

from abc import ABC, abstractmethod
from typing import List, Optional



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self			):
						# test for the above condition
						self.test()






					def   __A			( self			):
						A__							:    List[str]        =		0
						A__							:    List[str]        =		False
						while not completed:
							if counter == 1:
								self.reset()
							A__							:    Optional[Any]        =		self.advance()
							if not self.does_advance(A__			):
								raise Exception(
								    """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."""			)

							A__   ,   A__   ,   A__							:    List[Any]        =		self.update(A__			)
							counter += 1

							if counter > 1_0000:
								raise Exception("""update() does not fulfill the constraint."""			)

						if self.remaining() != 0:
							raise Exception("""Custom Constraint is not defined correctly."""			)






					@abstractmethod
					def   __A			( self			):
						raise NotImplementedError(
						    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""			)






					@abstractmethod
					def   __A			( self    ,       A__			):
						raise NotImplementedError(
						    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""			)






					@abstractmethod
					def   __A			( self    ,       A__			):
						raise NotImplementedError(
						    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""			)






					@abstractmethod
					def   __A			( self			):
						raise NotImplementedError(
						    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""			)






					@abstractmethod
					def   __A			( self			):
						raise NotImplementedError(
						    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""			)






					@abstractmethod
					def   __A			( self    ,       A__=False			):
						raise NotImplementedError(
						    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""			)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       A__			):
						super(A__    ,       self			).__init__()

						if not isinstance(A__    ,       A__			) or len(A__			) == 0:
							raise ValueError(F"""`token_ids` has to be a non-empty list, but is {token_ids}."""			)
						if any((not isinstance(A__    ,       A__			) or token_id < 0) for token_id in token_ids			):
							raise ValueError(F"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."""			)

						A__							:    List[Any]        =		token_ids

						A__							:    str        =		len(self.token_ids			)
						A__							:    Dict        =		-1  # the index of the currently fulfilled step
						A__							:    List[str]        =		False






					def   __A			( self			):
						if self.completed:
							return None
						return self.token_ids[self.fulfilled_idx + 1]






					def   __A			( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(A__			)}"""			)

						if self.completed:
							return False

						return token_id == self.token_ids[self.fulfilled_idx + 1]






					def   __A			( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(A__			)}"""			)

						A__							:    Any        =		False
						A__							:    Optional[int]        =		False
						A__							:    Union[str, Any]        =		False

						if self.does_advance(A__			):
							self.fulfilled_idx += 1
							A__							:    List[Any]        =		True
							if self.fulfilled_idx == (self.seqlen - 1):
								A__							:    Optional[int]        =		True
							A__							:    List[Any]        =		completed
						else:
							# failed to make progress.
							A__							:    List[str]        =		True
							self.reset()
						return stepped, completed, reset






					def   __A			( self			):
						A__							:    List[str]        =		False
						A__							:    Any        =		0






					def   __A			( self			):
						return self.seqlen - (self.fulfilled_idx + 1)






					def   __A			( self    ,       A__=False			):
						A__							:    Any        =		PhrasalConstraint(self.token_ids			)

						if stateful:
							A__							:    List[str]        =		self.seqlen
							A__							:    str        =		self.fulfilled_idx
							A__							:    Optional[Any]        =		self.completed

						return new_constraint



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=True			):
						A__							:    List[str]        =		max([len(A__			) for one in nested_token_ids]			)

						A__							:    str        =		{}
						for token_ids in nested_token_ids:
							A__							:    List[Any]        =		root
							for tidx, token_id in enumerate(A__			):
								if token_id not in level:
									A__							:    Tuple        =		{}

								A__							:    int        =		level[token_id]

						if no_subsets and self.has_subsets(A__    ,       A__			):
							raise ValueError(
							    """Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
							    F""" {nested_token_ids}."""			)

						A__							:    int        =		root






					def   __A			( self    ,       A__			):
						A__							:    Tuple        =		self.trie

						for current_token in current_seq:
							A__							:    Tuple        =		start[current_token]

						A__							:    Optional[int]        =		list(start.keys()			)

						return next_tokens






					def   __A			( self    ,       A__			):
						A__							:    List[str]        =		self.next_tokens(A__			)

						return len(A__			) == 0






					def   __A			( self    ,       A__			):
						A__							:    Tuple        =		list(root.values()			)
						if len(A__			) == 0:
							return 1
						else:
							return sum([self.count_leaves(A__			) for nn in next_nodes]			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[Any]        =		self.count_leaves(A__			)
						return len(A__			) != leaf_count



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       A__			):
						super(A__    ,       self			).__init__()

						if not isinstance(A__    ,       A__			) or len(A__			) == 0:
							raise ValueError(F"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."""			)
						if any(not isinstance(A__    ,       A__			) for token_ids in nested_token_ids			):
							raise ValueError(F"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."""			)
						if any(
						    any((not isinstance(A__    ,       A__			) or token_id < 0) for token_id in token_ids			)
						    for token_ids in nested_token_ids			):
							raise ValueError(
							    F"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."""			)

						A__							:    Dict        =		DisjunctiveTrie(A__			)
						A__							:    List[str]        =		nested_token_ids

						A__							:    Optional[int]        =		self.trie.max_height
						A__							:    Tuple        =		[]
						A__							:    List[Any]        =		False






					def   __A			( self			):
						A__							:    Optional[int]        =		self.trie.next_tokens(self.current_seq			)

						if len(A__			) == 0:
							return None
						else:
							return token_list






					def   __A			( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(A__			)}"""			)

						A__							:    Optional[Any]        =		self.trie.next_tokens(self.current_seq			)

						return token_id in next_tokens






					def   __A			( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(A__			)}"""			)

						A__							:    List[str]        =		False
						A__							:    Any        =		False
						A__							:    Optional[int]        =		False

						if self.does_advance(A__			):
							self.current_seq.append(A__			)
							A__							:    str        =		True
						else:
							A__							:    Union[str, Any]        =		True
							self.reset()

						A__							:    Tuple        =		self.trie.reached_leaf(self.current_seq			)
						A__							:    Any        =		completed

						return stepped, completed, reset






					def   __A			( self			):
						A__							:    Optional[int]        =		False
						A__							:    List[Any]        =		[]






					def   __A			( self			):
						if self.completed:
							# since this can be completed without reaching max height
							return 0
						else:
							return self.seqlen - len(self.current_seq			)






					def   __A			( self    ,       A__=False			):
						A__							:    Union[str, Any]        =		DisjunctiveConstraint(self.token_ids			)

						if stateful:
							A__							:    str        =		self.seqlen
							A__							:    int        =		self.current_seq
							A__							:    int        =		self.completed

						return new_constraint



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__			):
						A__							:    List[str]        =		constraints

						# max # of steps required to fulfill a given constraint
						A__							:    List[Any]        =		max([c.seqlen for c in constraints]			)
						A__							:    str        =		len(A__			)
						A__							:    Optional[Any]        =		False

						self.init_state()






					def   __A			( self			):
						A__							:    int        =		[]
						A__							:    Union[str, Any]        =		None
						A__							:    List[Any]        =		[constraint.copy(stateful=A__			) for constraint in self.constraints]






					def   __A			( self			):
						A__							:    List[str]        =		0
						if self.inprogress_constraint:
							# extra points for having a constraint mid-fulfilled
							add += self.max_seqlen - self.inprogress_constraint.remaining()

						return (len(self.complete_constraints			) * self.max_seqlen) + add






					def   __A			( self			):
						A__							:    Optional[Any]        =		[]
						if self.inprogress_constraint is None:
							for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
								A__							:    Optional[Any]        =		constraint.advance()
								if isinstance(A__    ,       A__			):
									token_list.append(A__			)
								elif isinstance(A__    ,       A__			):
									token_list.extend(A__			)
						else:
							A__							:    Optional[Any]        =		self.inprogress_constraint.advance()
							if isinstance(A__    ,       A__			):
								token_list.append(A__			)
							elif isinstance(A__    ,       A__			):
								token_list.extend(A__			)

						if len(A__			) == 0:
							return None
						else:
							return token_list






					def   __A			( self    ,       A__			):
						self.init_state()

						if token_ids is not None:
							for token in token_ids:
								# completes or steps **one** constraint
								A__   ,   A__							:    Tuple        =		self.add(A__			)

								# the entire list of constraints are fulfilled
								if self.completed:
									break






					def   __A			( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`."""			)

						A__   ,   A__							:    Optional[Any]        =		False, False

						if self.completed:
							A__							:    List[str]        =		True
							A__							:    str        =		False
							return complete, stepped

						if self.inprogress_constraint is not None:
							# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
							# job, simply update the state

							A__   ,   A__   ,   A__							:    Dict        =		self.inprogress_constraint.update(A__			)
							if reset:
								# 1. If the next token breaks the progress, then we must restart.
								#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

								#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
								#     constraint, not the full list of constraints.

								self.pending_constraints.append(self.inprogress_constraint.copy(stateful=A__			)			)
								A__							:    Optional[int]        =		None

							if complete:
								# 2. If the next token completes the constraint, move it to completed list, set
								#     inprogress to None. If there are no pending constraints either, then this full list of constraints
								#     is complete.

								self.complete_constraints.append(self.inprogress_constraint			)
								A__							:    Any        =		None

								if len(self.pending_constraints			) == 0:
									# we're done!
									A__							:    List[Any]        =		True

						else:
							# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
							# of constraints?

							for cidx, pending_constraint in enumerate(self.pending_constraints			):
								if pending_constraint.does_advance(A__			):
									A__   ,   A__   ,   A__							:    List[Any]        =		pending_constraint.update(A__			)

									if not stepped:
										raise Exception(
										    """`constraint.update(token_id)` is not yielding incremental progress, """
										    """even though `constraint.does_advance(token_id)` is true."""			)

									if complete:
										self.complete_constraints.append(A__			)
										A__							:    Optional[Any]        =		None

									if not complete and stepped:
										A__							:    Dict        =		pending_constraint

									if complete or stepped:
										# If we made any progress at all, then it's at least not a "pending constraint".

										A__							:    Tuple        =		(
										    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
										)

										if len(self.pending_constraints			) == 0 and self.inprogress_constraint is None:
											# If there's no longer any pending after this and no inprogress either, then we must be
											# complete.

											A__							:    Any        =		True

										break  # prevent accidentally stepping through multiple constraints with just one token.

						return complete, stepped






					def   __A			( self    ,       A__=True			):
						A__							:    int        =		ConstraintListState(self.constraints			)  # we actually never though self.constraints objects
						# throughout this process. So it's at initialization state.

						if stateful:
							A__							:    Optional[int]        =		[
							    constraint.copy(stateful=A__			) for constraint in self.complete_constraints
							]
							if self.inprogress_constraint is not None:
								A__							:    str        =		self.inprogress_constraint.copy(stateful=A__			)
							A__							:    Union[str, Any]        =		[constraint.copy() for constraint in self.pending_constraints]

						return new_state






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_       : List[str]         =			{
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Optional[int]         =			[
	    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXJapaneseForCausalLM',
	    'GPTNeoXJapaneseLayer',
	    'GPTNeoXJapaneseModel',
	    'GPTNeoXJapanesePreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	A_       : Any         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



