
import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa

class      lowerCAmelCase__  (			snake_case_    ):
  a__ :		str   						=	["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]






  def __init__(	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							str						)  ->							List[str]:
      assert Path(hparams.data_dir						).exists()
      __lowerCamelCase					      =			Path(hparams.output_dir						)
      self.output_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__						)

      __lowerCamelCase					      =			self.output_dir.joinpath('''student'''						)

      __lowerCamelCase					      =			str(SCREAMING_SNAKE_CASE__						)  # Tell lightning we are training the student
      __lowerCamelCase					      =			AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher						).eval()
      use_task_specific_params(SCREAMING_SNAKE_CASE__							,    hparams.task						)  # We copy good generation parameters to student by default
      if hparams.student is not None:
          __lowerCamelCase					      =			AutoModelForSeqaSeqLM.from_pretrained(hparams.student						)
          use_task_specific_params(SCREAMING_SNAKE_CASE__							,    hparams.task						)
          __lowerCamelCase      ,					__lowerCamelCase					      =			None, None
      else:
          __lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			create_student_by_copying_alternating_layers(
              SCREAMING_SNAKE_CASE__							,    e=hparams.student_encoder_layers							,    d=hparams.student_decoder_layers							,    save_path=SCREAMING_SNAKE_CASE__						)

      if hparams.length_penalty != -1:
          __lowerCamelCase					      =			hparams.length_penalty
      __lowerCamelCase					      =			hparams.teacher  # Use teacher's tokenizer
      super().__init__(SCREAMING_SNAKE_CASE__							,    model=SCREAMING_SNAKE_CASE__							,    config=student.config						)
      assert student.config.model_type == teacher.config.model_type, (
          f'''teacher, student model types should be the same, got {student.config.model_type} !='''
          f''' {teacher.config.model_type}'''
      )

      if student.config.model_type == "t5":
          __lowerCamelCase					      =			len(student.get_encoder().block						)
          __lowerCamelCase					      =			len(student.get_decoder().block						)
          __lowerCamelCase					      =			len(teacher.get_encoder().block						)
          __lowerCamelCase					      =			len(teacher.get_decoder().block						)
      else:
          __lowerCamelCase					      =			student.config.encoder_layers
          __lowerCamelCase					      =			student.config.decoder_layers
          __lowerCamelCase					      =			teacher.config.encoder_layers
          __lowerCamelCase					      =			teacher.config.decoder_layers

      __lowerCamelCase					      =			not (hparams.student is None or hparams.teacher == hparams.student)
      __lowerCamelCase					      =			(not self.different_base_models) and hparams.alpha_hid > 0
      __lowerCamelCase					      =			self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      __lowerCamelCase					      =			teacher
      freeze_params(self.teacher						)

      if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
          try:
              del self.teacher.model.encoder
          except AttributeError:  # T5
              del self.teacher.encoder

      if e_layer_ids is None:
          __lowerCamelCase					      =			list(range(SCREAMING_SNAKE_CASE__						)						)
      if d_layer_ids is None:
          __lowerCamelCase					      =			list(range(SCREAMING_SNAKE_CASE__						)						)

      __lowerCamelCase      ,					__lowerCamelCase					      =			e_layer_ids, d_layer_ids  # type: List[int], List[int]

      if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
          if hparams.supervise_forward:
              __lowerCamelCase					      =			get_layers_to_supervise(
                  n_student=len(self.e_layer_ids						)							,    n_teacher=SCREAMING_SNAKE_CASE__						)
              __lowerCamelCase					      =			get_layers_to_supervise(
                  n_student=len(self.d_layer_ids						)							,    n_teacher=SCREAMING_SNAKE_CASE__						)
          else:  # student layer should emulate hidden states of the teacher layer it was copied from
              __lowerCamelCase					      =			self.e_layer_ids
              __lowerCamelCase					      =			self.d_layer_ids
      else:
          __lowerCamelCase					      =			None
          __lowerCamelCase					      =			None

      __lowerCamelCase					      =			nn.KLDivLoss(reduction='''batchmean'''						)
      __lowerCamelCase					      =			2.0
      __lowerCamelCase					      =			hparams.alpha_mlm
      __lowerCamelCase					      =			hparams.alpha_ce
      __lowerCamelCase					      =			hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()






  def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							str							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							str:
      __lowerCamelCase					      =			mask[:, :, None].expand_as(SCREAMING_SNAKE_CASE__						)
      __lowerCamelCase					      =			s_logits.size(-1						)
      __lowerCamelCase					      =			torch.masked_select(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase					      =			torch.masked_select(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase					      =			s_logits_slct.view(-1							,    SCREAMING_SNAKE_CASE__						)  # (bs * seq_length, voc_size) modulo the 1s in mask
      __lowerCamelCase					      =			t_logits_slct.view(-1							,    SCREAMING_SNAKE_CASE__						)  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      __lowerCamelCase					      =			(
          self.ce_loss_fct(
              nn.functional.log_softmax(s_logits_slct / self.temperature							,    dim=-1						)							,    nn.functional.softmax(t_logits_slct / self.temperature							,    dim=-1						)							,    )
          * (self.temperature) ** 2
      )
      return loss_ce






  @staticmethod
  def 				__A (	SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							int						)  ->							Union[str, Any]:
      SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
      add_distill_args(SCREAMING_SNAKE_CASE__						)
      return parser






  def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							str						)  ->							tuple:
      __lowerCamelCase					      =			self.tokenizer.pad_token_id
      __lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
      if isinstance(self.model							,    SCREAMING_SNAKE_CASE__						):
          __lowerCamelCase					      =			self.model._shift_right(SCREAMING_SNAKE_CASE__						)
      else:
          __lowerCamelCase					      =			shift_tokens_right(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

      # noinspection PyCallingNonCallable
      __lowerCamelCase					      =			self(
          SCREAMING_SNAKE_CASE__							,    attention_mask=SCREAMING_SNAKE_CASE__							,    decoder_input_ids=SCREAMING_SNAKE_CASE__							,    output_hidden_states=self.do_calc_hidden_loss							,    output_attentions=SCREAMING_SNAKE_CASE__							,    use_cache=SCREAMING_SNAKE_CASE__							,    )
      __lowerCamelCase					      =			student_outputs['''logits''']

      # Same cross entropy vs. label smoothing logic as finetune.py
      assert lm_logits.shape[-1] == self.model.config.vocab_size
      if self.hparams.label_smoothing == 0:
          # Same behavior as modeling_bart.py, besides ignoring pad_token_id
          __lowerCamelCase					      =			nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE__						)
          __lowerCamelCase					      =			loss_fct(lm_logits.view(-1							,    lm_logits.shape[-1]						)							,    labels.view(-1						)						)
      else:
          __lowerCamelCase					      =			nn.functional.log_softmax(SCREAMING_SNAKE_CASE__							,    dim=-1						)
          __lowerCamelCase      ,					__lowerCamelCase					      =			label_smoothed_nll_loss(
              SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    self.hparams.label_smoothing							,    ignore_index=SCREAMING_SNAKE_CASE__						)

      def zero_tensor():
          return torch.tensor(0.0						).type_as(SCREAMING_SNAKE_CASE__						)

      __lowerCamelCase					      =			student_outputs[
          '''encoder_last_hidden_state'''
      ]  # use this unless self.different_base_models
      __lowerCamelCase      ,					__lowerCamelCase					      =			zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
          __lowerCamelCase					      =			self.teacher.get_encoder()(
              SCREAMING_SNAKE_CASE__							,    attention_mask=SCREAMING_SNAKE_CASE__							,    output_hidden_states=self.do_calc_hidden_loss							,    )
          if self.different_base_models:
              __lowerCamelCase					      =			all_teacher_encoder_outputs['''last_hidden_state''']
          elif self.do_calc_hidden_loss:
              __lowerCamelCase					      =			self.calc_hidden_loss(
                  SCREAMING_SNAKE_CASE__							,    student_outputs['''encoder_hidden_states''']							,    all_teacher_encoder_outputs['''hidden_states''']							,    self.e_matches							,    normalize_hidden=self.hparams.normalize_hidden							,    )

      __lowerCamelCase					      =			self.teacher(
          SCREAMING_SNAKE_CASE__							,    attention_mask=SCREAMING_SNAKE_CASE__							,    encoder_outputs=(teacher_enc_outputs,)							,    decoder_input_ids=SCREAMING_SNAKE_CASE__							,    output_hidden_states=self.do_calc_hidden_loss							,    use_cache=SCREAMING_SNAKE_CASE__							,    )
      __lowerCamelCase					      =			decoder_input_ids.ne(SCREAMING_SNAKE_CASE__						)
      __lowerCamelCase					      =			self.calc_ce_loss(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    teacher_outputs['''logits''']						)
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
          __lowerCamelCase					      =			self.calc_hidden_loss(
              SCREAMING_SNAKE_CASE__							,    student_outputs['''decoder_hidden_states''']							,    teacher_outputs['''decoder_hidden_states''']							,    self.d_matches							,    normalize_hidden=self.hparams.normalize_hidden							,    )

      __lowerCamelCase					      =			(
          self.alpha_ce * loss_ce
          + self.alpha_mlm * student_lm_loss
          + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
      )
      return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







  @staticmethod
  def 				__A (	SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Any							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							Tuple:
      __lowerCamelCase					      =			'''expected list or tuple for hidden_states, got tensor of shape: '''
      assert not isinstance(SCREAMING_SNAKE_CASE__							,    torch.Tensor						), f'''{msg}{hidden_states.shape}'''
      assert not isinstance(SCREAMING_SNAKE_CASE__							,    torch.Tensor						), f'''{msg}{hidden_states_T.shape}'''
      __lowerCamelCase					      =			attention_mask.to(hidden_states[0]						)
      __lowerCamelCase					      =			mask.sum() * hidden_states[0].size(-1						)
      __lowerCamelCase					      =			torch.stack([hidden_states[i] for i in range(len(SCREAMING_SNAKE_CASE__						)						)]						)
      __lowerCamelCase					      =			torch.stack([hidden_states_T[j] for j in matches]						)
      assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
      if normalize_hidden:
          __lowerCamelCase					      =			nn.functional.layer_norm(SCREAMING_SNAKE_CASE__							,    student_states.shape[1:]						)
          __lowerCamelCase					      =			nn.functional.layer_norm(SCREAMING_SNAKE_CASE__							,    teacher_states.shape[1:]						)
      __lowerCamelCase					      =			nn.functional.mse_loss(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    reduction='''none'''						)
      __lowerCamelCase					      =			(mse * mask.unsqueeze(0						).unsqueeze(-1						)).sum() / valid_count
      return masked_mse





def 				__magic_name__				(     __lowerCAmelCase			:		Optional[int]							)    ->     str:
    # NOTE: if --student argument was specified and the teacher and student base models
    # are different, the models still have to have the same tokenizer, specified by
    # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
    # from bart to t5. This s because if the tokenizers are different, the output space
    # for the two models is also different and their logits are not comparable.
    parser.add_argument('''--teacher'''	,						type=__lowerCamelCase							)
    parser.add_argument('''--alpha_ce'''	,						default=0.8	,						type=__lowerCamelCase							)
    parser.add_argument('''--alpha_mlm'''	,						default=0.2	,						type=__lowerCamelCase							)
    parser.add_argument('''--alpha_hid'''	,						default=0.0	,						type=__lowerCamelCase	,						required=__lowerCamelCase							)
    parser.add_argument('''--student'''	,						type=__lowerCamelCase	,						required=__lowerCamelCase							)
    parser.add_argument('''--student_decoder_layers'''	,						default=12	,						type=__lowerCamelCase	,						required=__lowerCamelCase							)
    parser.add_argument('''--student_encoder_layers'''	,						default=12	,						type=__lowerCamelCase	,						required=__lowerCamelCase							)
    parser.add_argument('''--no_teacher'''	,						action='''store_true'''	,						default=__lowerCamelCase							)
    parser.add_argument('''--length_penalty'''	,						type=__lowerCamelCase	,						default=-1							)
    parser.add_argument('''--supervise_forward'''	,						action='''store_true'''	,						default=__lowerCamelCase							)
    parser.add_argument('''--normalize_hidden'''	,						action='''store_true'''	,						default=__lowerCamelCase							)

class      lowerCAmelCase__  (			snake_case_    ):
  a__ :		Optional[int]   						=	"""translation"""
  a__ :		List[str]   						=	["""bleu"""]
  a__ :		int   						=	"""bleu"""






  def __init__(	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							int						)  ->							Any:
      super().__init__(SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      __lowerCamelCase					      =			hparams.src_lang
      __lowerCamelCase					      =			hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,    SCREAMING_SNAKE_CASE__						):
          __lowerCamelCase					      =			self.tokenizer.lang_code_to_id[hparams.tgt_lang]






  def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							List[str]						)  ->							dict:
      return calculate_bleu(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)







  @staticmethod
  def 				__A (	SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							str						)  ->							Any:
      TranslationModule.add_model_specific_args(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
      add_distill_args(SCREAMING_SNAKE_CASE__						)
      return parser





def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]							)    ->     Tuple:
    if args.no_teacher:
        __lowerCamelCase					      =			TranslationModule if '''translation''' in args.task else SummarizationModule
    else:  # DISTILL WITH TEACHER
        __lowerCamelCase					      =			TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
    __lowerCamelCase					      =			module_cls.__name__
    print(f'''using module {args.setup_cls}'''							)
    __lowerCamelCase					      =			module_cls(__lowerCamelCase							)
    return model




def 				__magic_name__				(     __lowerCAmelCase			:		List[str]							)    ->     str:
    Path(args.output_dir							).mkdir(exist_ok=__lowerCamelCase							)
    check_output_dir(__lowerCamelCase	,						expected_items=3							)

    __lowerCamelCase					      =			create_module(__lowerCamelCase							)
    return ft_main(__lowerCamelCase	,						model=__lowerCamelCase							)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     :    List[str]      							=					argparse.ArgumentParser()
     SCREAMING_SNAKE_CASE__     :    List[Any]      							=					pl.Trainer.add_argparse_args(parser)
     SCREAMING_SNAKE_CASE__     :    List[str]      							=					SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
     SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					parser.parse_args()

     distill_main(args)





from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    "cross_attention",
    "0.20.0",
    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
    standard_warn=False,
)


SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AttentionProcessor

class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							int						)  ->							List[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    **SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							Optional[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							List[Any]							,    **SCREAMING_SNAKE_CASE__	:							List[str]						)  ->							List[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[int]							,    *SCREAMING_SNAKE_CASE__	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							int							,    **SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							List[str]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Tuple							,    **SCREAMING_SNAKE_CASE__	:							Any						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							int:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							Any							,    **SCREAMING_SNAKE_CASE__	:							str						)  ->							Dict:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)



