


"""simple docstring"""







from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__a			 =   getLogger(__name__)


__a			 =   4
__a			 =   7

__a			 =   {
    "float16": torch.floataa,
    "float32": torch.floataa,
}






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if isinstance(_UpperCamelCase	,			torch.Tensor ):
												return image
						elif isinstance(_UpperCamelCase	,			PIL.Image.Image ):
												UpperCAmelCase__		=    [image]

						if isinstance(image[0]	,			PIL.Image.Image ):
												UpperCAmelCase__	,				UpperCAmelCase__		=    image[0].size
												UpperCAmelCase__	,				UpperCAmelCase__		=    (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

												UpperCAmelCase__		=    [np.array(i.resize((w, h) ) )[None, :] for i in image]
												UpperCAmelCase__		=    np.concatenate(_UpperCamelCase	,			axis=0 )
												UpperCAmelCase__		=    np.array(_UpperCamelCase ).astype(np.floataa ) / 255.0
												UpperCAmelCase__		=    image.transpose(0	,			3	,			1	,			2 )
												UpperCAmelCase__		=    2.0 * image - 1.0
												UpperCAmelCase__		=    torch.from_numpy(_UpperCamelCase )
						elif isinstance(image[0]	,			torch.Tensor ):
												UpperCAmelCase__		=    torch.cat(_UpperCamelCase	,			dim=0 )

						return image


class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""






							def __init__(						self :     int		, lowerCamelCase_ :     OnnxRuntimeModel		, lowerCamelCase_ :     OnnxRuntimeModel		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     OnnxRuntimeModel		, lowerCamelCase_ :     DDPMScheduler		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     int = 350		, ):
													super().__init__(
													    vae=lowerCamelCase_		, text_encoder=lowerCamelCase_		, tokenizer=lowerCamelCase_		, unet=lowerCamelCase_		, low_res_scheduler=lowerCamelCase_		, scheduler=lowerCamelCase_		, safety_checker=lowerCamelCase_		, feature_extractor=lowerCamelCase_		, watermarker=lowerCamelCase_		, max_noise_level=lowerCamelCase_		, )






							def __call__(						self :     List[str]		, lowerCamelCase_ :     Union[str, List[str]]		, lowerCamelCase_ :     Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]		, lowerCamelCase_ :     int = 75		, lowerCamelCase_ :     float = 9.0		, lowerCamelCase_ :     int = 20		, lowerCamelCase_ :     Optional[Union[str, List[str]]] = None		, lowerCamelCase_ :     Optional[int] = 1		, lowerCamelCase_ :     float = 0.0		, lowerCamelCase_ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None		, lowerCamelCase_ :     Optional[torch.FloatTensor] = None		, lowerCamelCase_ :     Optional[np.ndarray] = None		, lowerCamelCase_ :     Optional[np.ndarray] = None		, lowerCamelCase_ :     Optional[str] = "pil"		, lowerCamelCase_ :     bool = True		, lowerCamelCase_ :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None		, lowerCamelCase_ :     Optional[int] = 1		, ):
													self.check_inputs(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)

													# 2. Define call parameters
													if prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    1
													elif prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    len(lowerCamelCase_				)
													else:
																			UpperCAmelCase__		=    prompt_embeds.shape[0]

													UpperCAmelCase__		=    self._execution_device
													# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
													# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
													# corresponds to doing no classifier free guidance.
													UpperCAmelCase__		=    guidance_scale > 1.0

													# 3. Encode input prompt
													UpperCAmelCase__		=    self._encode_prompt(
													    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, prompt_embeds=lowerCamelCase_		, negative_prompt_embeds=lowerCamelCase_		, )

													UpperCAmelCase__		=    ORT_TO_PT_TYPE[str(text_embeddings.dtype				)]

													# 4. Preprocess image
													UpperCAmelCase__		=    preprocess(lowerCamelCase_				)
													UpperCAmelCase__		=    image.cpu()

													# 5. set timesteps
													self.scheduler.set_timesteps(lowerCamelCase_		, device=lowerCamelCase_				)
													UpperCAmelCase__		=    self.scheduler.timesteps

													# 5. Add noise to image
													UpperCAmelCase__		=    torch.tensor([noise_level]		, dtype=torch.long		, device=lowerCamelCase_				)
													UpperCAmelCase__		=    torch.randn(image.shape		, generator=lowerCamelCase_		, device=lowerCamelCase_		, dtype=lowerCamelCase_				)
													UpperCAmelCase__		=    self.low_res_scheduler.add_noise(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)

													UpperCAmelCase__		=    2 if do_classifier_free_guidance else 1
													UpperCAmelCase__		=    np.concatenate([image] * batch_multiplier * num_images_per_prompt				)
													UpperCAmelCase__		=    np.concatenate([noise_level] * image.shape[0]				)

													# 6. Prepare latent variables
													UpperCAmelCase__	,				UpperCAmelCase__		=    image.shape[2:]
													UpperCAmelCase__		=    self.prepare_latents(
													    batch_size * num_images_per_prompt		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

													# 7. Check that sizes of image and latents match
													UpperCAmelCase__		=    image.shape[1]
													if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																			raise ValueError(
																			    '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
																			    f""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
																			    f""" `num_channels_image`: {num_channels_image} """
																			    f""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
																			    ''' `pipeline.unet` or your `image` input.'''				)

													# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
													UpperCAmelCase__		=    self.prepare_extra_step_kwargs(lowerCamelCase_		, lowerCamelCase_				)

													UpperCAmelCase__		=    next(
													    (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')		, '''tensor(float)'''				)
													UpperCAmelCase__		=    ORT_TO_NP_TYPE[timestep_dtype]

													# 9. Denoising loop
													UpperCAmelCase__		=    len(lowerCamelCase_				) - num_inference_steps * self.scheduler.order
													with self.progress_bar(total=lowerCamelCase_				) as progress_bar:
																			for i, t in enumerate(lowerCamelCase_				):
																									# expand the latents if we are doing classifier free guidance
																									UpperCAmelCase__		=    np.concatenate([latents] * 2				) if do_classifier_free_guidance else latents

																									# concat latents, mask, masked_image_latents in the channel dimension
																									UpperCAmelCase__		=    self.scheduler.scale_model_input(lowerCamelCase_		, lowerCamelCase_				)
																									UpperCAmelCase__		=    np.concatenate([latent_model_input, image]		, axis=1				)

																									# timestep to tensor
																									UpperCAmelCase__		=    np.array([t]		, dtype=lowerCamelCase_				)

																									# predict the noise residual
																									UpperCAmelCase__		=    self.unet(
																									    sample=lowerCamelCase_		, timestep=lowerCamelCase_		, encoder_hidden_states=lowerCamelCase_		, class_labels=noise_level.astype(np.intaa				)		, )[0]

																									# perform guidance
																									if do_classifier_free_guidance:
																															UpperCAmelCase__	,				UpperCAmelCase__		=    np.split(lowerCamelCase_		, 2				)
																									UpperCAmelCase__		=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									# compute the previous noisy sample x_t -> x_t-1
																									UpperCAmelCase__		=    self.scheduler.step(
																									    torch.from_numpy(lowerCamelCase_				)		, lowerCamelCase_		, lowerCamelCase_		, **lowerCamelCase_				).prev_sample

																									# call the callback, if provided
																									if i == len(lowerCamelCase_				) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																															progress_bar.update()
																															if callback is not None and i % callback_steps == 0:
																																					callback(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)

        # 10. Post-processing
													UpperCAmelCase__		=    self.decode_latents(latents.float()				)

													# 11. Convert to PIL
													if output_type == "pil":
																			UpperCAmelCase__		=    self.numpy_to_pil(lowerCamelCase_				)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     Any				):
													UpperCAmelCase__		=    1 / 0.08333 * latents
													UpperCAmelCase__		=    self.vae(latent_sample=lowerCamelCase_				)[0]
													UpperCAmelCase__		=    np.clip(image / 2 + 0.5		, 0		, 1				)
													UpperCAmelCase__		=    image.transpose((0, 2, 3, 1)				)
													return image






							def 					lowerCAmelCase     (						self :     int		, lowerCamelCase_ :     Union[str, List[str]]		, lowerCamelCase_ :     Optional[int]		, lowerCamelCase_ :     Optional[int]		, lowerCamelCase_ :     bool		, lowerCamelCase_ :     Optional[str]		, lowerCamelCase_ :     Optional[torch.FloatTensor] = None		, lowerCamelCase_ :     Optional[torch.FloatTensor] = None		, ):
													if prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    1
													elif prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    len(lowerCamelCase_				)
													else:
																			UpperCAmelCase__		=    prompt_embeds.shape[0]

													if prompt_embeds is None:
																			UpperCAmelCase__		=    self.tokenizer(
																			    lowerCamelCase_		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, truncation=lowerCamelCase_		, return_tensors='''pt'''		, )
																			UpperCAmelCase__		=    text_inputs.input_ids
																			UpperCAmelCase__		=    self.tokenizer(lowerCamelCase_		, padding='''longest'''		, return_tensors='''pt'''				).input_ids

																			if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																			    lowerCamelCase_		, lowerCamelCase_				):
																									UpperCAmelCase__		=    self.tokenizer.batch_decode(
																									    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]				)
																									logger.warning(
																									    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																									    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""				)

																			# no positional arguments to text_encoder
																			UpperCAmelCase__		=    self.text_encoder(
																			    input_ids=text_input_ids.int().to(lowerCamelCase_				)		, )
																			UpperCAmelCase__		=    prompt_embeds[0]

													UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    prompt_embeds.shape
													# duplicate text embeddings for each generation per prompt, using mps friendly method
													UpperCAmelCase__		=    prompt_embeds.repeat(1		, lowerCamelCase_				)
													UpperCAmelCase__		=    prompt_embeds.reshape(bs_embed * num_images_per_prompt		, lowerCamelCase_		, -1				)

													# get unconditional embeddings for classifier free guidance
													if do_classifier_free_guidance and negative_prompt_embeds is None:
																			UpperCAmelCase__		=    42
																			if negative_prompt is None:
																									UpperCAmelCase__		=    [''''''] * batch_size
																			elif type(lowerCamelCase_				) is not type(lowerCamelCase_				):
																									raise TypeError(
																									    f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_				)} !="""
																									    f""" {type(lowerCamelCase_				)}."""				)
																			elif isinstance(lowerCamelCase_		, lowerCamelCase_				):
																									UpperCAmelCase__		=    [negative_prompt]
																			elif batch_size != len(lowerCamelCase_				):
																									raise ValueError(
																									    f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_				)}, but `prompt`:"""
																									    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																									    ''' the batch size of `prompt`.'''				)
																			else:
																									UpperCAmelCase__		=    negative_prompt

																			UpperCAmelCase__		=    text_input_ids.shape[-1]
																			UpperCAmelCase__		=    self.tokenizer(
																			    lowerCamelCase_		, padding='''max_length'''		, max_length=lowerCamelCase_		, truncation=lowerCamelCase_		, return_tensors='''pt'''		, )

																			# if hasattr(uncond_input, "attention_mask"):
																			#     attention_mask = uncond_input.attention_mask.to(device)
																			# else:
																			#     attention_mask = None

																			UpperCAmelCase__		=    self.text_encoder(
																			    input_ids=uncond_input.input_ids.int().to(lowerCamelCase_				)		, )
																			UpperCAmelCase__		=    uncond_embeddings[0]

													if do_classifier_free_guidance:
																			UpperCAmelCase__		=    uncond_embeddings.shape[1]
																			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																			UpperCAmelCase__		=    uncond_embeddings.repeat(1		, lowerCamelCase_				)
																			UpperCAmelCase__		=    uncond_embeddings.reshape(batch_size * num_images_per_prompt		, lowerCamelCase_		, -1				)

																			# For classifier free guidance, we need to do two forward passes.
																			# Here we concatenate the unconditional and text embeddings into a single batch
																			# to avoid doing two forward passes
																			UpperCAmelCase__		=    np.concatenate([uncond_embeddings, prompt_embeds]				)

													return prompt_embeds



"""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 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      ["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]






							def __init__(						self :     int		, lowerCamelCase_ :     Tuple				):
													assert Path(hparams.data_dir				).exists()
													UpperCAmelCase__		=    Path(hparams.output_dir				)
													self.output_dir.mkdir(exist_ok=lowerCamelCase_				)

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

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

													if hparams.length_penalty != -1:
																			UpperCAmelCase__		=    hparams.length_penalty
													UpperCAmelCase__		=    hparams.teacher  # Use teacher's tokenizer
													super().__init__(lowerCamelCase_		, model=lowerCamelCase_		, 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__		=    len(student.get_encoder().block				)
																			UpperCAmelCase__		=    len(student.get_decoder().block				)
																			UpperCAmelCase__		=    len(teacher.get_encoder().block				)
																			UpperCAmelCase__		=    len(teacher.get_decoder().block				)
													else:
																			UpperCAmelCase__		=    student.config.encoder_layers
																			UpperCAmelCase__		=    student.config.decoder_layers
																			UpperCAmelCase__		=    teacher.config.encoder_layers
																			UpperCAmelCase__		=    teacher.config.decoder_layers

													UpperCAmelCase__		=    not (hparams.student is None or hparams.teacher == hparams.student)
													UpperCAmelCase__		=    (not self.different_base_models) and hparams.alpha_hid > 0
													UpperCAmelCase__		=    self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
													# self.different_encoder determines whether we need to run the teacher encoder
													UpperCAmelCase__		=    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__		=    list(range(lowerCamelCase_				)				)
													if d_layer_ids is None:
																			UpperCAmelCase__		=    list(range(lowerCamelCase_				)				)

													UpperCAmelCase__	,				UpperCAmelCase__		=    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__		=    get_layers_to_supervise(
																									    n_student=len(self.e_layer_ids				)		, n_teacher=lowerCamelCase_				)
																									UpperCAmelCase__		=    get_layers_to_supervise(
																									    n_student=len(self.d_layer_ids				)		, n_teacher=lowerCamelCase_				)
																			else:  # student layer should emulate hidden states of the teacher layer it was copied from
																									UpperCAmelCase__		=    self.e_layer_ids
																									UpperCAmelCase__		=    self.d_layer_ids
													else:
																			UpperCAmelCase__		=    None
																			UpperCAmelCase__		=    None

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






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






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     dict				):
													UpperCAmelCase__		=    self.tokenizer.pad_token_id
													UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
													if isinstance(self.model		, lowerCamelCase_				):
																			UpperCAmelCase__		=    self.model._shift_right(lowerCamelCase_				)
													else:
																			UpperCAmelCase__		=    shift_tokens_right(lowerCamelCase_		, lowerCamelCase_				)

													# noinspection PyCallingNonCallable
													UpperCAmelCase__		=    self(
													    lowerCamelCase_		, attention_mask=lowerCamelCase_		, decoder_input_ids=lowerCamelCase_		, output_hidden_states=self.do_calc_hidden_loss		, output_attentions=lowerCamelCase_		, use_cache=lowerCamelCase_		, )
													UpperCAmelCase__		=    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__		=    nn.CrossEntropyLoss(ignore_index=lowerCamelCase_				)
																			UpperCAmelCase__		=    loss_fct(lm_logits.view(-1		, lm_logits.shape[-1]				)		, labels.view(-1				)				)
													else:
																			UpperCAmelCase__		=    nn.functional.log_softmax(lowerCamelCase_		, dim=-1				)
																			UpperCAmelCase__	,				UpperCAmelCase__		=    label_smoothed_nll_loss(
																			    lowerCamelCase_		, lowerCamelCase_		, self.hparams.label_smoothing		, ignore_index=lowerCamelCase_				)

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

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

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

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						parser.add_argument('''--teacher'''	,			type=_UpperCamelCase )
						parser.add_argument('''--alpha_ce'''	,			default=0.8	,			type=_UpperCamelCase )
						parser.add_argument('''--alpha_mlm'''	,			default=0.2	,			type=_UpperCamelCase )
						parser.add_argument('''--alpha_hid'''	,			default=0.0	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--student'''	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--student_decoder_layers'''	,			default=12	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--student_encoder_layers'''	,			default=12	,			type=_UpperCamelCase	,			required=_UpperCamelCase )
						parser.add_argument('''--no_teacher'''	,			action='''store_true'''	,			default=_UpperCamelCase )
						parser.add_argument('''--length_penalty'''	,			type=_UpperCamelCase	,			default=-1 )
						parser.add_argument('''--supervise_forward'''	,			action='''store_true'''	,			default=_UpperCamelCase )
						parser.add_argument('''--normalize_hidden'''	,			action='''store_true'''	,			default=_UpperCamelCase )
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """translation"""
							SCREAMING_SNAKE_CASE										=      ["""bleu"""]
							SCREAMING_SNAKE_CASE										=      """bleu"""






							def __init__(						self :     Any		, lowerCamelCase_ :     Any		, **lowerCamelCase_ :     str				):
													super().__init__(lowerCamelCase_		, **lowerCamelCase_				)
													assert hparams.src_lang is not None
													assert hparams.tgt_lang is not None
													UpperCAmelCase__		=    hparams.src_lang
													UpperCAmelCase__		=    hparams.tgt_lang
													if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		, lowerCamelCase_				):
																			UpperCAmelCase__		=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     List[str]		, lowerCamelCase_ :     int				):
													return calculate_bleu(lowerCamelCase_		, lowerCamelCase_				)






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     List[Any]		, lowerCamelCase_ :     str				):
													TranslationModule.add_model_specific_args(lowerCamelCase_		, lowerCamelCase_				)
													add_distill_args(lowerCamelCase_				)
													return parser






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if args.no_teacher:
												UpperCAmelCase__		=    TranslationModule if '''translation''' in args.task else SummarizationModule
						else:  # DISTILL WITH TEACHER
												UpperCAmelCase__		=    TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
						UpperCAmelCase__		=    module_cls.__name__
						print(F"""using module {args.setup_cls}""" )
						UpperCAmelCase__		=    module_cls(_UpperCamelCase )
						return model






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						Path(args.output_dir ).mkdir(exist_ok=_UpperCamelCase )
						check_output_dir(_UpperCamelCase	,			expected_items=3 )

						UpperCAmelCase__		=    create_module(_UpperCamelCase )
						return ft_main(_UpperCamelCase	,			model=_UpperCamelCase )


if __name__ == "__main__":
						__a			 =   argparse.ArgumentParser()
						__a			 =   pl.Trainer.add_argparse_args(parser)
						__a			 =   SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
						__a			 =   parser.parse_args()

						distill_main(args)