress(base64.b64decode(gz))
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
with open(pymain, "wb") as f:
    f.write(bin)
os.chmod(pymain, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
if p == "":
    pymain = "./" + pymain
subprocess.call(pymain)


# compiled by pythran
'''
#pythran export main2()
def main2():
    s = open('/dev/stdin').readline().replace('eraser', '').replace('erase', '').replace('dreamer', '').replace('dream', '')
    if s == '\n':
        print('YES')
    else:
        print('NO')
'''