





"""simple docstring"""




import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm





def 			a_							(				_lowercase			,		_lowercase  ):
						if isinstance(_lowercase			,		(list, tuple)  ):
												return honor_type(_lowercase			,		(move_to_device(_lowercase			,		_lowercase  ) for t in state)  )
						elif isinstance(_lowercase			,		_lowercase  ):
												return type(_lowercase  )({k: move_to_device(_lowercase			,		_lowercase  ) for k, v in state.items()}  )
						elif isinstance(_lowercase			,		torch.Tensor  ):
												return state.to(_lowercase  )
						return state






class 					_a		( torch.optim.Optimizer							):






				def __init__(					self						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       List[Any]=True,					lowerCAmelCase__						:       Optional[int]=None				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       optimizer
										_UpperCamelCase     :   Optional[Any]      =       scaler
										_UpperCamelCase     :   Tuple      =       AcceleratorState()
										_UpperCamelCase     :   Dict      =       GradientState()
										_UpperCamelCase     :   Tuple      =       device_placement
										_UpperCamelCase     :   Dict      =       False
										_UpperCamelCase     :   Tuple      =       None

										# Handle device placement
										if device_placement:
																_UpperCamelCase     :   Optional[Any]      =       self.optimizer.state_dict()
																if self.accelerator_state.distributed_type == DistributedType.TPU:
																						xm.send_cpu_data_to_device(lowerCAmelCase__,					self.accelerator_state.device				)
																else:
																						_UpperCamelCase     :   Dict      =       move_to_device(lowerCAmelCase__,					self.accelerator_state.device				)
																self.optimizer.load_state_dict(lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       int				)      -> List[Any]:




										'''simple docstring'''



										return self.optimizer.state






				@state.setter
				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       Tuple				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       state






				@property
				def 							snake_case      (					self						:       Optional[int]				)      -> List[str]:




										'''simple docstring'''



										return self.optimizer.param_groups






				@param_groups.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       param_groups






				@property
				def 							snake_case      (					self						:       Any				)      -> Any:




										'''simple docstring'''



										return self.optimizer.defaults






				@defaults.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Tuple				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       defaults






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Any				)      -> Optional[Any]:




										'''simple docstring'''



										self.optimizer.add_param_group(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Optional[Any]				)      -> Optional[Any]:




										'''simple docstring'''



										if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
																xm.send_cpu_data_to_device(lowerCAmelCase__,					self.accelerator_state.device				)
										self.optimizer.load_state_dict(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> str:




										'''simple docstring'''



										return self.optimizer.state_dict()






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[str]=None				)      -> Any:




										'''simple docstring'''



										if self.gradient_state.sync_gradients:
																_UpperCamelCase     :   Optional[Any]      =       '''set_to_none''' in inspect.signature(self.optimizer.zero_grad				).parameters
																if accept_arg:
																						if set_to_none is None:
																												_UpperCamelCase     :   List[Any]      =       False
																						self.optimizer.zero_grad(set_to_none=lowerCAmelCase__				)
																else:
																						if set_to_none is not None:
																												raise ValueError('''`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'''				)
																						self.optimizer.zero_grad()






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       str=None				)      -> Dict:




										'''simple docstring'''



										if self.gradient_state.sync_gradients:
																if self.accelerator_state.distributed_type == DistributedType.TPU:
																						_UpperCamelCase     :   Any      =       {'''closure''': closure} if closure is not None else {}
																						xm.optimizer_step(self.optimizer,					optimizer_args=lowerCAmelCase__				)
																elif self.scaler is not None:
																						_UpperCamelCase     :   Dict      =       False
																						if self._last_scale is None:
																												# `get_scale` is an async operation requiring full synchronization
																												# on CPU and GPUs before finishing. As a result, we store away
																												# the prior one to reduce the call overhead
																												_UpperCamelCase     :   Dict      =       self.scaler.get_scale()
																												_UpperCamelCase     :   int      =       True
																						self.scaler.step(self.optimizer,					lowerCAmelCase__				)
																						self.scaler.update()
																						_UpperCamelCase     :   Union[str, Any]      =       self.scaler.get_scale()
																						if not new_scale:
																												# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																												_UpperCamelCase     :   List[str]      =       scale_after < self._last_scale
																						_UpperCamelCase     :   Union[str, Any]      =       scale_after

																else:
																						self.optimizer.step(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       Optional[int]				)      -> List[str]:




										'''simple docstring'''



										for param_group in self.optimizer.param_groups:
																_UpperCamelCase     :   Optional[int]      =       [parameters_map.get(lowerCAmelCase__,					lowerCAmelCase__				) for p in param_group['''params''']]






				@property
				def 							snake_case      (					self						:       Tuple				)      -> str:




										'''simple docstring'''



										warnings.warn(
										    '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
										    '''`optimizer.step_was_skipped` instead.''',					lowerCAmelCase__,					)
										return self._is_overflow






				@property
				def 							snake_case      (					self						:       int				)      -> int:




										'''simple docstring'''



										return self._is_overflow






				def __getstate__(					self						:       int				)      -> Tuple:




										'''simple docstring'''



										return self.__dict__.copy()






				def __setstate__(					self						:       Any,					lowerCAmelCase__						:       List[Any]				)      -> List[str]:




										'''simple docstring'''



										self.__dict__.update(lowerCAmelCase__				)






"""simple docstring"""




import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class      __UpperCamelCase					(  unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       EsmTokenizer


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()
							A     =   tempfile.mkdtemp()
							# fmt: off
							A     =   ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
							# fmt: on
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"])
							with open(self.vocab_file	,	"w"	,	encoding="utf-8") as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	**__SCREAMING_SNAKE_CASE					:						str):
							return [self.get_tokenizer(**__SCREAMING_SNAKE_CASE)]


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							return self.tokenizer_class.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   tokenizer.tokenize("LAGVS")
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	["L", "A", "G", "V", "S"])
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)	,	[4, 5, 6, 7, 8])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   "LAGVS"
							self.assertListEqual(tokenizer.encode(__SCREAMING_SNAKE_CASE)	,	[0, 4, 5, 6, 7, 8, 2])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]])


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]])


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE)
							for tokenizer in tokenizers:
												with self.subTest(F"""{tokenizer.__class__.__name__}"""):
																	A     =   "<unk>"
																	A     =   "<mask>"

																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)
																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)

																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1)
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1)
																	self.assertEqual(token_a[0]	,	__SCREAMING_SNAKE_CASE)
																	self.assertEqual(token_a[0]	,	__SCREAMING_SNAKE_CASE)





