






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler


class    _lowerCAmelCase			:



			"""simple docstring"""



			def __init__(      self    :    int			,		UpperCamelCase__    :    int):






										'''simple docstring'''







										if isinstance(UpperCamelCase__			,		UpperCamelCase__):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	snake_case__	  =       deepcopy(UpperCamelCase__)
										elif os.path.exists(UpperCamelCase__):
																	with io.open(UpperCamelCase__			,		"""r"""			,		encoding="""utf-8""") as f:
																								snake_case__	  =       json.load(UpperCamelCase__)
										else:
																	try:
																								snake_case__	  =       baseaa.urlsafe_baadecode(UpperCamelCase__).decode("""utf-8""")
																								snake_case__	  =       json.loads(UpperCamelCase__)
																	except (UnicodeDecodeError, AttributeError, ValueError):
																								raise ValueError(
																								    F'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''')

										snake_case__	  =       config

										self.set_stage_and_offload()



			def 					__magic_name__							(      self    :    List[str]):






										'''simple docstring'''







										snake_case__	  =       self.get_value("""zero_optimization.stage"""			,		-1)

										# offload
										snake_case__	  =       False
										if self.is_zeroa() or self.is_zeroa():
																	snake_case__	  =       set(["""cpu""", """nvme"""])
																	snake_case__	  =       set(
																	    [
																	        self.get_value("""zero_optimization.offload_optimizer.device"""),
																	        self.get_value("""zero_optimization.offload_param.device"""),
																	    ])
																	if len(offload_devices & offload_devices_valid) > 0:
																								snake_case__	  =       True



			def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Any):






										'''simple docstring'''







										snake_case__	  =       self.config

										# find the config node of interest if it exists
										snake_case__	  =       ds_key_long.split(""".""")
										snake_case__	  =       nodes.pop()
										for node in nodes:
																	snake_case__	  =       config.get(UpperCamelCase__)
																	if config is None:
																								return None, ds_key

										return config, ds_key



			def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int=None):






										'''simple docstring'''







										snake_case__  , snake_case__	  =       self.find_config_node(UpperCamelCase__)
										if config is None:
																	return default
										return config.get(UpperCamelCase__			,		UpperCamelCase__)



			def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    List[Any]=False):






										'''simple docstring'''







										snake_case__	  =       self.config

										# find the config node of interest if it exists
										snake_case__	  =       ds_key_long.split(""".""")
										for node in nodes:
																	snake_case__	  =       config
																	snake_case__	  =       config.get(UpperCamelCase__)
																	if config is None:
																								if must_exist:
																															raise ValueError(F'''Can\'t find {ds_key_long} entry in the config: {self.config}''')
																								else:
																															return

        # if found remove it
										if parent_config is not None:
																	parent_config.pop(UpperCamelCase__)



			def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    List[str]):






										'''simple docstring'''







										snake_case__	  =       self.get_value(UpperCamelCase__)
										return False if value is None else bool(UpperCamelCase__)



			def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Optional[Any]):






										'''simple docstring'''







										snake_case__	  =       self.get_value(UpperCamelCase__)
										return False if value is None else not bool(UpperCamelCase__)



			def 					__magic_name__							(      self    :    List[Any]):






										'''simple docstring'''







										return self._stage == 2



			def 					__magic_name__							(      self    :    Tuple):






										'''simple docstring'''







										return self._stage == 3



			def 					__magic_name__							(      self    :    str):






										'''simple docstring'''







										return self._offload


class    _lowerCAmelCase			:



			"""simple docstring"""



			def __init__(      self    :    List[str]			,		UpperCamelCase__    :    Any):






										'''simple docstring'''







										snake_case__	  =       engine



			def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    Any			,		**UpperCamelCase__    :    List[Any]):






										'''simple docstring'''







										self.engine.backward(UpperCamelCase__			,		**UpperCamelCase__)

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.


class    _lowerCAmelCase			(		lowercase_      ):



			"""simple docstring"""



			def __init__(      self    :    int			,		UpperCamelCase__    :    str):






										'''simple docstring'''







										super().__init__(UpperCamelCase__			,		device_placement=UpperCamelCase__			,		scaler=UpperCamelCase__)
										snake_case__	  =       hasattr(self.optimizer			,		"""overflow""")



			def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Union[str, Any]=None):






										'''simple docstring'''







										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



			def 					__magic_name__							(      self    :    Union[str, Any]):






										'''simple docstring'''







										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



			@property
			def 					__magic_name__							(      self    :    List[Any]):






										'''simple docstring'''







										if self.__has_overflow__:
																	return self.optimizer.overflow
										return False


class    _lowerCAmelCase			(		lowercase_      ):



			"""simple docstring"""



			def __init__(      self    :    Any			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    str):






										'''simple docstring'''







										super().__init__(UpperCamelCase__			,		UpperCamelCase__)



			def 					__magic_name__							(      self    :    Dict):






										'''simple docstring'''







										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


class    _lowerCAmelCase			:



			"""simple docstring"""



			def __init__(      self    :    Tuple			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Dict=0.0_01			,		UpperCamelCase__    :    Any=0			,		**UpperCamelCase__    :    List[Any]):






										'''simple docstring'''







										snake_case__	  =       params
										snake_case__	  =       lr
										snake_case__	  =       weight_decay
										snake_case__	  =       kwargs


class    _lowerCAmelCase			:



			"""simple docstring"""



			def __init__(      self    :    List[Any]			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    List[str]=None			,		UpperCamelCase__    :    Dict=0			,		**UpperCamelCase__    :    int):






										'''simple docstring'''







										snake_case__	  =       optimizer
										snake_case__	  =       total_num_steps
										snake_case__	  =       warmup_num_steps
										snake_case__	  =       kwargs








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__       =     logging.getLogger()

def _UpperCAmelCase   (	):
							snake_case__	  =       argparse.ArgumentParser()
							parser.add_argument("""-f""" )
							snake_case__	  =       parser.parse_args()
							return args.f



class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       logging.StreamHandler(sys.stdout)
											logger.addHandler(UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       get_gpu_count()

											if n_gpu > 1:
																		pass
																		# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
																		# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
																		# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
																		# cmd = [sys.executable] + distributed_args + args
																		# execute_subprocess_async(cmd, env=self.get_env())
																		# XXX: test the results - need to save them first into .json file
											else:
																		args.insert(0			,		"""run_glue_deebert.py""")
																		with patch.object(UpperCamelCase__			,		"""argv"""			,		UpperCamelCase__):
																									snake_case__	  =       run_glue_deebert.main()
																									for value in result.values():
																																self.assertGreaterEqual(UpperCamelCase__			,		0.6_66)



				@slow
				@require_torch_non_multi_gpu
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
											self.run_and_check(UpperCamelCase__)

											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
											self.run_and_check(UpperCamelCase__)

											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
											self.run_and_check(UpperCamelCase__)
