# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import sys
import paddle.profiler as profiler

# A global variable to record the number of calling times for profiler
# functions. It is used to specify the tracing range of training steps.
_profiler_step_id = 0

# A global variable to avoid parsing from string every time.
_profiler_options = None
_prof = None

class ProfilerOptions(object):
    '''
    Use a string to initialize a ProfilerOptions.
    The string should be in the format: "key1=value1;key2=value;key3=value3".
    For example:
      "profile_path=model.profile"
      "batch_range=[50, 60]; profile_path=model.profile"
      "batch_range=[50, 60]; tracer_option=OpDetail; profile_path=model.profile"

    ProfilerOptions supports following key-value pair:
      batch_range      - a integer list, e.g. [100, 110].
      state            - a string, the optional values are 'CPU', 'GPU' or 'All'. 
      sorted_key       - a string, the optional values are 'calls', 'total',
                         'max', 'min' or 'ave.
      tracer_option    - a string, the optional values are 'Default', 'OpDetail',
                         'AllOpDetail'.
      profile_path     - a string, the path to save the serialized profile data,
                         which can be used to generate a timeline.
      exit_on_finished - a boolean.
    '''

    def __init__(self, options_str):
        assert isinstance(options_str, str)

        self._options = {
            'batch_range': [10, 20],
            'state': 'All',
            'sorted_key': 'total',
            'tracer_option': 'Default',
            'profile_path': '/tmp/profile',
            'exit_on_finished': True,
            'timer_only': True
        }
        self._parse_from_string(options_str)

    def _parse_from_string(self, options_str):
        for kv in options_str.replace(' ', '').split(';'):
            key, value = kv.split('=')
            if key == 'batch_range':
                value_list = value.replace('[', '').replace(']', '').split(',')
                value_list = list(map(int, value_list))
                if len(value_list) >= 2 and value_list[0] >= 0 and value_list[
                        1] > value_list[0]:
                    self._options[key] = value_list
            elif key == 'exit_on_finished':
                self._options[key] = value.lower() in ("yes", "true", "t", "1")
            elif key in [
                    'state', 'sorted_key', 'tracer_option', 'profile_path'
            ]:
                self._options[key] = value
            elif key == 'timer_only':
                self._options[key] = value

    def __getitem__(self, name):
        if self._options.get(name, None) is None:
            raise ValueError(
                "ProfilerOptions does not have an option named %s." % name)
        return self._options[name]


def add_profiler_step(options_str=None):
    '''
    Enable the operator-level timing using PaddlePaddle's profiler.
    The profiler uses a independent variable to count the profiler steps.
    One call of this function is treated as a profiler step.
    Args:
      profiler_options - a string to initialize the ProfilerOptions.
                         Default is None, and the profiler is disabled.
    '''
    if options_str is None:
        return

    global _prof 
    global _profiler_step_id
    global _profiler_options

    if _profiler_options is None:
        _profiler_options = ProfilerOptions(options_str)
    # profile : https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/performance_improving/profiling_model.html#chakanxingnengshujudetongjibiaodan
    # timer_only = True  only the model's throughput and time overhead are displayed
    # timer_only = False calling summary can print a statistical form that presents performance data from different perspectives.
    # timer_only = False the output Timeline information can be found in the profiler_log directory
    if _prof is None:
        _timer_only = str(_profiler_options['timer_only']) == str(True)
        _prof = profiler.Profiler(
                   scheduler = (_profiler_options['batch_range'][0], _profiler_options['batch_range'][1]),
                   on_trace_ready = profiler.export_chrome_tracing('./profiler_log'),
                   timer_only = _timer_only)
        _prof.start()
    else:
        _prof.step()
        
    if _profiler_step_id == _profiler_options['batch_range'][1]:
        _prof.stop()
        _prof.summary(
             op_detail=True,
             thread_sep=False,
             time_unit='ms')
        _prof = None
        if _profiler_options['exit_on_finished']:
            sys.exit(0)

    _profiler_step_id += 1
