



'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class      UpperCamelCase_					(				__magic_name__ ,	__magic_name__ ,	__magic_name__ ,	unittest.TestCase     ):
	lowercase		=					StableDiffusionImageVariationPipeline
	lowercase		=					IMAGE_VARIATION_PARAMS
	lowercase		=					IMAGE_VARIATION_BATCH_PARAMS
	lowercase		=					frozenset([]     )
	# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase		=					frozenset([]     )





	def 					_lowercase(    self       )     ->     int:
								torch.manual_seed(0       )
								UpperCAmelCase	:						List[Any]					=						UNetaDConditionModel(
								    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=4						,						out_channels=4						,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,						cross_attention_dim=32						,						)
								UpperCAmelCase	:						int					=						PNDMScheduler(skip_prk_steps=A       )
								torch.manual_seed(0       )
								UpperCAmelCase	:						Union[str, Any]					=						AutoencoderKL(
								    block_out_channels=[32, 64]						,						in_channels=3						,						out_channels=3						,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,						latent_channels=4						,						)
								torch.manual_seed(0       )
								UpperCAmelCase	:						int					=						CLIPVisionConfig(
								    hidden_size=32						,						projection_dim=32						,						intermediate_size=37						,						layer_norm_eps=1e-05						,						num_attention_heads=4						,						num_hidden_layers=5						,						image_size=32						,						patch_size=4						,						)
								UpperCAmelCase	:						str					=						CLIPVisionModelWithProjection(A       )
								UpperCAmelCase	:						int					=						CLIPImageProcessor(crop_size=32						,						size=32       )

								UpperCAmelCase	:						Optional[int]					=						{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """image_encoder""": image_encoder,
								    """feature_extractor""": feature_extractor,
								    """safety_checker""": None,
								}
								return components





	def 					_lowercase(    self						,						A						,						A=0       )     ->     Union[str, Any]:
								UpperCAmelCase	:						Optional[Any]					=						floats_tensor((1, 3, 32, 32)						,						rng=random.Random(A       )       )
								UpperCAmelCase	:						Dict					=						image.cpu().permute(0						,						2						,						3						,						1       )[0]
								UpperCAmelCase	:						List[str]					=						Image.fromarray(np.uinta(A       )       ).convert("""RGB"""       ).resize((32, 32)       )
								if str(A       ).startswith("""mps"""       ):
															UpperCAmelCase	:						Any					=						torch.manual_seed(A       )
								else:
															UpperCAmelCase	:						int					=						torch.Generator(device=A       ).manual_seed(A       )
								UpperCAmelCase	:						Any					=						{
								    """image""": image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs





	def 					_lowercase(    self       )     ->     Union[str, Any]:
								UpperCAmelCase	:						Optional[int]					=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase	:						Tuple					=						self.get_dummy_components()
								UpperCAmelCase	:						Dict					=						StableDiffusionImageVariationPipeline(**A       )
								UpperCAmelCase	:						Optional[int]					=						sd_pipe.to(A       )
								sd_pipe.set_progress_bar_config(disable=A       )

								UpperCAmelCase	:						int					=						self.get_dummy_inputs(A       )
								UpperCAmelCase	:						Optional[Any]					=						sd_pipe(**A       ).images
								UpperCAmelCase	:						int					=						image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								UpperCAmelCase	:						List[str]					=						np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]       )

								assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3





	def 					_lowercase(    self       )     ->     Optional[Any]:
								UpperCAmelCase	:						List[Any]					=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase	:						Optional[int]					=						self.get_dummy_components()
								UpperCAmelCase	:						Optional[Any]					=						StableDiffusionImageVariationPipeline(**A       )
								UpperCAmelCase	:						List[Any]					=						sd_pipe.to(A       )
								sd_pipe.set_progress_bar_config(disable=A       )

								UpperCAmelCase	:						Any					=						self.get_dummy_inputs(A       )
								UpperCAmelCase	:						Optional[int]					=						2 * [inputs["""image"""]]
								UpperCAmelCase	:						Optional[int]					=						sd_pipe(**A       )

								UpperCAmelCase	:						int					=						output.images

								UpperCAmelCase	:						Optional[int]					=						image[-1, -3:, -3:, -1]

								assert image.shape == (2, 64, 64, 3)
								UpperCAmelCase	:						Union[str, Any]					=						np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]       )

								assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3






	def 					_lowercase(    self       )     ->     int:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3       )




@slow
@require_torch_gpu
class      UpperCamelCase_					(				unittest.TestCase     ):





	def 					_lowercase(    self       )     ->     List[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





	def 					_lowercase(    self						,						A						,						A="cpu"						,						A=torch.floataa						,						A=0       )     ->     str:
								UpperCAmelCase	:						List[Any]					=						torch.Generator(device=A       ).manual_seed(A       )
								UpperCAmelCase	:						int					=						load_image(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/input_image_vermeer.png"""       )
								UpperCAmelCase	:						List[Any]					=						np.random.RandomState(A       ).standard_normal((1, 4, 64, 64)       )
								UpperCAmelCase	:						Optional[Any]					=						torch.from_numpy(A       ).to(device=A						,						dtype=A       )
								UpperCAmelCase	:						str					=						{
								    """image""": init_image,
								    """latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 3,
								    """guidance_scale""": 7.5,
								    """output_type""": """numpy""",
								}
								return inputs





	def 					_lowercase(    self       )     ->     Optional[Any]:
								UpperCAmelCase	:						Optional[Any]					=						StableDiffusionImageVariationPipeline.from_pretrained(
								    """lambdalabs/sd-image-variations-diffusers"""						,						safety_checker=A       )
								UpperCAmelCase	:						Any					=						sd_pipe.to(A       )
								sd_pipe.set_progress_bar_config(disable=A       )

								UpperCAmelCase	:						Dict					=						self.get_inputs(A       )
								UpperCAmelCase	:						Optional[int]					=						sd_pipe(**A       ).images
								UpperCAmelCase	:						Dict					=						image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase	:						List[Any]					=						np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]       )
								assert np.abs(image_slice - expected_slice       ).max() < 6e-3





	def 					_lowercase(    self       )     ->     Union[str, Any]:
								UpperCAmelCase	:						List[Any]					=						0

								def callback_fn(A						,						A						,						A       ) -> None:
															UpperCAmelCase	:						Union[str, Any]					=						True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																						UpperCAmelCase	:						str					=						latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						UpperCAmelCase	:						int					=						latents[0, -3:, -3:, -1]
																						UpperCAmelCase	:						str					=						np.array(
																						    [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]       )

																						assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2
															elif step == 2:
																						UpperCAmelCase	:						Dict					=						latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						UpperCAmelCase	:						Optional[int]					=						latents[0, -3:, -3:, -1]
																						UpperCAmelCase	:						Optional[int]					=						np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]       )

																						assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5e-2

								UpperCAmelCase	:						Optional[int]					=						False

								UpperCAmelCase	:						List[str]					=						StableDiffusionImageVariationPipeline.from_pretrained(
								    """fusing/sd-image-variations-diffusers"""						,						safety_checker=A						,						torch_dtype=torch.floataa						,						)
								pipe.to(A       )
								pipe.set_progress_bar_config(disable=A       )
								pipe.enable_attention_slicing()

								UpperCAmelCase	:						Union[str, Any]					=						self.get_inputs(A						,						dtype=torch.floataa       )
								pipe(**A						,						callback=A						,						callback_steps=1       )
								assert callback_fn.has_been_called
								assert number_of_steps == inputs["num_inference_steps"]






	def 					_lowercase(    self       )     ->     Tuple:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCAmelCase	:						Any					=						"""fusing/sd-image-variations-diffusers"""
								UpperCAmelCase	:						Tuple					=						StableDiffusionImageVariationPipeline.from_pretrained(
								    A						,						safety_checker=A						,						torch_dtype=torch.floataa       )
								UpperCAmelCase	:						List[Any]					=						pipe.to(A       )
								pipe.set_progress_bar_config(disable=A       )
								pipe.enable_attention_slicing(1       )
								pipe.enable_sequential_cpu_offload()

								UpperCAmelCase	:						List[str]					=						self.get_inputs(A						,						dtype=torch.floataa       )
								UpperCAmelCase	:						List[str]					=						pipe(**A       )

								UpperCAmelCase	:						Any					=						torch.cuda.max_memory_allocated()
								# make sure that less than 2.6 GB is allocated
								assert mem_bytes < 2.6 * 10**9




@nightly
@require_torch_gpu
class      UpperCamelCase_					(				unittest.TestCase     ):





	def 					_lowercase(    self       )     ->     Optional[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





	def 					_lowercase(    self						,						A						,						A="cpu"						,						A=torch.floataa						,						A=0       )     ->     Dict:
								UpperCAmelCase	:						List[Any]					=						torch.Generator(device=A       ).manual_seed(A       )
								UpperCAmelCase	:						str					=						load_image(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/input_image_vermeer.png"""       )
								UpperCAmelCase	:						str					=						np.random.RandomState(A       ).standard_normal((1, 4, 64, 64)       )
								UpperCAmelCase	:						List[Any]					=						torch.from_numpy(A       ).to(device=A						,						dtype=A       )
								UpperCAmelCase	:						Optional[int]					=						{
								    """image""": init_image,
								    """latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 50,
								    """guidance_scale""": 7.5,
								    """output_type""": """numpy""",
								}
								return inputs





	def 					_lowercase(    self       )     ->     Union[str, Any]:
								UpperCAmelCase	:						str					=						StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""       )
								sd_pipe.to(A       )
								sd_pipe.set_progress_bar_config(disable=A       )

								UpperCAmelCase	:						Any					=						self.get_inputs(A       )
								UpperCAmelCase	:						str					=						sd_pipe(**A       ).images[0]

								UpperCAmelCase	:						Tuple					=						load_numpy(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""       )
								UpperCAmelCase	:						Tuple					=						np.abs(expected_image - image       ).max()
								assert max_diff < 1e-3






	def 					_lowercase(    self       )     ->     List[str]:
								UpperCAmelCase	:						Union[str, Any]					=						StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""       )
								UpperCAmelCase	:						Any					=						DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config       )
								sd_pipe.to(A       )
								sd_pipe.set_progress_bar_config(disable=A       )

								UpperCAmelCase	:						Tuple					=						self.get_inputs(A       )
								UpperCAmelCase	:						Optional[int]					=						25
								UpperCAmelCase	:						Tuple					=						sd_pipe(**A       ).images[0]

								UpperCAmelCase	:						str					=						load_numpy(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
								    """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""       )
								UpperCAmelCase	:						Union[str, Any]					=						np.abs(expected_image - image       ).max()
								assert max_diff < 1e-3






'''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)

