






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]=2						,	__lowerCamelCase    :  Union[str, Any]=True						,	__lowerCamelCase    :  List[str]=False	):
  __UpperCAmelCase							:			List[Any]     =						Accelerator(step_scheduler_with_optimizer=__lowerCamelCase						,	split_batches=__lowerCamelCase	)
  __UpperCAmelCase							:			Union[str, Any]     =						torch.nn.Linear(2						,	4	)
  __UpperCAmelCase							:			Any     =						torch.optim.AdamW(model.parameters()						,	lr=1.0	)
  __UpperCAmelCase							:			int     =						torch.optim.lr_scheduler.OneCycleLR(__lowerCamelCase						,	max_lr=0.0_1						,	steps_per_epoch=2						,	epochs=1	)
  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase							:			Optional[int]     =						accelerator.prepare(__lowerCamelCase						,	__lowerCamelCase						,	__lowerCamelCase	)

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[Any]=2						,	__lowerCamelCase    :  List[Any]=True						,	__lowerCamelCase    :  Dict=False	):
  __UpperCAmelCase							:			Dict     =						Accelerator(step_scheduler_with_optimizer=__lowerCamelCase						,	split_batches=__lowerCamelCase	)
  __UpperCAmelCase							:			Optional[Any]     =						torch.nn.Linear(2						,	4	)
  __UpperCAmelCase							:			Any     =						torch.optim.AdamW(model.parameters()						,	lr=1.0	)
  __UpperCAmelCase							:			Union[str, Any]     =						torch.optim.lr_scheduler.LambdaLR(__lowerCamelCase						,	lr_lambda=lambda __lowerCamelCase	: 1 - n / 10	)
  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase							:			int     =						accelerator.prepare(__lowerCamelCase						,	__lowerCamelCase						,	__lowerCamelCase	)

  # Optimizer has stepped
  __UpperCAmelCase							:			Optional[Any]     =						False
  scheduler.step()
  __UpperCAmelCase							:			Tuple     =						1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

  # Optimizer has not stepped
  __UpperCAmelCase							:			Dict     =						True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    __UpperCAmelCase							:			Tuple     =						1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""






def        lowerCamelCase__   (					__lowerCamelCase    :  int = 2	):
  from transformers import get_linear_schedule_with_warmup

  __UpperCAmelCase							:			Dict     =						[1, 2, 4]
  for num_steps in steps:
    __UpperCAmelCase							:			Union[str, Any]     =						GradientAccumulationPlugin(num_steps=__lowerCamelCase						,	adjust_scheduler=num_steps > 1	)
    __UpperCAmelCase							:			Optional[Any]     =						Accelerator(gradient_accumulation_plugin=__lowerCamelCase	)
    __UpperCAmelCase							:			Optional[Any]     =						torch.nn.Linear(2						,	4	)
    __UpperCAmelCase							:			Tuple     =						torch.optim.AdamW(model.parameters()						,	lr=1_0.0	)
    __UpperCAmelCase							:			Tuple     =						get_linear_schedule_with_warmup(optimizer=__lowerCamelCase						,	num_warmup_steps=0						,	num_training_steps=20	)

    __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase							:			Tuple     =						accelerator.prepare(__lowerCamelCase						,	__lowerCamelCase						,	__lowerCamelCase	)

    for i in range(10 * num_steps	):
      with accelerator.accumulate(__lowerCamelCase	):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
    GradientState._reset_state()







@require_cpu
class 					a					(				unittest.TestCase     ):



      """simple docstring"""


      def 			UpperCAmelCase   (   self      :    Tuple      )							->   List[str]:
        debug_launcher(partial(__lowercase	,   num_processes=1      )	,   num_processes=1      )
        debug_launcher(partial(__lowercase	,   num_processes=1	,   split_batches=__lowercase      )	,   num_processes=1      )
      def 			UpperCAmelCase   (   self      :    Any      )							->   Union[str, Any]:
        debug_launcher(partial(__lowercase	,   num_processes=1      )	,   num_processes=1      )
        debug_launcher(partial(__lowercase	,   num_processes=1	,   split_batches=__lowercase      )	,   num_processes=1      )
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Tuple:
        debug_launcher(partial(__lowercase	,   num_processes=1	,   step_scheduler_with_optimizer=__lowercase      )	,   num_processes=1      )
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   List[str]:
        debug_launcher(partial(__lowercase	,   num_processes=1	,   step_scheduler_with_optimizer=__lowercase      )	,   num_processes=1      )
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Union[str, Any]:
        AcceleratorState._reset_state(__lowercase      )
        debug_launcher(__lowercase      )
        debug_launcher(partial(__lowercase	,   num_processes=1	,   split_batches=__lowercase      )	,   num_processes=1      )
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   Union[str, Any]:
        AcceleratorState._reset_state(__lowercase      )
        debug_launcher(__lowercase      )
        debug_launcher(partial(__lowercase	,   num_processes=1	,   split_batches=__lowercase      )	,   num_processes=1      )
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Optional[int]:
        AcceleratorState._reset_state(__lowercase      )
        debug_launcher(partial(__lowercase	,   step_scheduler_with_optimizer=__lowercase      )      )
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   List[Any]:
        AcceleratorState._reset_state(__lowercase      )
        debug_launcher(partial(__lowercase	,   step_scheduler_with_optimizer=__lowercase      )      )




      @require_huggingface_suite
      def 			UpperCAmelCase   (   self      :    str      )							->   Optional[Any]:
        AcceleratorState._reset_state(__lowercase      )
        debug_launcher(partial(__lowercase	,   num_processes=1      )      )
        debug_launcher(__lowercase      )










# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


a	: Union[str, Any]      =				"Update an existing config file with the latest defaults while maintaining the old configuration."






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  __UpperCAmelCase							:			Optional[Any]     =						args.config_file
  if config_file is None and Path(__lowerCamelCase	).exists():
    __UpperCAmelCase							:			int     =						default_config_file
  elif not Path(__lowerCamelCase	).exists():
    raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""	)
  __UpperCAmelCase							:			Tuple     =						load_config_from_file(__lowerCamelCase	)

  if config_file.endswith(""".json"""	):
    config.to_json_file(__lowerCamelCase	)
  else:
    config.to_yaml_file(__lowerCamelCase	)
  return config_file






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			List[str]     =						parser.add_parser("""update"""						,	parents=__lowerCamelCase						,	help=__lowerCamelCase						,	formatter_class=__lowerCamelCase	)
  parser.add_argument(
      """--config_file"""						,	default=__lowerCamelCase						,	help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )						,	)

  parser.set_defaults(func=__lowerCamelCase	)
  return parser







def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]	):
  __UpperCAmelCase							:			Optional[Any]     =						update_config(__lowerCamelCase	)
  print(f"""Sucessfully updated the configuration file at {config_file}."""	)


