



'''simple docstring'''




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a  :     List[str]       =       """pcuenq/k-upscaler"""



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							*, _lowercase      ,							_lowercase						) ->     List[Any]:
							UpperCAmelCase	:						int					=						{
							    # norm1
							    F'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[F'''{resnet_prefix}.main.0.mapper.weight'''],
							    F'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[F'''{resnet_prefix}.main.0.mapper.bias'''],
							    # conv1
							    F'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[F'''{resnet_prefix}.main.2.weight'''],
							    F'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[F'''{resnet_prefix}.main.2.bias'''],
							    # norm2
							    F'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[F'''{resnet_prefix}.main.4.mapper.weight'''],
							    F'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[F'''{resnet_prefix}.main.4.mapper.bias'''],
							    # conv2
							    F'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[F'''{resnet_prefix}.main.6.weight'''],
							    F'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[F'''{resnet_prefix}.main.6.bias'''],
							}

							if resnet.conv_shortcut is not None:
														rv.update(
														    {
														        F'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[F'''{resnet_prefix}.skip.weight'''],
														    }						)

							return rv



def  __lowerCamelCase    (   _lowercase      ,							*, _lowercase      ,							_lowercase						) ->     List[Any]:
							UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	:						Any					=						checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3      ,							dim=0						)
							UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	:						Tuple					=						checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3      ,							dim=0						)
							UpperCAmelCase	:						Optional[int]					=						{
							    # norm
							    F'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[F'''{attention_prefix}.norm_in.mapper.weight'''],
							    F'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[F'''{attention_prefix}.norm_in.mapper.bias'''],
							    # to_q
							    F'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1						).squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
							    # to_k
							    F'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1						).squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
							    # to_v
							    F'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1						).squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
							    # to_out
							    F'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[F'''{attention_prefix}.out_proj.weight''']
							    .squeeze(-1						)
							    .squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[F'''{attention_prefix}.out_proj.bias'''],
							}

							return rv



def  __lowerCamelCase    (   _lowercase      ,							*, _lowercase      ,							_lowercase      ,							_lowercase						) ->     str:
							UpperCAmelCase	,	UpperCAmelCase	:						Optional[int]					=						checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2      ,							dim=0						)
							UpperCAmelCase	,	UpperCAmelCase	:						Union[str, Any]					=						checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2      ,							dim=0						)

							UpperCAmelCase	:						Optional[int]					=						{
							    # norm2 (ada groupnorm)
							    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
							        F'''{attention_prefix}.norm_dec.mapper.weight'''
							    ],
							    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
							        F'''{attention_prefix}.norm_dec.mapper.bias'''
							    ],
							    # layernorm on encoder_hidden_state
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
							        F'''{attention_prefix}.norm_enc.weight'''
							    ],
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
							        F'''{attention_prefix}.norm_enc.bias'''
							    ],
							    # to_q
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
							        F'''{attention_prefix}.q_proj.weight'''
							    ]
							    .squeeze(-1						)
							    .squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
							        F'''{attention_prefix}.q_proj.bias'''
							    ],
							    # to_k
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1						).squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
							    # to_v
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1						).squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
							    # to_out
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
							        F'''{attention_prefix}.out_proj.weight'''
							    ]
							    .squeeze(-1						)
							    .squeeze(-1						),
							    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
							        F'''{attention_prefix}.out_proj.bias'''
							    ],
							}

							return rv



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase						) ->     List[Any]:
							UpperCAmelCase	:						Optional[Any]					=						"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
							UpperCAmelCase	:						str					=						F'''{block_prefix}.{block_idx}'''

							UpperCAmelCase	:						List[str]					=						{}

							if not hasattr(_lowercase      ,							"""attentions"""						):
														UpperCAmelCase	:						Dict					=						1  # resnet only
							elif not block.attentions[0].add_self_attention:
														UpperCAmelCase	:						Dict					=						2  # resnet -> cross-attention
							else:
														UpperCAmelCase	:						Any					=						3  # resnet -> self-attention -> cross-attention)

							for resnet_idx, resnet in enumerate(block.resnets						):
														# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
														UpperCAmelCase	:						Union[str, Any]					=						F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
														UpperCAmelCase	:						List[Any]					=						n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
														UpperCAmelCase	:						Union[str, Any]					=						F'''{block_prefix}.{idx}''' if block_type == """up""" else F'''{block_prefix}.{idx}'''

														diffusers_checkpoint.update(
														    resnet_to_diffusers_checkpoint(
														        _lowercase      ,							_lowercase      ,							diffusers_resnet_prefix=_lowercase      ,							resnet_prefix=_lowercase						)						)

							if hasattr(_lowercase      ,							"""attentions"""						):
														for attention_idx, attention in enumerate(block.attentions						):
																					UpperCAmelCase	:						Optional[int]					=						F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
																					UpperCAmelCase	:						Optional[Any]					=						n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
																					UpperCAmelCase	:						str					=						F'''{block_prefix}.{idx}'''
																					UpperCAmelCase	:						Optional[int]					=						F'''{block_prefix}.{idx }'''
																					UpperCAmelCase	:						int					=						1 if not attention.add_self_attention else 2
																					UpperCAmelCase	:						int					=						(
																					    n * attention_idx + cross_attention_index
																					    if block_type == """up"""
																					    else n * attention_idx + cross_attention_index + 1
																					)
																					UpperCAmelCase	:						Optional[Any]					=						F'''{block_prefix}.{idx }'''

																					diffusers_checkpoint.update(
																					    cross_attn_to_diffusers_checkpoint(
																					        _lowercase      ,							diffusers_attention_prefix=_lowercase      ,							diffusers_attention_index=2      ,							attention_prefix=_lowercase      ,							)						)

																					if attention.add_self_attention is True:
																												diffusers_checkpoint.update(
																												    self_attn_to_diffusers_checkpoint(
																												        _lowercase      ,							diffusers_attention_prefix=_lowercase      ,							attention_prefix=_lowercase      ,							)						)

							return diffusers_checkpoint



def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     Any:
							UpperCAmelCase	:						Optional[Any]					=						{}

							# pre-processing
							diffusers_checkpoint.update(
							    {
							        """conv_in.weight""": checkpoint["""inner_model.proj_in.weight"""],
							        """conv_in.bias""": checkpoint["""inner_model.proj_in.bias"""],
							    }						)

							# timestep and class embedding
							diffusers_checkpoint.update(
							    {
							        """time_proj.weight""": checkpoint["""inner_model.timestep_embed.weight"""].squeeze(-1						),
							        """time_embedding.linear_1.weight""": checkpoint["""inner_model.mapping.0.weight"""],
							        """time_embedding.linear_1.bias""": checkpoint["""inner_model.mapping.0.bias"""],
							        """time_embedding.linear_2.weight""": checkpoint["""inner_model.mapping.2.weight"""],
							        """time_embedding.linear_2.bias""": checkpoint["""inner_model.mapping.2.bias"""],
							        """time_embedding.cond_proj.weight""": checkpoint["""inner_model.mapping_cond.weight"""],
							    }						)

							# down_blocks
							for down_block_idx, down_block in enumerate(model.down_blocks						):
														diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowercase      ,							_lowercase      ,							_lowercase      ,							"""down"""						)						)

							# up_blocks
							for up_block_idx, up_block in enumerate(model.up_blocks						):
														diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowercase      ,							_lowercase      ,							_lowercase      ,							"""up"""						)						)

							# post-processing
							diffusers_checkpoint.update(
							    {
							        """conv_out.weight""": checkpoint["""inner_model.proj_out.weight"""],
							        """conv_out.bias""": checkpoint["""inner_model.proj_out.bias"""],
							    }						)

							return diffusers_checkpoint



def  __lowerCamelCase    (   _lowercase						) ->     Any:
							UpperCAmelCase	:						Union[str, Any]					=						original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
							UpperCAmelCase	:						str					=						original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

							UpperCAmelCase	:						List[Any]					=						original_config["""channels"""]

							assert (
							    len(set(original_config["""depths"""]						)						) == 1
							), "UNet2DConditionModel currently do not support blocks with different number of layers"
							UpperCAmelCase	:						Union[str, Any]					=						original_config["""depths"""][0]

							UpperCAmelCase	:						Optional[Any]					=						original_config["""mapping_cond_dim"""]
							UpperCAmelCase	:						Optional[int]					=						original_config["""cross_cond_dim"""]

							UpperCAmelCase	:						str					=						[]
							UpperCAmelCase	:						Optional[int]					=						[]
							for s, c in zip(original_config["""self_attn_depths"""]      ,							original_config["""cross_attn_depths"""]						):
														if s:
																					UpperCAmelCase	:						Union[str, Any]					=						"""self"""
																					UpperCAmelCase	:						str					=						"""cross""" if c else None
														elif c:
																					UpperCAmelCase	:						Any					=						"""cross"""
																					UpperCAmelCase	:						Tuple					=						None
														else:
																					UpperCAmelCase	:						List[str]					=						None
																					UpperCAmelCase	:						Union[str, Any]					=						None
														attna_types.append(_lowercase						)
														attna_types.append(_lowercase						)

							UpperCAmelCase	:						List[str]					=						UNetaDConditionModel(
							    in_channels=_lowercase      ,							out_channels=_lowercase      ,							down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")      ,							mid_block_type=_lowercase      ,							up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")      ,							block_out_channels=_lowercase      ,							layers_per_block=_lowercase      ,							act_fn="""gelu"""      ,							norm_num_groups=_lowercase      ,							cross_attention_dim=_lowercase      ,							attention_head_dim=6_4      ,							time_cond_proj_dim=_lowercase      ,							resnet_time_scale_shift="""scale_shift"""      ,							time_embedding_type="""fourier"""      ,							timestep_post_act="""gelu"""      ,							conv_in_kernel=1      ,							conv_out_kernel=1      ,							)

							return unet



def  __lowerCamelCase    (   _lowercase						) ->     List[Any]:
							UpperCAmelCase	:						List[Any]					=						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)

							UpperCAmelCase	:						List[str]					=						huggingface_hub.hf_hub_download(_lowercase      ,							"""config_laion_text_cond_latent_upscaler_2.json"""						)
							UpperCAmelCase	:						Tuple					=						huggingface_hub.hf_hub_download(
							    _lowercase      ,							"""laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"""						)
							print(F'''loading original model configuration from {orig_config_path}'''						)
							print(F'''loading original model checkpoint from {orig_weights_path}'''						)

							print("""converting to diffusers unet"""						)
							UpperCAmelCase	:						int					=						K.config.load_config(open(_lowercase						)						)["""model"""]
							UpperCAmelCase	:						int					=						unet_model_from_original_config(_lowercase						)

							UpperCAmelCase	:						Union[str, Any]					=						torch.load(_lowercase      ,							map_location=_lowercase						)["""model_ema"""]
							UpperCAmelCase	:						List[Any]					=						unet_to_diffusers_checkpoint(_lowercase      ,							_lowercase						)

							model.load_state_dict(_lowercase      ,							strict=_lowercase						)
							model.save_pretrained(args.dump_path						)
							print(F'''saving converted unet model in {args.dump_path}'''						)


if __name__ == "__main__":
							a  :     int       =       argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
							a  :     str       =       parser.parse_args()

							main(args)






'''simple docstring'''




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      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']





	def __init__(    self						,						A       )     ->     Any:
								assert Path(hparams.data_dir       ).exists()
								UpperCAmelCase	:						Dict					=						Path(hparams.output_dir       )
								self.output_dir.mkdir(exist_ok=A       )

								UpperCAmelCase	:						int					=						self.output_dir.joinpath("""student"""       )

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

								if hparams.length_penalty != -1:
															UpperCAmelCase	:						int					=						hparams.length_penalty
								UpperCAmelCase	:						str					=						hparams.teacher  # Use teacher's tokenizer
								super().__init__(A						,						model=A						,						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":
															UpperCAmelCase	:						Optional[int]					=						len(student.get_encoder().block       )
															UpperCAmelCase	:						Any					=						len(student.get_decoder().block       )
															UpperCAmelCase	:						Union[str, Any]					=						len(teacher.get_encoder().block       )
															UpperCAmelCase	:						Optional[int]					=						len(teacher.get_decoder().block       )
								else:
															UpperCAmelCase	:						Dict					=						student.config.encoder_layers
															UpperCAmelCase	:						int					=						student.config.decoder_layers
															UpperCAmelCase	:						Any					=						teacher.config.encoder_layers
															UpperCAmelCase	:						List[Any]					=						teacher.config.decoder_layers

								UpperCAmelCase	:						Dict					=						not (hparams.student is None or hparams.teacher == hparams.student)
								UpperCAmelCase	:						Optional[int]					=						(not self.different_base_models) and hparams.alpha_hid > 0
								UpperCAmelCase	:						Union[str, Any]					=						self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
								# self.different_encoder determines whether we need to run the teacher encoder
								UpperCAmelCase	:						Dict					=						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:
															UpperCAmelCase	:						Tuple					=						list(range(A       )       )
								if d_layer_ids is None:
															UpperCAmelCase	:						List[Any]					=						list(range(A       )       )

								UpperCAmelCase	,	UpperCAmelCase	:						str					=						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:
																						UpperCAmelCase	:						List[str]					=						get_layers_to_supervise(
																						    n_student=len(self.e_layer_ids       )						,						n_teacher=A       )
																						UpperCAmelCase	:						int					=						get_layers_to_supervise(
																						    n_student=len(self.d_layer_ids       )						,						n_teacher=A       )
															else:  # student layer should emulate hidden states of the teacher layer it was copied from
																						UpperCAmelCase	:						Dict					=						self.e_layer_ids
																						UpperCAmelCase	:						List[Any]					=						self.d_layer_ids
								else:
															UpperCAmelCase	:						Tuple					=						None
															UpperCAmelCase	:						Union[str, Any]					=						None

								UpperCAmelCase	:						Any					=						nn.KLDivLoss(reduction="""batchmean"""       )
								UpperCAmelCase	:						Tuple					=						2.0
								UpperCAmelCase	:						Tuple					=						hparams.alpha_mlm
								UpperCAmelCase	:						List[str]					=						hparams.alpha_ce
								UpperCAmelCase	:						Dict					=						hparams.alpha_hid
								gc.collect()
								torch.cuda.empty_cache()





	def 					_lowercase(    self						,						A						,						A						,						A       )     ->     Any:
								UpperCAmelCase	:						Union[str, Any]					=						mask[:, :, None].expand_as(A       )
								UpperCAmelCase	:						str					=						s_logits.size(-1       )
								UpperCAmelCase	:						Optional[Any]					=						torch.masked_select(A						,						A       )  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCAmelCase	:						List[str]					=						torch.masked_select(A						,						A       )  # (bs * seq_length * voc_size) modulo the 1s in mask
								UpperCAmelCase	:						List[str]					=						s_logits_slct.view(-1						,						A       )  # (bs * seq_length, voc_size) modulo the 1s in mask
								UpperCAmelCase	:						Optional[Any]					=						t_logits_slct.view(-1						,						A       )  # (bs * seq_length, voc_size) modulo the 1s in mask
								assert t_logits_slct.size() == s_logits_slct.size()
								UpperCAmelCase	:						int					=						(
								    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 					_lowercase(    A						,						A       )     ->     Optional[int]:
								SummarizationModule.add_model_specific_args(A						,						A       )
								add_distill_args(A       )
								return parser





	def 					_lowercase(    self						,						A       )     ->     tuple:
								UpperCAmelCase	:						Union[str, Any]					=						self.tokenizer.pad_token_id
								UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	:						Optional[int]					=						batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
								if isinstance(self.model						,						A       ):
															UpperCAmelCase	:						List[str]					=						self.model._shift_right(A       )
								else:
															UpperCAmelCase	:						str					=						shift_tokens_right(A						,						A       )

								# noinspection PyCallingNonCallable
								UpperCAmelCase	:						Union[str, Any]					=						self(
								    A						,						attention_mask=A						,						decoder_input_ids=A						,						output_hidden_states=self.do_calc_hidden_loss						,						output_attentions=A						,						use_cache=A						,						)
								UpperCAmelCase	:						Dict					=						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
															UpperCAmelCase	:						Optional[Any]					=						nn.CrossEntropyLoss(ignore_index=A       )
															UpperCAmelCase	:						Tuple					=						loss_fct(lm_logits.view(-1						,						lm_logits.shape[-1]       )						,						labels.view(-1       )       )
								else:
															UpperCAmelCase	:						int					=						nn.functional.log_softmax(A						,						dim=-1       )
															UpperCAmelCase	,	UpperCAmelCase	:						List[Any]					=						label_smoothed_nll_loss(
															    A						,						A						,						self.hparams.label_smoothing						,						ignore_index=A       )

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

								UpperCAmelCase	:						Union[str, Any]					=						student_outputs[
								    """encoder_last_hidden_state"""
								]  # use this unless self.different_base_models
								UpperCAmelCase	,	UpperCAmelCase	:						Optional[Any]					=						zero_tensor(), zero_tensor()
								if self.different_encoder:  # compute encoder hidden state loss
															UpperCAmelCase	:						int					=						self.teacher.get_encoder()(
															    A						,						attention_mask=A						,						output_hidden_states=self.do_calc_hidden_loss						,						)
															if self.different_base_models:
																						UpperCAmelCase	:						Any					=						all_teacher_encoder_outputs["""last_hidden_state"""]
															elif self.do_calc_hidden_loss:
																						UpperCAmelCase	:						List[str]					=						self.calc_hidden_loss(
																						    A						,						student_outputs["""encoder_hidden_states"""]						,						all_teacher_encoder_outputs["""hidden_states"""]						,						self.e_matches						,						normalize_hidden=self.hparams.normalize_hidden						,						)

								UpperCAmelCase	:						str					=						self.teacher(
								    A						,						attention_mask=A						,						encoder_outputs=(teacher_enc_outputs,)						,						decoder_input_ids=A						,						output_hidden_states=self.do_calc_hidden_loss						,						use_cache=A						,						)
								UpperCAmelCase	:						Union[str, Any]					=						decoder_input_ids.ne(A       )
								UpperCAmelCase	:						Union[str, Any]					=						self.calc_ce_loss(A						,						A						,						teacher_outputs["""logits"""]       )
								if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
															UpperCAmelCase	:						List[Any]					=						self.calc_hidden_loss(
															    A						,						student_outputs["""decoder_hidden_states"""]						,						teacher_outputs["""decoder_hidden_states"""]						,						self.d_matches						,						normalize_hidden=self.hparams.normalize_hidden						,						)

								UpperCAmelCase	:						Dict					=						(
								    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 					_lowercase(    A						,						A						,						A						,						A						,						A       )     ->     Optional[Any]:
								UpperCAmelCase	:						Optional[int]					=						"""expected list or tuple for hidden_states, got tensor of shape: """
								assert not isinstance(A						,						torch.Tensor       ), f'''{msg}{hidden_states.shape}'''
								assert not isinstance(A						,						torch.Tensor       ), f'''{msg}{hidden_states_T.shape}'''
								UpperCAmelCase	:						Optional[Any]					=						attention_mask.to(hidden_states[0]       )
								UpperCAmelCase	:						str					=						mask.sum() * hidden_states[0].size(-1       )
								UpperCAmelCase	:						Dict					=						torch.stack([hidden_states[i] for i in range(len(A       )       )]       )
								UpperCAmelCase	:						Any					=						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:
															UpperCAmelCase	:						Union[str, Any]					=						nn.functional.layer_norm(A						,						student_states.shape[1:]       )
															UpperCAmelCase	:						str					=						nn.functional.layer_norm(A						,						teacher_states.shape[1:]       )
								UpperCAmelCase	:						List[str]					=						nn.functional.mse_loss(A						,						A						,						reduction="""none"""       )
								UpperCAmelCase	:						List[Any]					=						(mse * mask.unsqueeze(0       ).unsqueeze(-1       )).sum() / valid_count
								return masked_mse




def  __lowerCamelCase    (   _lowercase						) ->     Dict:
							# 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=_lowercase						)
							parser.add_argument("""--alpha_ce"""      ,							default=0.8      ,							type=_lowercase						)
							parser.add_argument("""--alpha_mlm"""      ,							default=0.2      ,							type=_lowercase						)
							parser.add_argument("""--alpha_hid"""      ,							default=0.0      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--student"""      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--student_decoder_layers"""      ,							default=1_2      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--student_encoder_layers"""      ,							default=1_2      ,							type=_lowercase      ,							required=_lowercase						)
							parser.add_argument("""--no_teacher"""      ,							action="""store_true"""      ,							default=_lowercase						)
							parser.add_argument("""--length_penalty"""      ,							type=_lowercase      ,							default=-1						)
							parser.add_argument("""--supervise_forward"""      ,							action="""store_true"""      ,							default=_lowercase						)
							parser.add_argument("""--normalize_hidden"""      ,							action="""store_true"""      ,							default=_lowercase						)

class      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					'translation'
	lowercase		=					['bleu']
	lowercase		=					'bleu'





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





	def 					_lowercase(    self						,						A						,						A       )     ->     dict:
								return calculate_bleu(A						,						A       )






	@staticmethod
	def 					_lowercase(    A						,						A       )     ->     Dict:
								TranslationModule.add_model_specific_args(A						,						A       )
								add_distill_args(A       )
								return parser




def  __lowerCamelCase    (   _lowercase						) ->     Tuple:
							if args.no_teacher:
														UpperCAmelCase	:						Optional[int]					=						TranslationModule if """translation""" in args.task else SummarizationModule
							else:  # DISTILL WITH TEACHER
														UpperCAmelCase	:						List[Any]					=						TranslationDistiller if """translation""" in args.task else SummarizationDistiller
							UpperCAmelCase	:						str					=						module_cls.__name__
							print(F'''using module {args.setup_cls}'''						)
							UpperCAmelCase	:						Optional[Any]					=						module_cls(_lowercase						)
							return model



def  __lowerCamelCase    (   _lowercase						) ->     Optional[int]:
							Path(args.output_dir						).mkdir(exist_ok=_lowercase						)
							check_output_dir(_lowercase      ,							expected_items=3						)

							UpperCAmelCase	:						int					=						create_module(_lowercase						)
							return ft_main(_lowercase      ,							model=_lowercase						)


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

							distill_main(args)

