





import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A					:    List[str]   =  logging.get_logger(__name__)

_A					:    int   =  {"vocab_file": "vocab.txt"}

_A					:    List[Any]   =  {
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

_A					:    int   =  {
    "facebook/esm2_t6_8M_UR50D": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}



def        _a						(    UpperCAmelCase     )				->				Optional[Any]:







   """simple docstring"""
   with open(UpperCAmelCase__			,  '''r'''     ) as f:
      lowerCamelCase__   :						Optional[Any]					   =							f.read().splitlines()
      return [l.strip() for l in lines]




class    __SCREAMING_SNAKE_CASE			( _UpperCAmelCase					):
       _UpperCAmelCase      :  Any     =    VOCAB_FILES_NAMES
       _UpperCAmelCase      :  List[str]     =    PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase      :  List[str]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase      :  List[str]     =    ["input_ids", "attention_mask"]







       def __init__(      self     :	Dict		,					A     :	List[str]		,					A     :	Any="<unk>"		,					A     :	str="<cls>"		,					A     :	Dict="<pad>"		,					A     :	int="<mask>"		,					A     :	Optional[Any]="<eos>"		,					**A     :	Any		,					)   ->List[Any]:
          super().__init__(**lowercase_			)
          lowerCamelCase__   :						List[Any]					   =							load_vocab_file(lowercase_			)
          lowerCamelCase__   :						Union[str, Any]					   =							dict(enumerate(self.all_tokens			)			)
          lowerCamelCase__   :						Tuple					   =							{tok: ind for ind, tok in enumerate(self.all_tokens			)}
          lowerCamelCase__   :						Optional[int]					   =							unk_token
          lowerCamelCase__   :						List[str]					   =							cls_token
          lowerCamelCase__   :						Dict					   =							pad_token
          lowerCamelCase__   :						Optional[Any]					   =							mask_token
          lowerCamelCase__   :						List[str]					   =							eos_token
          lowerCamelCase__   :						List[Any]					   =							self.all_tokens
          self._create_trie(self.unique_no_split_tokens			)







       def 						__lowerCamelCase    (      self     :	Optional[int]		,					A     :	int			)   ->Optional[Any]:
          return self._id_to_token.get(lowercase_		,					self.unk_token			)







       def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	str			)   ->Any:
          return self._token_to_id.get(lowercase_		,					self._token_to_id.get(self.unk_token			)			)







       def 						__lowerCamelCase    (      self     :	Optional[Any]		,					A     :	Tuple		,					**A     :	List[str]			)   ->Optional[Any]:
          return text.split()







       def 						__lowerCamelCase    (      self     :	str		,					A     :	Any=False			)   ->int:
          return len(self._id_to_token			)







       def 						__lowerCamelCase    (      self     :	List[Any]			)   ->Optional[int]:
          return {token: i for i, token in enumerate(self.all_tokens			)}







       def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	str			)   ->Optional[Any]:
          return self._token_to_id.get(lowercase_		,					self._token_to_id.get(self.unk_token			)			)







       def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	int			)   ->Union[str, Any]:
          return self._id_to_token.get(lowercase_		,					self.unk_token			)







       def 						__lowerCamelCase    (      self     :	int		,					A     :	List[int]		,					A     :	Optional[List[int]] = None			)   ->Any:
          lowerCamelCase__   :						Any					   =							[self.cls_token_id]
          lowerCamelCase__   :						List[Any]					   =							[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
             if self.eos_token_id is None:
                return cls + token_ids_a
             else:
                return cls + token_ids_a + sep
          elif self.eos_token_id is None:
             raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''			)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token







       def 						__lowerCamelCase    (      self     :	List[Any]		,					A     :	List		,					A     :	Optional[List] = None		,					A     :	bool = False			)   ->Tuple:
          if already_has_special_tokens:
             if token_ids_a is not None:
                raise ValueError(
                    '''You should not supply a second sequence if the provided sequence of '''
                    '''ids is already formatted with special tokens for the model.'''			)

             return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowerCamelCase__   :						Optional[Any]					   =							[1] + ([0] * len(lowercase_			)) + [1]
          if token_ids_a is not None:
             mask += [0] * len(lowercase_			) + [1]
          return mask







       def 						__lowerCamelCase    (      self     :	Optional[Any]		,					A     :	Dict		,					A     :	Optional[Any]			)   ->Tuple:
          lowerCamelCase__   :						Dict					   =							os.path.join(lowercase_		,					(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''			)
          with open(lowercase_		,					'''w'''			) as f:
             f.write('''\n'''.join(self.all_tokens			)			)
          return (vocab_file,)







       @property
       def 						__lowerCamelCase    (      self     :	Tuple			)   ->Dict:
          return self.get_vocab_size(with_added_tokens=lowercase_			)







       def 						__lowerCamelCase    (      self     :	Tuple		,					A     :	Union[List[str], List[AddedToken]]		,					A     :	bool = False			)   ->Dict:
          return super()._add_tokens(lowercase_		,					special_tokens=lowercase_			)




'''simple docstring'''




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

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





class     __magic_name__						:







			def __init__(	self				:  str				,			lowercase_				:  Dict    ):
										if isinstance(lowercase_				,			lowercase_    ):
																	# 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
																	lowercase_					:			List[Any]										=       deepcopy(lowercase_    )
										elif os.path.exists(lowercase_    ):
																	with io.open(lowercase_				,			"""r"""				,			encoding="""utf-8"""    ) as f:
																								lowercase_					:			Union[str, Any]										=       json.load(lowercase_    )
										else:
																	try:
																								lowercase_					:			int										=       baseaa.urlsafe_baadecode(lowercase_    ).decode("""utf-8"""    )
																								lowercase_					:			str										=       json.loads(lowercase_    )
																	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}'''    )

										lowercase_					:			Any										=       config

										self.set_stage_and_offload()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										# zero stage - this is done as early as possible, before model is created, to allow
										# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
										# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
										lowercase_					:			Tuple										=       self.get_value("""zero_optimization.stage"""				,			-1    )

										# offload
										lowercase_					:			str										=       False
										if self.is_zeroa() or self.is_zeroa():
																	lowercase_					:			Dict										=       set(["""cpu""", """nvme"""]    )
																	lowercase_					:			List[Any]										=       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:
																								lowercase_					:			Tuple										=       True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Tuple										=       ds_key_long.split("""."""    )
										lowercase_					:			Union[str, Any]										=       nodes.pop()
										for node in nodes:
																	lowercase_					:			List[str]										=       config.get(lowercase_    )
																	if config is None:
																								return None, ds_key

										return config, ds_key







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]=None    ):
										lowercase_						,						lowercase_					:			List[Any]										=       self.find_config_node(lowercase_    )
										if config is None:
																	return default
										return config.get(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int=False    ):
										lowercase_					:			int										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Dict										=       ds_key_long.split("""."""    )
										for node in nodes:
																	lowercase_					:			List[Any]										=       config
																	lowercase_					:			Dict										=       config.get(lowercase_    )
																	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(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Tuple    ):
										lowercase_					:			str										=       self.get_value(lowercase_    )
										return False if value is None else bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Union[str, Any]										=       self.get_value(lowercase_    )
										return False if value is None else not bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self._stage == 2







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										return self._stage == 3







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return self._offload





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Any										=       engine







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			**lowercase_				:  str    ):
										# runs backpropagation and handles mixed precision
										self.engine.backward(lowercase_				,			**lowercase_    )

										# 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     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			device_placement=lowercase_				,			scaler=lowercase_    )
										lowercase_					:			Any										=       hasattr(self.optimizer				,			"""overflow"""    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple=None    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										if self.__has_overflow__:
																	return self.optimizer.overflow
										return False





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Dict				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]=0.0_01				,			lowercase_				:  List[str]=0				,			**lowercase_				:  List[Any]    ):
										lowercase_					:			str										=       params
										lowercase_					:			List[Any]										=       lr
										lowercase_					:			int										=       weight_decay
										lowercase_					:			Union[str, Any]										=       kwargs





class     __magic_name__						:







			def __init__(	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]=None				,			lowercase_				:  int=0				,			**lowercase_				:  int    ):
										lowercase_					:			Union[str, Any]										=       optimizer
										lowercase_					:			List[str]										=       total_num_steps
										lowercase_					:			Dict										=       warmup_num_steps
										lowercase_					:			Dict										=       kwargs





