





'''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


a__							:							int =logging.get_logger(__name__)

a__							:							List[Any] ={'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

a__							:							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''',
    },
}

a__							:							Dict ={
    '''abeja/gpt-neox-japanese-2.7b''': 2_048,
}
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              __UpperCamelCase       =  json.loads(f.read()     )

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

       return vocab, raw_vocab, ids_to_tokens, emoji

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					List[str]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					str					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					Dict					=["input_ids", "attention_mask"]






 def __init__(       self       :							Dict							,	__A       :							List[Any]							,	__A       :							Union[str, Any]							,	__A       :							List[str]="<|endoftext|>"							,	__A       :							int="<|endoftext|>"							,	__A       :							Optional[Any]="<|startoftext|>"							,	__A       :							Dict="<|endoftext|>"							,	__A       :							Dict=False							,	**__A       :							Any							,	):
        super().__init__(
            unk_token=__A							,	pad_token=__A							,	bos_token=__A							,	eos_token=__A							,	do_clean_text=__A							,	**__A							,	)
        if not os.path.isfile(__A	):
               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(__A	):
               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)`'	)
        __UpperCamelCase       =  do_clean_text
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  load_vocab_and_emoji(__A							,	__A	)
        __UpperCamelCase       =  SubWordJapaneseTokenizer(
            vocab=self.vocab							,	ids_to_tokens=self.ids_to_tokens							,	emoji=self.emoji	)






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






 def        _lowerCamelCase					(       self       :							Any	):
        return dict(self.raw_vocab							,	**self.added_tokens_encoder	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							str	):
        return self.subword_tokenizer.tokenize(__A							,	clean=self.do_clean_text	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int	):
        return self.vocab.get(__A							,	self.vocab.get(self.unk_token	)	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[Any]	):
        return self.subword_tokenizer.convert_id_to_token(__A	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[str]	):
        __UpperCamelCase       =  ''.join(__A	).strip()
        return out_string






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							"Conversation"	):
        __UpperCamelCase       =  []
        for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__A							,	add_special_tokens=__A	) + [self.eos_token_id]	)

        if len(__A	) > self.model_max_length:
               __UpperCamelCase       =  input_ids[-self.model_max_length :]
        return input_ids






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							str							,	__A       :							Optional[str] = None	):
        __UpperCamelCase       =  0
        if os.path.isdir(__A	):
               __UpperCamelCase       =  os.path.join(
                   __A							,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
               __UpperCamelCase       =  os.path.join(
                   __A							,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']	)
        else:
               __UpperCamelCase       =  (
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
               )
               __UpperCamelCase       =  (
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
               )
        with open(__A							,	'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!'	)
                             __UpperCamelCase       =  token_index
                      writer.write(','.join(__A	) + '\n'	)
                      index += 1
        with open(__A							,	'w'							,	encoding='utf-8'	) as writer:
               json.dump(self.emoji							,	__A	)
        return vocab_file, emoji_file






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



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






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






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							List[str]	):
        __UpperCamelCase       =  self.content_repattera.sub('<URL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<EMAIL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<TEL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<DATE>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<DATE>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<PRICE>'							,	__A	)
        __UpperCamelCase       =  content.translate(self.content_transa	)
        while "<BLOCK><BLOCK>" in content:
               __UpperCamelCase       =  content.replace('<BLOCK><BLOCK>'							,	'<BLOCK>'	)
        return content






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							Any							,	__A       :							Union[str, Any]=False	):
        __UpperCamelCase       =  text.replace(' '							,	'<SP>'	)
        __UpperCamelCase       =  text.replace('　'							,	'<SP>'	)
        __UpperCamelCase       =  text.replace('\r\n'							,	'<BR>'	)
        __UpperCamelCase       =  text.replace('\n'							,	'<BR>'	)
        __UpperCamelCase       =  text.replace('\r'							,	'<BR>'	)
        __UpperCamelCase       =  text.replace('\t'							,	'<TAB>'	)
        __UpperCamelCase       =  text.replace('—'							,	'ー'	)
        __UpperCamelCase       =  text.replace('−'							,	'ー'	)
        for k, v in self.emoji["emoji"].items():
               if k in text:
                      __UpperCamelCase       =  text.replace(__A							,	__A	)
        if clean:
               __UpperCamelCase       =  self.clean_text(__A	)

        def check_simbol(__A       :							str	):
               __UpperCamelCase       =  x.encode()
               if len(__A	) == 1 and len(__A	) == 2:
                      __UpperCamelCase       =  (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(__A       :							Tuple	):
               __UpperCamelCase       =  x.encode()
               if len(__A	) == 1 and len(__A	) == 3:
                      __UpperCamelCase       =  (int(e[0]	) << 1_6) + (int(e[1]	) << 8) + int(e[2]	)
                      if c >= 0xe2_8080 and c <= 0xe2_b07f:
                             return True
               return False

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






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Any							,	__A       :							List[Any]="\n"	):
        __UpperCamelCase       =  []
        __UpperCamelCase       =  []
        __UpperCamelCase       =  self.ids_to_tokens[index][0]
        if word[:6] == "<|byte" and word[-2:] == "|>":
               byte_tokens.append(int(word[6:-2]	)	)
        else:
               if len(__A	) > 0:
                      words.append(bytearray(__A	).decode('utf-8'							,	errors='replace'	)	)
                      __UpperCamelCase       =  []
               if word[:7] == "<|emoji" and word[-2:] == "|>":
                      words.append(self.emoji['emoji_inv'][word]	)
               elif word == "<SP>":
                      words.append(' '	)
               elif word == "<BR>":
                      words.append(__A	)
               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(__A	)
        if len(__A	) > 0:
               words.append(bytearray(__A	).decode('utf-8'							,	errors='replace'	)	)
        __UpperCamelCase       =  ''.join(__A	)
        return text







'''simple docstring'''
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Tuple				,  __lowercase						:  Tuple     ) ->      Tuple:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  List[str]="attention"     ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       =  __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  k_tmp.reshape(k_tmp.shape[0]				,  k_tmp.shape[1] * k_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,  o_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  q_tmp.reshape(q_tmp.shape[0]				,  q_tmp.shape[1] * q_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  v_tmp.reshape(v_tmp.shape[0]				,  v_tmp.shape[1] * v_tmp.shape[2]     )
       return k, o, q, v
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict				,  __lowercase						:  int				,  __lowercase						:  List[Any]=False     ) ->      Optional[Any]:



       """simple docstring"""






       if split_mlp_wi:
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
              __UpperCamelCase       =  (wi_a, wi_a)
       else:
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

       __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
       return wi, wo
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[Any]				,  __lowercase						:  List[str]				,  __lowercase						:  Optional[int]     ) ->      str:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def 				lowercase__   (   __lowercase						:  dict				,  *, __lowercase						:  int				,  __lowercase						:  bool				,  __lowercase						:  bool = False     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  traverse_util.flatten_dict(variables['target']     )
       __UpperCamelCase       =  {'/'.join(__lowercase     ): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       __UpperCamelCase       =  'encoder/encoder/mlp/wi_0/kernel' in old
       print('Split MLP:'				,  __lowercase     )

       __UpperCamelCase       =  collections.OrderedDict()

       # Shared embeddings.
       __UpperCamelCase       =  old['token_embedder/embedding']

       # Encoder.
       for i in range(__lowercase     ):
              # Block i, layer 0 (Self Attention).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_attention_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'attention'     )
              __UpperCamelCase       =  layer_norm
              __UpperCamelCase       =  k.T
              __UpperCamelCase       =  o.T
              __UpperCamelCase       =  q.T
              __UpperCamelCase       =  v.T

              # Block i, layer 1 (MLP).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_mlp_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'encoder'				,  __lowercase     )
              __UpperCamelCase       =  layer_norm
              if split_mlp_wi:
                     __UpperCamelCase       =  wi[0].T
                     __UpperCamelCase       =  wi[1].T
              else:
                     __UpperCamelCase       =  wi.T
              __UpperCamelCase       =  wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     __UpperCamelCase       =  tax_relpos_bias_lookup(
                         __lowercase				,  __lowercase				,  'encoder'     ).T

       __UpperCamelCase       =  old['encoder/encoder_norm/scale']

       if not scalable_attention:
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'encoder'     ).T
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'decoder'     ).T

       if not is_encoder_only:
              # Decoder.
              for i in range(__lowercase     ):
                     # Block i, layer 0 (Self Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_self_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'self_attention'     )
                     __UpperCamelCase       =  layer_norm
                     __UpperCamelCase       =  k.T
                     __UpperCamelCase       =  o.T
                     __UpperCamelCase       =  q.T
                     __UpperCamelCase       =  v.T

                     # Block i, layer 1 (Cross Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_cross_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'encoder_decoder_attention'     )
                     __UpperCamelCase       =  layer_norm
                     __UpperCamelCase       =  k.T
                     __UpperCamelCase       =  o.T
                     __UpperCamelCase       =  q.T
                     __UpperCamelCase       =  v.T

                     # Block i, layer 2 (MLP).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_mlp_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'decoder'				,  __lowercase     )
                     __UpperCamelCase       =  layer_norm
                     if split_mlp_wi:
                            __UpperCamelCase       =  wi[0].T
                            __UpperCamelCase       =  wi[1].T
                     else:
                            __UpperCamelCase       =  wi.T
                     __UpperCamelCase       =  wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            __UpperCamelCase       =  tax_relpos_bias_lookup(__lowercase				,  __lowercase				,  'decoder'     ).T

              __UpperCamelCase       =  old['decoder/decoder_norm/scale']

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     __UpperCamelCase       =  old['decoder/logits_dense/kernel'].T

       return new
def 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  bool     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              __UpperCamelCase       =  state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     __UpperCamelCase       =  state_dict['shared.weight']

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print('Using shared word embeddings as lm_head.'     )
                     __UpperCamelCase       =  state_dict['shared.weight']

       return state_dict
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Dict				,  __lowercase						:  str				,  __lowercase						:  int				,  __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  checkpoints.load_tax_checkpoint(__lowercase     )
       __UpperCamelCase       =  convert_tax_to_pytorch(
           __lowercase				,  num_layers=config.num_layers				,  is_encoder_only=__lowercase				,  scalable_attention=__lowercase     )
       __UpperCamelCase       =  make_state_dict(__lowercase				,  __lowercase     )
       model.load_state_dict(__lowercase				,  strict=__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  bool = False				,  __lowercase						:  bool = False				,  ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  MTaConfig.from_json_file(__lowercase     )
       print(F'''Building PyTorch model from configuration: {config}'''     )
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              __UpperCamelCase       =  UMTaEncoderModel(__lowercase     )
       else:
              __UpperCamelCase       =  UMTaForConditionalGeneration(__lowercase     )

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(__lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase     )

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
       model.save_pretrained(__lowercase     )

       # Verify that we can load the checkpoint.
       model.from_pretrained(__lowercase     )
       print('Done'     )


if __name__ == "__main__":
    a__							:							List[Any] =argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
    )
    parser.add_argument(
        '''--scalable_attention''',
        action='''store_true''',
        help='''Whether the model uses scaled attention (umt5 model)''',
        default=False,
    )
    a__							:							List[str] =parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )
