



'''simple docstring'''



import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
				import torch






@require_flax
class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						Optional[int]    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						int=14    ,			__UpperCamelCase		:						List[str]=7    ,			__UpperCamelCase		:						Tuple=True    ,			__UpperCamelCase		:						List[str]=True    ,			__UpperCamelCase		:						str=True    ,			__UpperCamelCase		:						Union[str, Any]=99    ,			__UpperCamelCase		:						Tuple=32    ,			__UpperCamelCase		:						Union[str, Any]=5    ,			__UpperCamelCase		:						Tuple=4    ,			__UpperCamelCase		:						Optional[int]=37    ,			__UpperCamelCase		:						Tuple="gelu"    ,			__UpperCamelCase		:						str=0.1    ,			__UpperCamelCase		:						Dict=0.1    ,			__UpperCamelCase		:						Any=512    ,			__UpperCamelCase		:						List[str]=0.02    ,			__UpperCamelCase		:						Dict=None    ,			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   List[str]      =    parent
							snake_case__      :   Tuple      =    batch_size
							snake_case__      :   List[str]      =    seq_length
							snake_case__      :   str      =    is_training
							snake_case__      :   Optional[Any]      =    use_input_mask
							snake_case__      :   str      =    use_labels
							snake_case__      :   int      =    vocab_size
							snake_case__      :   Optional[int]      =    d_model
							snake_case__      :   Union[str, Any]      =    num_hidden_layers
							snake_case__      :   Any      =    num_attention_heads
							snake_case__      :   List[Any]      =    ffn_dim
							snake_case__      :   Tuple      =    activation_function
							snake_case__      :   int      =    activation_dropout
							snake_case__      :   Optional[Any]      =    attention_dropout
							snake_case__      :   Tuple      =    max_position_embeddings
							snake_case__      :   Optional[int]      =    initializer_range
							snake_case__      :   Tuple      =    None
							snake_case__      :   Optional[int]      =    0
							snake_case__      :   str      =    2
							snake_case__      :   List[str]      =    1



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   Dict      =    np.clip(ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size			)    ,			3    ,			self.vocab_size			)

							snake_case__      :   Union[str, Any]      =    None
							if self.use_input_mask:
											snake_case__      :   int      =    random_attention_mask([self.batch_size, self.seq_length]			)

							snake_case__      :   List[Any]      =    XGLMConfig(
							    vocab_size=self.vocab_size    ,			d_model=self.hidden_size    ,			num_layers=self.num_hidden_layers    ,			attention_heads=self.num_attention_heads    ,			ffn_dim=self.ffn_dim    ,			activation_function=self.activation_function    ,			activation_dropout=self.activation_dropout    ,			attention_dropout=self.attention_dropout    ,			max_position_embeddings=self.max_position_embeddings    ,			initializer_range=self.initializer_range    ,			use_cache=__UpperCamelCase    ,			bos_token_id=self.bos_token_id    ,			eos_token_id=self.eos_token_id    ,			pad_token_id=self.pad_token_id    ,			)

							return (config, input_ids, input_mask)



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    self.prepare_config_and_inputs()
							snake_case__  ,      snake_case__  ,      snake_case__      :   int      =    config_and_inputs
							snake_case__      :   str      =    {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Dict:
							"""simple docstring"""





							snake_case__  ,      snake_case__  ,      snake_case__      :   Optional[Any]      =    self.prepare_config_and_inputs()

							snake_case__      :   str      =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
							snake_case__      :   Dict      =    ids_tensor([self.batch_size, self.seq_length]    ,			vocab_size=2			)

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





							snake_case__      :   Tuple      =    20
							snake_case__      :   Any      =    model_class_name(__UpperCamelCase			)

							snake_case__      :   List[str]      =    model.init_cache(input_ids.shape[0]    ,			__UpperCamelCase			)
							snake_case__      :   str      =    jnp.ones((input_ids.shape[0], max_decoder_length)    ,			dtype='''i4'''			)

							snake_case__      :   Any      =    jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :]    ,			(input_ids.shape[0], input_ids.shape[-1] - 1)			)
							snake_case__      :   Optional[int]      =    model(
							    input_ids[:, :-1]    ,			attention_mask=__UpperCamelCase    ,			past_key_values=__UpperCamelCase    ,			position_ids=__UpperCamelCase    ,			)

							snake_case__      :   Tuple      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,			dtype='''i4'''			)
							snake_case__      :   Tuple      =    model(
							    input_ids[:, -1:]    ,			attention_mask=__UpperCamelCase    ,			past_key_values=outputs_cache.past_key_values    ,			position_ids=__UpperCamelCase    ,			)

							snake_case__      :   List[Any]      =    model(__UpperCamelCase			)

							snake_case__      :   List[str]      =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
							self.parent.assertTrue(diff < 1e-3    ,			msg=F'''Max diff is {diff}'''			)







			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						List[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    20
							snake_case__      :   List[Any]      =    model_class_name(__UpperCamelCase			)

							snake_case__      :   Dict      =    jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]    ,			axis=-1    ,			)

							snake_case__      :   Any      =    model.init_cache(input_ids.shape[0]    ,			__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1			)[None, :]    ,			(input_ids.shape[0], input_ids.shape[-1] - 1)			)

							snake_case__      :   Any      =    model(
							    input_ids[:, :-1]    ,			attention_mask=__UpperCamelCase    ,			past_key_values=__UpperCamelCase    ,			position_ids=__UpperCamelCase    ,			)
							snake_case__      :   Union[str, Any]      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,			dtype='''i4'''			)
							snake_case__      :   List[Any]      =    model(
							    input_ids[:, -1:]    ,			past_key_values=outputs_cache.past_key_values    ,			attention_mask=__UpperCamelCase    ,			position_ids=__UpperCamelCase    ,			)

							snake_case__      :   str      =    model(__UpperCamelCase    ,			attention_mask=__UpperCamelCase			)
							snake_case__      :   Any      =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
							self.parent.assertTrue(diff < 1e-3    ,			msg=F'''Max diff is {diff}'''			)






@require_sentencepiece
@require_flax
class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__,	unittest.TestCase      ):
			A__								=  (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			A__								=  (FlaxXGLMForCausalLM,) if is_flax_available() else ()



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   str      =    FlaxXGLMModelTester(self			)



			def 				lowerCAmelCase (     self		:						Any			)      ->      str:
							"""simple docstring"""





							for model_class_name in self.all_model_classes:
											snake_case__  ,      snake_case__  ,      snake_case__      :   List[str]      =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      List[Any]:
							"""simple docstring"""





							for model_class_name in self.all_model_classes:
											snake_case__  ,      snake_case__  ,      snake_case__      :   List[str]      =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    __UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)



			@slow
			def 				lowerCAmelCase (     self		:						str			)      ->      Any:
							"""simple docstring"""





							snake_case__      :   str      =    XGLMTokenizer.from_pretrained('''XGLM'''    ,			padding_side='''left'''			)
							snake_case__      :   Dict      =    tokenizer(['''Hello this is a long string''', '''Hey''']    ,			return_tensors='''np'''    ,			padding=__UpperCamelCase    ,			truncation=__UpperCamelCase			)

							snake_case__      :   Optional[int]      =    FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''			)
							snake_case__      :   Any      =    1
							snake_case__      :   Optional[int]      =    False

							snake_case__      :   Dict      =    jax.jit(model.generate			)

							snake_case__      :   int      =    jit_generate(inputs['''input_ids''']    ,			attention_mask=inputs['''attention_mask''']			).sequences

							snake_case__      :   int      =    tokenizer.batch_decode(__UpperCamelCase    ,			skip_special_tokens=__UpperCamelCase			)

							snake_case__      :   Any      =    [
							    '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
							    '''Hey, I\'m a newbie to the forum and I\'''',
							]

							self.assertListEqual(__UpperCamelCase    ,			__UpperCamelCase			)



			@is_pt_flax_cross_test
			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      str:
							"""simple docstring"""





							snake_case__  ,      snake_case__      :   List[str]      =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__			):
															# prepare inputs
															snake_case__      :   Any      =    self._prepare_for_class(__UpperCamelCase    ,			__UpperCamelCase			)
															snake_case__      :   Any      =    {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															snake_case__      :   List[Any]      =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
															snake_case__      :   Any      =    getattr(__UpperCamelCase    ,			__UpperCamelCase			)

															snake_case__  ,      snake_case__      :   Tuple      =    pt_inputs['''input_ids'''].shape
															snake_case__      :   List[Any]      =    np.random.randint(0    ,			seq_length - 1    ,			size=(batch_size,)			)
															for batch_idx, start_index in enumerate(__UpperCamelCase			):
																			snake_case__      :   Dict      =    0
																			snake_case__      :   Optional[int]      =    1
																			snake_case__      :   Any      =    0
																			snake_case__      :   Dict      =    1
															snake_case__      :   str      =    pt_model_class(__UpperCamelCase			).eval()
															# Flax models don't use the `use_cache` option and cache is not returned as a default.
															# So we disable `use_cache` here for PyTorch model.
															snake_case__      :   Optional[int]      =    False
															snake_case__      :   List[Any]      =    model_class(__UpperCamelCase    ,			dtype=jnp.floataa			)
															snake_case__      :   Optional[int]      =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,			__UpperCamelCase			)
															snake_case__      :   Any      =    fx_state

															with torch.no_grad():
																			snake_case__      :   Dict      =    pt_model(**__UpperCamelCase			).to_tuple()

															snake_case__      :   Any      =    fx_model(**__UpperCamelCase			).to_tuple()
															self.assertEqual(len(__UpperCamelCase			)    ,			len(__UpperCamelCase			)    ,			'''Output lengths differ between Flax and PyTorch'''			)
															for fx_output, pt_output in zip(__UpperCamelCase    ,			__UpperCamelCase			):
																			self.assert_almost_equals(fx_output[:, -1]    ,			pt_output[:, -1].numpy()    ,			4e-2			)

															with tempfile.TemporaryDirectory() as tmpdirname:
																			pt_model.save_pretrained(__UpperCamelCase			)
																			snake_case__      :   List[Any]      =    model_class.from_pretrained(__UpperCamelCase    ,			from_pt=__UpperCamelCase			)

															snake_case__      :   str      =    fx_model_loaded(**__UpperCamelCase			).to_tuple()
															self.assertEqual(
															    len(__UpperCamelCase			)    ,			len(__UpperCamelCase			)    ,			'''Output lengths differ between Flax and PyTorch'''			)
															for fx_output_loaded, pt_output in zip(__UpperCamelCase    ,			__UpperCamelCase			):
																			self.assert_almost_equals(fx_output_loaded[:, -1]    ,			pt_output[:, -1].numpy()    ,			4e-2			)



			@is_pt_flax_cross_test
			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      Tuple:
							"""simple docstring"""





							snake_case__  ,      snake_case__      :   str      =    self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__			):
															# prepare inputs
															snake_case__      :   str      =    self._prepare_for_class(__UpperCamelCase    ,			__UpperCamelCase			)
															snake_case__      :   Dict      =    {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															snake_case__      :   Tuple      =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
															snake_case__      :   Tuple      =    getattr(__UpperCamelCase    ,			__UpperCamelCase			)

															snake_case__      :   Optional[Any]      =    pt_model_class(__UpperCamelCase			).eval()
															snake_case__      :   Optional[int]      =    False
															snake_case__      :   Optional[Any]      =    model_class(__UpperCamelCase    ,			dtype=jnp.floataa			)

															snake_case__      :   Any      =    load_flax_weights_in_pytorch_model(__UpperCamelCase    ,			fx_model.params			)
															snake_case__  ,      snake_case__      :   List[Any]      =    pt_inputs['''input_ids'''].shape
															snake_case__      :   Optional[Any]      =    np.random.randint(0    ,			seq_length - 1    ,			size=(batch_size,)			)
															for batch_idx, start_index in enumerate(__UpperCamelCase			):
																			snake_case__      :   Union[str, Any]      =    0
																			snake_case__      :   Optional[int]      =    1
																			snake_case__      :   int      =    0
																			snake_case__      :   Any      =    1

															# make sure weights are tied in PyTorch
															pt_model.tie_weights()

															with torch.no_grad():
																			snake_case__      :   Any      =    pt_model(**__UpperCamelCase			).to_tuple()

															snake_case__      :   str      =    fx_model(**__UpperCamelCase			).to_tuple()
															self.assertEqual(len(__UpperCamelCase			)    ,			len(__UpperCamelCase			)    ,			'''Output lengths differ between Flax and PyTorch'''			)
															for fx_output, pt_output in zip(__UpperCamelCase    ,			__UpperCamelCase			):
																			self.assert_almost_equals(fx_output[:, -1]    ,			pt_output[:, -1].numpy()    ,			4e-2			)

															with tempfile.TemporaryDirectory() as tmpdirname:
																			fx_model.save_pretrained(__UpperCamelCase			)
																			snake_case__      :   str      =    pt_model_class.from_pretrained(__UpperCamelCase    ,			from_flax=__UpperCamelCase			)

															with torch.no_grad():
																			snake_case__      :   Optional[int]      =    pt_model_loaded(**__UpperCamelCase			).to_tuple()

															self.assertEqual(
															    len(__UpperCamelCase			)    ,			len(__UpperCamelCase			)    ,			'''Output lengths differ between Flax and PyTorch'''			)
															for fx_output, pt_output in zip(__UpperCamelCase    ,			__UpperCamelCase			):
																			self.assert_almost_equals(fx_output[:, -1]    ,			pt_output[:, -1].numpy()    ,			4e-2			)







			@slow
			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      List[Any]:
							"""simple docstring"""





							for model_class_name in self.all_model_classes:
											snake_case__      :   Optional[int]      =    model_class_name.from_pretrained('''facebook/xglm-564M'''			)
											snake_case__      :   List[str]      =    model(np.ones((1, 1)			)			)
											self.assertIsNotNone(__UpperCamelCase			)






"""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      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				str   			=      ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







       def __init__(       self				,lowerCamelCase_		):



          '''simple docstring'''

          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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _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  _snake_case      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          SummarizationModule.add_model_specific_args(lowerCamelCase_				,lowerCamelCase_		)
          add_distill_args(lowerCamelCase_		)
          return parser







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _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  _snake_case      (       lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _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       __UpperCAmelCase		(		a_      :		List[str]					)  ->							Tuple:
   # 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=a_					)
   parser.add_argument("--alpha_ce"     ,	default=0.8     ,	type=a_					)
   parser.add_argument("--alpha_mlm"     ,	default=0.2     ,	type=a_					)
   parser.add_argument("--alpha_hid"     ,	default=0.0     ,	type=a_     ,	required=a_					)
   parser.add_argument("--student"     ,	type=a_     ,	required=a_					)
   parser.add_argument("--student_decoder_layers"     ,	default=12     ,	type=a_     ,	required=a_					)
   parser.add_argument("--student_encoder_layers"     ,	default=12     ,	type=a_     ,	required=a_					)
   parser.add_argument("--no_teacher"     ,	action="store_true"     ,	default=a_					)
   parser.add_argument("--length_penalty"     ,	type=a_     ,	default=-1					)
   parser.add_argument("--supervise_forward"     ,	action="store_true"     ,	default=a_					)
   parser.add_argument("--normalize_hidden"     ,	action="store_true"     ,	default=a_					)






class      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Union[str, Any]   			=      'translation'
       _lowerCamelCase				:				Dict   			=      ['bleu']
       _lowerCamelCase				:				Any   			=      'bleu'







       def __init__(       self				,lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          return calculate_bleu(lowerCamelCase_				,lowerCamelCase_		)







       @staticmethod
       def  _snake_case      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          TranslationModule.add_model_specific_args(lowerCamelCase_				,lowerCamelCase_		)
          add_distill_args(lowerCamelCase_		)
          return parser



def       __UpperCAmelCase		(		a_      :		str					)  ->							Any:
   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(a_					)
   return model
def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							str:
   Path(args.output_dir					).mkdir(exist_ok=a_					)
   check_output_dir(a_     ,	expected_items=3					)

   _UpperCAmelCase 						=       create_module(a_					)
   return ft_main(a_     ,	model=a_					)


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

  distill_main(args)

