import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

_lowercase:	List[str]       			=		logging.getLogger()






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							{}
							_lowerCAmelCase							=							os.path.join(snake_case     , 'all_results.json'  )
							if os.path.exists(snake_case  ):
														with open(snake_case     , 'r'  ) as f:
																					_lowerCAmelCase							=							json.load(snake_case  )
							else:
														raise ValueError(F'can\'t find {path}'  )
							return results


_lowercase:	Optional[int]       			=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@require_torch_tpu
class        lowerCamelCase__				(   _UpperCamelCase				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										import xla_spawn

										_lowerCAmelCase							=							self.get_auto_remove_tmp_dir()
										_lowerCAmelCase							=							f'\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

										with patch.object(lowercase__  ,  'argv'  ,  lowercase__						):
																	_lowerCAmelCase							=							time()
																	xla_spawn.main()
																	_lowerCAmelCase							=							time()

																	_lowerCAmelCase							=							get_results(lowercase__						)
																	self.assertGreaterEqual(result['eval_accuracy']  ,  0.7_5						)

																	# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
																	self.assertLess(end - start  ,  5_00						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										import xla_spawn

										_lowerCAmelCase							=							"""
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            """.split()
										with patch.object(lowercase__  ,  'argv'  ,  lowercase__						):
																	xla_spawn.main()












import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Dict      	=		'linear'
	__magic_name__   :       Dict      	=		'cosine'
	__magic_name__   :       Optional[int]      	=		'cosine_with_restarts'
	__magic_name__   :       List[str]      	=		'polynomial'
	__magic_name__   :       Any      	=		'constant'
	__magic_name__   :       Union[str, Any]      	=		'constant_with_warmup'
	__magic_name__   :       str      	=		'piecewise_constant'






def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int = -1				)     ->						Optional[Any]:
					return LambdaLR(snake_case		,	lambda snake_case				: 1		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int = -1				)     ->						List[Any]:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1.0		,	snake_case				)				)
										return 1.0

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		str		,	snake_case   :		int = -1				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Optional[Any]             =      {}
					__UpperCamelCase					:	int             =      step_rules.split(""","""				)
					for rule_str in rule_list[:-1]:
										__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      rule_str.split(""":"""				)
										__UpperCamelCase					:	int             =      int(snake_case				)
										__UpperCamelCase					:	Union[str, Any]             =      float(snake_case				)
										__UpperCamelCase					:	Optional[int]             =      value
					__UpperCamelCase					:	Dict             =      float(rule_list[-1]				)

					def create_rules_function(snake_case   :		List[str]		,	snake_case   :		Any				):
										def rule_func(snake_case   :		int				) -> float:
															__UpperCamelCase					:	Any             =      sorted(rules_dict.keys()				)
															for i, sorted_step in enumerate(snake_case				):
																				if steps < sorted_step:
																									return rules_dict[sorted_steps[i]]
															return last_lr_multiple

										return rule_func

					__UpperCamelCase					:	Tuple             =      create_rules_function(snake_case		,	snake_case				)

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		int		,	snake_case   :		Optional[Any]		,	snake_case   :		Union[str, Any]		,	snake_case   :		str=-1				)     ->						str:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										return max(
										    0.0		,	float(num_training_steps - current_step				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		float = 0.5		,	snake_case   :		int = -1				)     ->						List[str]:

					def lr_lambda(snake_case   :		Dict				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	Dict             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * float(snake_case				) * 2.0 * progress				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int = 1		,	snake_case   :		int = -1				)     ->						Tuple:

					def lr_lambda(snake_case   :		Optional[int]				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	List[str]             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										if progress >= 1.0:
															return 0.0
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * ((float(snake_case				) * progress) % 1.0)				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		List[str]		,	snake_case   :		Optional[Any]		,	snake_case   :		Optional[int]		,	snake_case   :		str=1e-7		,	snake_case   :		List[str]=1.0		,	snake_case   :		Dict=-1				)     ->						Tuple:
					__UpperCamelCase					:	Tuple             =      optimizer.defaults["""lr"""]
					if not (lr_init > lr_end):
										raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''				)

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															__UpperCamelCase					:	List[str]             =      lr_init - lr_end
															__UpperCamelCase					:	Any             =      num_training_steps - num_warmup_steps
															__UpperCamelCase					:	List[str]             =      1 - (current_step - num_warmup_steps) / decay_steps
															__UpperCamelCase					:	List[Any]             =      lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)


a__		     =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 				A__ (snake_case   :		Union[str, SchedulerType]		,	snake_case   :		Optimizer		,	snake_case   :		Optional[str] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		int = 1		,	snake_case   :		float = 1.0		,	snake_case   :		int = -1		,	)     ->						Dict:
					__UpperCamelCase					:	List[str]             =      SchedulerType(snake_case				)
					__UpperCamelCase					:	int             =      TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(snake_case		,	last_epoch=snake_case				)

					if name == SchedulerType.PIECEWISE_CONSTANT:
										return schedule_func(snake_case		,	step_rules=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_warmup_steps`
					if num_warmup_steps is None:
										raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''				)

					if name == SchedulerType.CONSTANT_WITH_WARMUP:
										return schedule_func(snake_case		,	num_warmup_steps=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_training_steps`
					if num_training_steps is None:
										raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''				)

					if name == SchedulerType.COSINE_WITH_RESTARTS:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	num_cycles=snake_case		,	last_epoch=snake_case		,	)

					if name == SchedulerType.POLYNOMIAL:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	power=snake_case		,	last_epoch=snake_case		,	)

					return schedule_func(
					    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	last_epoch=snake_case				)






