


'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				int									=		{"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

__SCREAMING_SNAKE_CASE					:				int									=		{
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

__SCREAMING_SNAKE_CASE					:				List[Any]									=		{
    """abeja/gpt-neox-japanese-2.7b""": 2048,
}







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							with open(UpperCAmelCase_						,  "r"						,  encoding="utf-8" ) as f:
														A_			    =		json.loads(f.read() )

							A_			    =		collections.OrderedDict()
							A_			    =		collections.OrderedDict()
							A_			    =		collections.OrderedDict()
							with open(UpperCAmelCase_						,  "r"						,  encoding="utf-8" ) as f:
														A_			    =		f.readlines()
							A_			    =		[[t.rstrip("\n" )] if (t == ',' or ',' not in t) else t.rstrip("\n" ).split("," ) for t in token]
							for idx, b in enumerate(UpperCAmelCase_ ):
														A_			    =		b
														A_			    =		idx
														for wd in b:
																					A_			    =		idx

							return vocab, raw_vocab, ids_to_tokens, emoji






class 					__lowerCAmelCase   (   _UpperCAmelCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[str]   						=VOCAB_FILES_NAMES
						_UpperCAmelCase		:       Tuple   						=PRETRAINED_VOCAB_FILES_MAP
						_UpperCAmelCase		:       Union[str, Any]   						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCAmelCase		:       Union[str, Any]   						=['input_ids', 'attention_mask']






						def __init__(  self							:      Dict ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      List[str]="<|endoftext|>" ,  lowerCAmelCase							:      Optional[Any]="<|endoftext|>" ,  lowerCAmelCase							:      List[str]="<|startoftext|>" ,  lowerCAmelCase							:      str="<|endoftext|>" ,  lowerCAmelCase							:      Union[str, Any]=False ,  **lowerCAmelCase							:      int ,  ):
													super().__init__(
													    unk_token=SCREAMING_SNAKE_CASE_ ,  pad_token=SCREAMING_SNAKE_CASE_ ,  bos_token=SCREAMING_SNAKE_CASE_ ,  eos_token=SCREAMING_SNAKE_CASE_ ,  do_clean_text=SCREAMING_SNAKE_CASE_ ,  **SCREAMING_SNAKE_CASE_ ,  )
													if not os.path.isfile(SCREAMING_SNAKE_CASE_	):
																				raise ValueError(
																				    F"Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained"
																				    " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
													if not os.path.isfile(SCREAMING_SNAKE_CASE_	):
																				raise ValueError(
																				    F"Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google"
																				    " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"	)
													A_			    =		do_clean_text
													A_			    =		load_vocab_and_emoji(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		SubWordJapaneseTokenizer(
													    vocab=self.vocab ,  ids_to_tokens=self.ids_to_tokens ,  emoji=self.emoji	)






						@property
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
													return len(self.raw_vocab	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													return dict(self.raw_vocab ,  **self.added_tokens_encoder	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      Optional[Any]	):
													return self.subword_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ,  clean=self.do_clean_text	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      Union[str, Any]	):
													return self.vocab.get(SCREAMING_SNAKE_CASE_ ,  self.vocab.get(self.unk_token	)	)






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      Optional[int]	):
													return self.subword_tokenizer.convert_id_to_token(SCREAMING_SNAKE_CASE_	)






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      int	):
													A_			    =		''.join(SCREAMING_SNAKE_CASE_	).strip()
													return out_string






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      Tuple	):
													A_			    =		[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_ ,  add_special_tokens=SCREAMING_SNAKE_CASE_	) + [self.eos_token_id]	)

													if len(SCREAMING_SNAKE_CASE_	) > self.model_max_length:
																				A_			    =		input_ids[-self.model_max_length :]
													return input_ids






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      str = None	):
													A_			    =		0
													if os.path.isdir(SCREAMING_SNAKE_CASE_	):
																				A_			    =		os.path.join(
																				    SCREAMING_SNAKE_CASE_ ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
																				A_			    =		os.path.join(
																				    SCREAMING_SNAKE_CASE_ ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]	)
													else:
																				A_			    =		(
																				    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
																				)
																				A_			    =		(
																				    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
																				)
													with open(SCREAMING_SNAKE_CASE_ ,  "w" ,  encoding="utf-8"	) as writer:
																				for token_index, token in self.ids_to_tokens.items():
																											if index != token_index:
																																		logger.warning(
																																		    F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																																		    " Please check that the vocabulary is not corrupted!"	)
																																		A_			    =		token_index
																											writer.write(",".join(SCREAMING_SNAKE_CASE_	) + "\n"	)
																											index += 1
													with open(SCREAMING_SNAKE_CASE_ ,  "w" ,  encoding="utf-8"	) as writer:
																				json.dump(self.emoji ,  SCREAMING_SNAKE_CASE_	)
													return vocab_file, emoji_file






class 					__lowerCAmelCase   (   _UpperCAmelCase						):

						"""simple docstring"""






						def __init__(  self							:      Tuple ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      str	):
													A_			    =		vocab  # same as swe
													A_			    =		ids_to_tokens  # same as bpe
													A_			    =		emoji
													A_			    =		np.max([len(SCREAMING_SNAKE_CASE_	) for w in self.vocab.keys()]	)
													A_			    =		re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"	)
													A_			    =		re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"	)
													A_			    =		re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"	)
													A_			    =		re.compile(
													    r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
													A_			    =		re.compile(
													    r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"	)
													A_			    =		re.compile(
													    r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"	)
													A_			    =		'─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
													A_			    =		'▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
													A_			    =		str.maketrans({k: "<BLOCK>" for k in keisen + blocks}	)






						def __len__(  self							:      List[str]	):
													return len(self.ids_to_tokens	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      Dict	):
													A_			    =		self.content_repattera.sub("<URL>" ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		self.content_repattera.sub("<EMAIL>" ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		self.content_repattera.sub("<TEL>" ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		self.content_repattera.sub("<PRICE>" ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		content.translate(self.content_transa	)
													while "<BLOCK><BLOCK>" in content:
																				A_			    =		content.replace("<BLOCK><BLOCK>" ,  "<BLOCK>"	)
													return content






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      List[Any]=False	):
													A_			    =		text.replace(" " ,  "<SP>"	)
													A_			    =		text.replace("　" ,  "<SP>"	)
													A_			    =		text.replace("\r\n" ,  "<BR>"	)
													A_			    =		text.replace("\n" ,  "<BR>"	)
													A_			    =		text.replace("\r" ,  "<BR>"	)
													A_			    =		text.replace("\t" ,  "<TAB>"	)
													A_			    =		text.replace("—" ,  "ー"	)
													A_			    =		text.replace("−" ,  "ー"	)
													for k, v in self.emoji["emoji"].items():
																				if k in text:
																											A_			    =		text.replace(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_	)
													if clean:
																				A_			    =		self.clean_text(SCREAMING_SNAKE_CASE_	)

													def check_simbol(lowerCAmelCase							:      Optional[int]	):
																				A_			    =		x.encode()
																				if len(SCREAMING_SNAKE_CASE_	) == 1 and len(SCREAMING_SNAKE_CASE_	) == 2:
																											A_			    =		(int(e[0]	) << 8) + int(e[1]	)
																											if (
																											    (c >= 0xC2A1 and c <= 0xC2BF)
																											    or (c >= 0xC780 and c <= 0xC783)
																											    or (c >= 0xCAB9 and c <= 0xCBBF)
																											    or (c >= 0xCC80 and c <= 0xCDA2)
																											):
																																		return True
																				return False

													def checkuae(lowerCAmelCase							:      Tuple	):
																				A_			    =		x.encode()
																				if len(SCREAMING_SNAKE_CASE_	) == 1 and len(SCREAMING_SNAKE_CASE_	) == 3:
																											A_			    =		(int(e[0]	) << 16) + (int(e[1]	) << 8) + int(e[2]	)
																											if c >= 0xE2_8080 and c <= 0xE2_B07F:
																																		return True
																				return False

													A_			    =		0
													A_			    =		[]
													while pos < len(SCREAMING_SNAKE_CASE_	):
																				A_			    =		min(len(SCREAMING_SNAKE_CASE_	) ,  pos + self.maxlen + 1	) if text[pos] == '<' else pos + 3
																				A_			    =		[]  # (token_id, token, pos)
																				for e in range(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_ ,  -1	):
																											A_			    =		text[pos:e]
																											if wd in self.vocab:
																																		if wd[0] == "<" and len(SCREAMING_SNAKE_CASE_	) > 2:
																																									A_			    =		[(self.vocab[wd], wd, e)]
																																									break
																																		else:
																																									candidates.append((self.vocab[wd], wd, e)	)
																				if len(SCREAMING_SNAKE_CASE_	) > 0:
																											# the smallest token_id is adopted
																											A_			    =		sorted(SCREAMING_SNAKE_CASE_ ,  key=lambda lowerCAmelCase	: x[0]	)[0]
																											result.append(SCREAMING_SNAKE_CASE_	)
																											A_			    =		e
																				else:
																											A_			    =		pos + 1
																											A_			    =		text[pos:end]
																											if check_simbol(SCREAMING_SNAKE_CASE_	):
																																		result.append("<KIGOU>"	)
																											elif checkuae(SCREAMING_SNAKE_CASE_	):
																																		result.append("<U2000U2BFF>"	)
																											else:
																																		for i in wd.encode("utf-8"	):
																																									result.append("<|byte%d|>" % i	)
																											A_			    =		end
													return result






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      List[Any]="\n"	):
													A_			    =		[]
													A_			    =		[]
													A_			    =		self.ids_to_tokens[index][0]
													if word[:6] == "<|byte" and word[-2:] == "|>":
																				byte_tokens.append(int(word[6:-2]	)	)
													else:
																				if len(SCREAMING_SNAKE_CASE_	) > 0:
																											words.append(bytearray(SCREAMING_SNAKE_CASE_	).decode("utf-8" ,  errors="replace"	)	)
																											A_			    =		[]
																				if word[:7] == "<|emoji" and word[-2:] == "|>":
																											words.append(self.emoji["emoji_inv"][word]	)
																				elif word == "<SP>":
																											words.append(" "	)
																				elif word == "<BR>":
																											words.append(SCREAMING_SNAKE_CASE_	)
																				elif word == "<TAB>":
																											words.append("\t"	)
																				elif word == "<BLOCK>":
																											words.append("▀"	)
																				elif word == "<KIGOU>":
																											words.append("ǀ"	)
																				elif word == "<U2000U2BFF>":
																											words.append("‖"	)
																				else:
																											words.append(SCREAMING_SNAKE_CASE_	)
													if len(SCREAMING_SNAKE_CASE_	) > 0:
																				words.append(bytearray(SCREAMING_SNAKE_CASE_	).decode("utf-8" ,  errors="replace"	)	)
													A_			    =		''.join(SCREAMING_SNAKE_CASE_	)
													return text









'''simple docstring'''



from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

# TODO Update this
A__						:   Tuple				=    {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     'esm'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10_26     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_="absolute"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	List[str]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_     ,	mask_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      str														=						num_hidden_layers
         __lowerCamelCase  :      List[str]														=						num_attention_heads
         __lowerCamelCase  :      Any														=						intermediate_size
         __lowerCamelCase  :      Optional[Any]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[int]														=						max_position_embeddings
         __lowerCamelCase  :      str														=						initializer_range
         __lowerCamelCase  :      Optional[int]														=						layer_norm_eps
         __lowerCamelCase  :      List[str]														=						position_embedding_type
         __lowerCamelCase  :      int														=						use_cache
         __lowerCamelCase  :      Optional[Any]														=						emb_layer_norm_before
         __lowerCamelCase  :      Optional[Any]														=						token_dropout
         __lowerCamelCase  :      str														=						is_folding_model
         if is_folding_model:
              if esmfold_config is None:
                   logger.info('No esmfold_config supplied for folding model, using default values.'      )
                   __lowerCamelCase  :      Dict														=						EsmFoldConfig()
              elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      Optional[int]														=						EsmFoldConfig(**SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						esmfold_config
              if vocab_list is None:
                   logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'      )
                   __lowerCamelCase  :      List[str]														=						get_default_vocab_list()
              else:
                   __lowerCamelCase  :      Optional[Any]														=						vocab_list
         else:
              __lowerCamelCase  :      Dict														=						None
              __lowerCamelCase  :      Optional[Any]														=						None
         if self.esmfold_config is not None and getattr(self.esmfold_config     ,	'use_esm_attn_map'     ,	SCREAMING_SNAKE_CASE_      ):
              raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Any														=						super().to_dict()
         if isinstance(self.esmfold_config     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      int														=						self.esmfold_config.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str					=     None
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   float					=     0

    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False

    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   "TrunkConfig"					=     None







    def  lowercase_							(				self      )							->	Any:
         if self.trunk is None:
              __lowerCamelCase  :      List[str]														=						TrunkConfig()
         elif isinstance(self.trunk     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Any														=						TrunkConfig(**self.trunk      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[int]														=						asdict(self      )
         __lowerCamelCase  :      str														=						self.trunk.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     4_8
    lowerCamelCase    :   int					=     1_0_2_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   Optional[int]					=     1_2_8
    lowerCamelCase    :   "StructureModuleConfig"					=     None







    def  lowercase_							(				self      )							->	Optional[int]:
         if self.structure_module is None:
              __lowerCamelCase  :      Dict														=						StructureModuleConfig()
         elif isinstance(self.structure_module     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[Any]														=						StructureModuleConfig(**self.structure_module      )

         if self.max_recycles <= 0:
              raise ValueError(f'`max_recycles` should be positive, got {self.max_recycles}.'      )
         if self.sequence_state_dim % self.sequence_state_dim != 0:
              raise ValueError(
                  '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
                  f' {self.sequence_state_dim} and {self.sequence_state_dim}.'      )
         if self.pairwise_state_dim % self.pairwise_state_dim != 0:
              raise ValueError(
                  '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
                  f' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'      )

         __lowerCamelCase  :      Tuple														=						self.sequence_state_dim // self.sequence_head_width
         __lowerCamelCase  :      str														=						self.pairwise_state_dim // self.pairwise_head_width

         if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
              raise ValueError(
                  '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
                  f' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'      )
         if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
              raise ValueError(
                  '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
                  f' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'      )
         if self.pairwise_state_dim % 2 != 0:
              raise ValueError(f'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'      )

         if self.dropout >= 0.4:
              raise ValueError(f'`dropout` should not be greater than 0.4, got {self.dropout}.'      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      List[str]														=						asdict(self      )
         __lowerCamelCase  :      int														=						self.structure_module.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     3_8_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_6
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_2
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   float					=     0.1
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     2
    lowerCamelCase    :   int					=     7
    lowerCamelCase    :   int					=     1_0
    lowerCamelCase    :   float					=     1e-8
    lowerCamelCase    :   float					=     1e5







    def  lowercase_							(				self      )							->	Any:
         return asdict(self      )







def      UpperCAmelCase__   (      )	->						Optional[Any]:
     return (
         "<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>",
     )






