





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


lowerCAmelCase_						   =    logging.get_logger(__name__)

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

lowerCAmelCase_						   =    {
    """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""",
    },
}

lowerCAmelCase_						   =    {
    """abeja/gpt-neox-japanese-2.7b""": 2048,
}


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Union[str, Any]				)->		Tuple:
  with open(lowerCAmelCase    ,							'r'    ,							encoding='utf-8'				) as f:
    _snake_case   :				Optional[Any]														=	json.loads(f.read()				)

  _snake_case   :				List[Any]														=	collections.OrderedDict()
  _snake_case   :				Optional[int]														=	collections.OrderedDict()
  _snake_case   :				Tuple														=	collections.OrderedDict()
  with open(lowerCAmelCase    ,							'r'    ,							encoding='utf-8'				) as f:
    _snake_case   :				Tuple														=	f.readlines()
  _snake_case   :				Union[str, Any]														=	[[t.rstrip('\n'				)] if (t == ',' or ',' not in t) else t.rstrip('\n'				).split(','				) for t in token]
  for idx, b in enumerate(lowerCAmelCase				):
    _snake_case   :				List[str]														=	b
    _snake_case   :				int														=	idx
    for wd in b:
      _snake_case   :				List[str]														=	idx

  return vocab, raw_vocab, ids_to_tokens, emoji



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Optional[int]      					=VOCAB_FILES_NAMES
  a_							:       List[Any]      					=PRETRAINED_VOCAB_FILES_MAP
  a_							:       Dict      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_							:       Dict      					=["""input_ids""", """attention_mask"""]







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					Dict      ,				UpperCamelCase      :					int="<|endoftext|>"      ,				UpperCamelCase      :					List[Any]="<|endoftext|>"      ,				UpperCamelCase      :					Optional[Any]="<|startoftext|>"      ,				UpperCamelCase      :					Tuple="<|endoftext|>"      ,				UpperCamelCase      :					Optional[Any]=False      ,				**UpperCamelCase      :					Union[str, Any]      ,				):


    '''simple docstring'''





    super().__init__(
        unk_token=UpperCamelCase      ,				pad_token=UpperCamelCase      ,				bos_token=UpperCamelCase      ,				eos_token=UpperCamelCase      ,				do_clean_text=UpperCamelCase      ,				**UpperCamelCase      ,				)
    if not os.path.isfile(UpperCamelCase							):
      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(UpperCamelCase							):
      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)`'							)
    _snake_case   :				Dict														=	do_clean_text
    _snake_case							,					_snake_case							,					_snake_case							,					_snake_case   :				str														=	load_vocab_and_emoji(UpperCamelCase      ,				UpperCamelCase							)
    _snake_case   :				Union[str, Any]														=	SubWordJapaneseTokenizer(
        vocab=self.vocab      ,				ids_to_tokens=self.ids_to_tokens      ,				emoji=self.emoji							)







  @property
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    return len(self.raw_vocab							)







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    return dict(self.raw_vocab      ,				**self.added_tokens_encoder							)







  def UpperCamelCase_ (      self      :					int      ,				UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    return self.subword_tokenizer.tokenize(UpperCamelCase      ,				clean=self.do_clean_text							)







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





    return self.vocab.get(UpperCamelCase      ,				self.vocab.get(self.unk_token							)							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    return self.subword_tokenizer.convert_id_to_token(UpperCamelCase							)







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	''.join(UpperCamelCase							).strip()
    return out_string







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					"Conversation"							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	[]
    for is_user, text in conversation.iter_texts():
      input_ids.extend(self.encode(UpperCamelCase      ,				add_special_tokens=UpperCamelCase							) + [self.eos_token_id]							)

    if len(UpperCamelCase							) > self.model_max_length:
      _snake_case   :				str														=	input_ids[-self.model_max_length :]
    return input_ids







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Optional[str] = None							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	0
    if os.path.isdir(UpperCamelCase							):
      _snake_case   :				Tuple														=	os.path.join(
          UpperCamelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
      _snake_case   :				Any														=	os.path.join(
          UpperCamelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']							)
    else:
      _snake_case   :				str														=	(
          (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
      )
      _snake_case   :				Dict														=	(
          (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
      )
    with open(UpperCamelCase      ,				'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!'							)
          _snake_case   :				int														=	token_index
        writer.write(','.join(UpperCamelCase							) + '\n'							)
        index += 1
    with open(UpperCamelCase      ,				'w'      ,				encoding='utf-8'							) as writer:
      json.dump(self.emoji      ,				UpperCamelCase							)
    return vocab_file, emoji_file




class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def __init__(      self      :					Optional[int]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





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







  def __len__(      self      :					Optional[Any]							):


    '''simple docstring'''





    return len(self.ids_to_tokens							)







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					str							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	self.content_repattera.sub('<URL>'      ,				UpperCamelCase							)
    _snake_case   :				int														=	self.content_repattera.sub('<EMAIL>'      ,				UpperCamelCase							)
    _snake_case   :				Optional[int]														=	self.content_repattera.sub('<TEL>'      ,				UpperCamelCase							)
    _snake_case   :				Tuple														=	self.content_repattera.sub('<DATE>'      ,				UpperCamelCase							)
    _snake_case   :				Union[str, Any]														=	self.content_repattera.sub('<DATE>'      ,				UpperCamelCase							)
    _snake_case   :				Optional[int]														=	self.content_repattera.sub('<PRICE>'      ,				UpperCamelCase							)
    _snake_case   :				str														=	content.translate(self.content_transa							)
    while "<BLOCK><BLOCK>" in content:
      _snake_case   :				Tuple														=	content.replace('<BLOCK><BLOCK>'      ,				'<BLOCK>'							)
    return content







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					Optional[int]=False							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	text.replace(' '      ,				'<SP>'							)
    _snake_case   :				Tuple														=	text.replace('　'      ,				'<SP>'							)
    _snake_case   :				Optional[Any]														=	text.replace('\r\n'      ,				'<BR>'							)
    _snake_case   :				int														=	text.replace('\n'      ,				'<BR>'							)
    _snake_case   :				Union[str, Any]														=	text.replace('\r'      ,				'<BR>'							)
    _snake_case   :				List[Any]														=	text.replace('\t'      ,				'<TAB>'							)
    _snake_case   :				Union[str, Any]														=	text.replace('—'      ,				'ー'							)
    _snake_case   :				List[Any]														=	text.replace('−'      ,				'ー'							)
    for k, v in self.emoji["emoji"].items():
      if k in text:
        _snake_case   :				Optional[int]														=	text.replace(UpperCamelCase      ,				UpperCamelCase							)
    if clean:
      _snake_case   :				List[str]														=	self.clean_text(UpperCamelCase							)

    def check_simbol(UpperCamelCase      :					Dict							):
      _snake_case   :				Any														=	x.encode()
      if len(UpperCamelCase							) == 1 and len(UpperCamelCase							) == 2:
        _snake_case   :				Any														=	(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(UpperCamelCase      :					Optional[Any]							):
      _snake_case   :				Optional[int]														=	x.encode()
      if len(UpperCamelCase							) == 1 and len(UpperCamelCase							) == 3:
        _snake_case   :				List[Any]														=	(int(e[0]							) << 16) + (int(e[1]							) << 8) + int(e[2]							)
        if c >= 0xe28080 and c <= 0xe2b07f:
          return True
      return False

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







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					int="\n"							):


    '''simple docstring'''





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











from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin



@dataclass
class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       torch.FloatTensor




class        _lowerCAmelCase    (			UpperCAmelCase_							,    UpperCAmelCase_							):


  '''simple docstring'''







  @register_to_config
  def __init__(      self      :					str      ,				UpperCamelCase      :					int = 32      ,				UpperCamelCase      :					int = 64      ,				UpperCamelCase      :					int = 20      ,				UpperCamelCase      :					int = 7_68      ,				UpperCamelCase      :					Optional[int]=77      ,				UpperCamelCase      :					int=4      ,				UpperCamelCase      :					float = 0.0      ,				UpperCamelCase      :					str = "silu"      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = "linear"      ,				UpperCamelCase      :					Optional[str] = "prd"      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				):


    '''simple docstring'''





    super().__init__()
    _snake_case   :				str														=	num_attention_heads
    _snake_case   :				Optional[int]														=	attention_head_dim
    _snake_case   :				Any														=	num_attention_heads * attention_head_dim
    _snake_case   :				List[Any]														=	additional_embeddings

    _snake_case   :				List[str]														=	time_embed_dim or inner_dim
    _snake_case   :				int														=	embedding_proj_dim or embedding_dim
    _snake_case   :				List[Any]														=	clip_embed_dim or embedding_dim

    _snake_case   :				Optional[Any]														=	Timesteps(UpperCamelCase      ,				UpperCamelCase      ,				0							)
    _snake_case   :				List[Any]														=	TimestepEmbedding(UpperCamelCase      ,				UpperCamelCase      ,				out_dim=UpperCamelCase      ,				act_fn=UpperCamelCase							)

    _snake_case   :				Optional[int]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    if embedding_proj_norm_type is None:
      _snake_case   :				str														=	None
    elif embedding_proj_norm_type == "layer":
      _snake_case   :				List[Any]														=	nn.LayerNorm(UpperCamelCase							)
    else:
      raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""							)

    _snake_case   :				str														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    if encoder_hid_proj_type is None:
      _snake_case   :				Any														=	None
    elif encoder_hid_proj_type == "linear":
      _snake_case   :				Optional[int]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)
    else:
      raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""							)

    _snake_case   :				List[str]														=	nn.Parameter(torch.zeros(1      ,				num_embeddings + additional_embeddings      ,				UpperCamelCase							)							)

    if added_emb_type == "prd":
      _snake_case   :				str														=	nn.Parameter(torch.zeros(1      ,				1      ,				UpperCamelCase							)							)
    elif added_emb_type is None:
      _snake_case   :				Dict														=	None
    else:
      raise ValueError(
          f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""							)

    _snake_case   :				Optional[int]														=	nn.ModuleList(
        [
            BasicTransformerBlock(
                UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				dropout=UpperCamelCase      ,				activation_fn='gelu'      ,				attention_bias=UpperCamelCase      ,				)
            for d in range(UpperCamelCase							)
        ]							)

    if norm_in_type == "layer":
      _snake_case   :				Optional[int]														=	nn.LayerNorm(UpperCamelCase							)
    elif norm_in_type is None:
      _snake_case   :				Optional[Any]														=	None
    else:
      raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}."""							)

    _snake_case   :				Optional[Any]														=	nn.LayerNorm(UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    _snake_case   :				List[Any]														=	torch.full(
        [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]      ,				-1_00_00.0							)
    causal_attention_mask.triu_(1							)
    _snake_case   :				Optional[Any]														=	causal_attention_mask[None, ...]
    self.register_buffer('causal_attention_mask'      ,				UpperCamelCase      ,				persistent=UpperCamelCase							)

    _snake_case   :				str														=	nn.Parameter(torch.zeros(1      ,				UpperCamelCase							)							)
    _snake_case   :				List[str]														=	nn.Parameter(torch.zeros(1      ,				UpperCamelCase							)							)







  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	{}

    def fn_recursive_add_processors(UpperCamelCase      :					str      ,				UpperCamelCase      :					torch.nn.Module      ,				UpperCamelCase      :					Dict[str, AttentionProcessor]							):
      if hasattr(UpperCamelCase      ,				'set_processor'							):
        _snake_case   :				Tuple														=	module.processor

      for sub_name, child in module.named_children():
        fn_recursive_add_processors(f"""{name}.{sub_name}"""      ,				UpperCamelCase      ,				UpperCamelCase							)

      return processors

    for name, module in self.named_children():
      fn_recursive_add_processors(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)

    return processors







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					Union[AttentionProcessor, Dict[str, AttentionProcessor]]							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	len(self.attn_processors.keys()							)

    if isinstance(UpperCamelCase      ,				UpperCamelCase							) and len(UpperCamelCase							) != count:
      raise ValueError(
          f"""A dict of processors was passed, but the number of processors {len(UpperCamelCase							)} does not match the"""
          f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""							)

    def fn_recursive_attn_processor(UpperCamelCase      :					str      ,				UpperCamelCase      :					torch.nn.Module      ,				UpperCamelCase      :					Union[str, Any]							):
      if hasattr(UpperCamelCase      ,				'set_processor'							):
        if not isinstance(UpperCamelCase      ,				UpperCamelCase							):
          module.set_processor(UpperCamelCase							)
        else:
          module.set_processor(processor.pop(f"""{name}.processor"""							)							)

      for sub_name, child in module.named_children():
        fn_recursive_attn_processor(f"""{name}.{sub_name}"""      ,				UpperCamelCase      ,				UpperCamelCase							)

    for name, module in self.named_children():
      fn_recursive_attn_processor(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    self.set_attn_processor(AttnProcessor()							)







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					Union[torch.Tensor, float, int]      ,				UpperCamelCase      :					torch.FloatTensor      ,				UpperCamelCase      :					Optional[torch.FloatTensor] = None      ,				UpperCamelCase      :					Optional[torch.BoolTensor] = None      ,				UpperCamelCase      :					bool = True      ,				):


    '''simple docstring'''





    _snake_case   :				Dict														=	hidden_states.shape[0]

    _snake_case   :				str														=	timestep
    if not torch.is_tensor(UpperCamelCase							):
      _snake_case   :				Dict														=	torch.tensor([timesteps]      ,				dtype=torch.long      ,				device=hidden_states.device							)
    elif torch.is_tensor(UpperCamelCase							) and len(timesteps.shape							) == 0:
      _snake_case   :				Tuple														=	timesteps[None].to(hidden_states.device							)

    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
    _snake_case   :				Optional[int]														=	timesteps * torch.ones(UpperCamelCase      ,				dtype=timesteps.dtype      ,				device=timesteps.device							)

    _snake_case   :				Union[str, Any]														=	self.time_proj(UpperCamelCase							)

    # timesteps does not contain any weights and will always return f32 tensors
    # but time_embedding might be fp16, so we need to cast here.
    _snake_case   :				Tuple														=	timesteps_projected.to(dtype=self.dtype							)
    _snake_case   :				List[Any]														=	self.time_embedding(UpperCamelCase							)

    if self.embedding_proj_norm is not None:
      _snake_case   :				Optional[Any]														=	self.embedding_proj_norm(UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	self.embedding_proj(UpperCamelCase							)
    if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
      _snake_case   :				Dict														=	self.encoder_hidden_states_proj(UpperCamelCase							)
    elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
      raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'							)

    _snake_case   :				str														=	self.proj_in(UpperCamelCase							)

    _snake_case   :				int														=	self.positional_embedding.to(hidden_states.dtype							)

    _snake_case   :				Optional[int]														=	[]
    _snake_case   :				List[Any]														=	0

    if encoder_hidden_states is not None:
      additional_embeds.append(UpperCamelCase							)
      additional_embeddings_len += encoder_hidden_states.shape[1]

    if len(proj_embeddings.shape							) == 2:
      _snake_case   :				str														=	proj_embeddings[:, None, :]

    if len(hidden_states.shape							) == 2:
      _snake_case   :				str														=	hidden_states[:, None, :]

    _snake_case   :				str														=	additional_embeds + [
        proj_embeddings,
        time_embeddings[:, None, :],
        hidden_states,
    ]

    if self.prd_embedding is not None:
      _snake_case   :				int														=	self.prd_embedding.to(hidden_states.dtype							).expand(UpperCamelCase      ,				-1      ,				-1							)
      additional_embeds.append(UpperCamelCase							)

    _snake_case   :				Optional[int]														=	torch.cat(
        UpperCamelCase      ,				dim=1      ,				)

    # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
    _snake_case   :				Tuple														=	additional_embeddings_len + proj_embeddings.shape[1] + 1
    if positional_embeddings.shape[1] < hidden_states.shape[1]:
      _snake_case   :				Optional[Any]														=	F.pad(
          UpperCamelCase      ,				(
              0,
              0,
              additional_embeddings_len,
              self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
          )      ,				value=0.0      ,				)

    _snake_case   :				Optional[Any]														=	hidden_states + positional_embeddings

    if attention_mask is not None:
      _snake_case   :				Any														=	(1 - attention_mask.to(hidden_states.dtype							)) * -1_00_00.0
      _snake_case   :				Tuple														=	F.pad(UpperCamelCase      ,				(0, self.additional_embeddings)      ,				value=0.0							)
      _snake_case   :				int														=	(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
      _snake_case   :				str														=	attention_mask.repeat_interleave(self.config.num_attention_heads      ,				dim=0							)

    if self.norm_in is not None:
      _snake_case   :				Tuple														=	self.norm_in(UpperCamelCase							)

    for block in self.transformer_blocks:
      _snake_case   :				Any														=	block(UpperCamelCase      ,				attention_mask=UpperCamelCase							)

    _snake_case   :				Dict														=	self.norm_out(UpperCamelCase							)

    if self.prd_embedding is not None:
      _snake_case   :				str														=	hidden_states[:, -1]
    else:
      _snake_case   :				Any														=	hidden_states[:, additional_embeddings_len:]

    _snake_case   :				List[Any]														=	self.proj_to_clip_embeddings(UpperCamelCase							)

    if not return_dict:
      return (predicted_image_embedding,)

    return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	(prior_latents * self.clip_std) + self.clip_mean
    return prior_latents




