





"""simple docstring"""
import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__UpperCamelCase     :						Optional[Any]						       =  '''bert-base-cased'''
__UpperCamelCase     :						str						       =  '''google/pegasus-xsum'''
__UpperCamelCase     :						int						       =  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__UpperCamelCase     :						Dict						       =  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__UpperCamelCase     :						Any						       =  '''patrickvonplaten/t5-tiny-random'''
__UpperCamelCase     :						List[Any]						       =  '''sshleifer/bart-tiny-random'''
__UpperCamelCase     :						Dict						       =  '''sshleifer/tiny-mbart'''
__UpperCamelCase     :						List[Any]						       =  '''sshleifer/tiny-marian-en-de'''

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Path  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= '\n'.join(_UpperCAmelCase			)
 Path(_UpperCAmelCase			).open('w'			).writelines(_UpperCAmelCase			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(_UpperCAmelCase  ,     F'{split}.source'			)  ,     _UpperCAmelCase			)
  _dump_articles(os.path.join(_UpperCAmelCase  ,     F'{split}.target'			)  ,     _UpperCAmelCase			)
 return tmp_dir







class       a							(   a__  ):



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]				,       )
 @slow
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case     )
  lowerCAmelCase    				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in ARTICLES     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in SUMMARIES     )
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 8
  assert max_len_target > max_src_len  # Will be truncated
  assert max_len_source > max_src_len  # Will be truncated
  lowerCAmelCase				,lowerCAmelCase    				= 'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
  lowerCAmelCase    				= SeqaSeqDataset(
      _snake_case				,       data_dir=_snake_case				,       type_path='train'				,       max_source_length=_snake_case				,       max_target_length=_snake_case				,       src_lang=_snake_case				,       tgt_lang=_snake_case				,       )
  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_size=2				,       collate_fn=train_dataset.collate_fn     )
  for batch in dataloader:
   assert isinstance(_snake_case				,       _snake_case     )
   assert batch["attention_mask"].shape == batch["input_ids"].shape
   # show that articles were trimmed.
   assert batch["input_ids"].shape[1] == max_src_len
   # show that targets are the same len
   assert batch["labels"].shape[1] == max_tgt_len
   if tok_name != MBART_TINY:
    continue
   # check language codes in correct place
   lowerCAmelCase    				= shift_tokens_right(batch['labels']				,       tokenizer.pad_token_id     )
   assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
   assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
   assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
   assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

   break  # No need to test every batch



 @parameterized.expand([BART_TINY, BERT_BASE_CASED]     )
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case     )
  lowerCAmelCase    				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in ARTICLES     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in SUMMARIES     )
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= LegacySeqaSeqDataset(
      _snake_case				,       data_dir=_snake_case				,       type_path='train'				,       max_source_length=20				,       max_target_length=_snake_case				,       )
  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_size=2				,       collate_fn=train_dataset.collate_fn     )
  for batch in dataloader:
   assert batch["attention_mask"].shape == batch["input_ids"].shape
   # show that articles were trimmed.
   assert batch["input_ids"].shape[1] == max_len_source
   assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
   # show that targets were truncated
   assert batch["labels"].shape[1] == trunc_target  # Truncated
   assert max_len_target > trunc_target  # Truncated
   break  # No need to test every batch



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'     )

  lowerCAmelCase    				= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
  lowerCAmelCase    				= tmp_dir.joinpath('train.source'     ).open().readlines()
  lowerCAmelCase    				= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
  pack_data_dir(_snake_case				,       _snake_case				,       1_28				,       _snake_case     )
  lowerCAmelCase    				= {x.name for x in tmp_dir.iterdir()}
  lowerCAmelCase    				= {x.name for x in save_dir.iterdir()}
  lowerCAmelCase    				= save_dir.joinpath('train.source'     ).open().readlines()
  # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
  # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
  assert len(_snake_case     ) < len(_snake_case     )
  assert len(_snake_case     ) == 1
  assert len(packed_examples[0]     ) == sum(len(_snake_case     ) for x in orig_examples     )
  assert orig_paths == new_paths



 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,       reason='This test requires fairseq'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if not FAIRSEQ_AVAILABLE:
   return
  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self._get_dataset(max_len=64     )
  lowerCAmelCase    				= 64
  lowerCAmelCase    				= ds.make_dynamic_sampler(_snake_case				,       required_batch_size_multiple=_snake_case     )
  lowerCAmelCase    				= [len(_snake_case     ) for x in batch_sampler]
  assert len(set(_snake_case     )     ) > 1  # it's not dynamic batch size if every batch is the same length
  assert sum(_snake_case     ) == len(_snake_case     )  # no dropped or added examples
  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_sampler=_snake_case				,       collate_fn=ds.collate_fn				,       num_workers=2     )
  lowerCAmelCase    				= []
  lowerCAmelCase    				= []
  for batch in data_loader:
   lowerCAmelCase    				= batch['input_ids'].shape
   lowerCAmelCase    				= src_shape[0]
   assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
   lowerCAmelCase    				= np.product(batch['input_ids'].shape     )
   num_src_per_batch.append(_snake_case     )
   if num_src_tokens > (max_tokens * 1.1):
    failures.append(_snake_case     )
  assert num_src_per_batch[0] == max(_snake_case     )
  if failures:
   raise AssertionError(F'too many tokens in {len(_snake_case     )} batches'     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self._get_dataset(max_len=5_12     )
  lowerCAmelCase    				= 2
  lowerCAmelCase    				= ds.make_sortish_sampler(_snake_case				,       shuffle=_snake_case     )

  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_size=_snake_case				,       collate_fn=ds.collate_fn				,       num_workers=2     )
  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_size=_snake_case				,       collate_fn=ds.collate_fn				,       num_workers=2				,       sampler=_snake_case     )

  lowerCAmelCase    				= tokenizer.pad_token_id

  def count_pad_tokens(_snake_case				,       _snake_case="input_ids"     ):
   return [batch[k].eq(_snake_case     ).sum().item() for batch in data_loader]

  assert sum(count_pad_tokens(_snake_case				,       k='labels'     )     ) < sum(count_pad_tokens(_snake_case				,       k='labels'     )     )
  assert sum(count_pad_tokens(_snake_case     )     ) < sum(count_pad_tokens(_snake_case     )     )
  assert len(_snake_case     ) == len(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case=10_00				,       _snake_case=1_28     ):







  """simple docstring"""






  if os.getenv('USE_REAL_DATA'				,       _snake_case     ):
   lowerCAmelCase    				= 'examples/seq2seq/wmt_en_ro'
   lowerCAmelCase    				= max_len * 2 * 64
   if not Path(_snake_case     ).joinpath('train.len'     ).exists():
    save_len_file(_snake_case				,       _snake_case     )
  else:
   lowerCAmelCase    				= 'examples/seq2seq/test_data/wmt_en_ro'
   lowerCAmelCase    				= max_len * 4
   save_len_file(_snake_case				,       _snake_case     )

  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case     )
  lowerCAmelCase    				= SeqaSeqDataset(
      _snake_case				,       data_dir=_snake_case				,       type_path='train'				,       max_source_length=_snake_case				,       max_target_length=_snake_case				,       n_obs=_snake_case				,       )
  return ds, max_tokens, tokenizer



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self._get_dataset()
  lowerCAmelCase    				= set(DistributedSortishSampler(_snake_case				,       2_56				,       num_replicas=2				,       rank=0				,       add_extra_examples=_snake_case     )     )
  lowerCAmelCase    				= set(DistributedSortishSampler(_snake_case				,       2_56				,       num_replicas=2				,       rank=1				,       add_extra_examples=_snake_case     )     )
  assert idsa.intersection(_snake_case     ) == set()



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]				,       )
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case				,       use_fast=_snake_case     )
  if tok_name == MBART_TINY:
   lowerCAmelCase    				= SeqaSeqDataset(
       _snake_case				,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )				,       type_path='train'				,       max_source_length=4				,       max_target_length=8				,       src_lang='EN'				,       tgt_lang='FR'				,       )
   lowerCAmelCase    				= train_dataset.dataset_kwargs
   assert "src_lang" in kwargs and "tgt_lang" in kwargs
  else:
   lowerCAmelCase    				= SeqaSeqDataset(
       _snake_case				,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )				,       type_path='train'				,       max_source_length=4				,       max_target_length=8				,       )
   lowerCAmelCase    				= train_dataset.dataset_kwargs
   assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
   assert len(_snake_case     ) == 1 if tok_name == BART_TINY else len(_snake_case     ) == 0












"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


__UpperCamelCase     :						List[Any]						       =  logging.get_logger(__name__)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    List[int]			):
 lowerCAmelCase    				= nn.ModuleList([src_layers[i] for i in layers_to_copy]			)
 assert len(_UpperCAmelCase			) == len(_UpperCAmelCase			), F'{len(_UpperCAmelCase			)} != {len(_UpperCAmelCase			)}'
 dest_layers.load_state_dict(layers_to_copy.state_dict()			)


__UpperCamelCase     :						Optional[Any]						       =  {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCamelCase     :						int						       =  {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    List[Any]			):
 try:
  lowerCAmelCase    				= LAYERS_TO_COPY[n_teacher][n_student]
  return val
 except KeyError:
  if n_student != n_teacher:
   warnings.warn(
       F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
       F' {n_student}'			)
  return list(range(_UpperCAmelCase			)			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Dict			):
 if n_student > n_teacher:
  raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'			)
 elif n_teacher == n_student:
  return list(range(_UpperCAmelCase			)			)
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, PreTrainedModel]  ,     _UpperCAmelCase					:    Union[str, Path] = "student"  ,     _UpperCAmelCase					:    Union[int, None] = None  ,     _UpperCAmelCase					:    Union[int, None] = None  ,     _UpperCAmelCase					:    List[str]=False  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     **_UpperCAmelCase					:    str  ,     ):
 lowerCAmelCase    				= 'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
 assert (e is not None) or (d is not None), _msg
 if isinstance(_UpperCAmelCase  ,     _UpperCAmelCase			):
  AutoTokenizer.from_pretrained(_UpperCAmelCase			).save_pretrained(_UpperCAmelCase			)  # purely for convenience
  lowerCAmelCase    				= AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase			).eval()
 else:
  assert isinstance(_UpperCAmelCase  ,     _UpperCAmelCase			), F'teacher must be a model or string got type {type(_UpperCAmelCase			)}'
 lowerCAmelCase    				= teacher.config.to_diff_dict()

 try:
  lowerCAmelCase				,lowerCAmelCase    				= teacher.config.encoder_layers, teacher.config.decoder_layers
  if e is None:
   lowerCAmelCase    				= teacher_e
  if d is None:
   lowerCAmelCase    				= teacher_d
  init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}			)
 except AttributeError:  # T5
  if hasattr(teacher.config  ,     'num_encoder_layers'			):
   lowerCAmelCase				,lowerCAmelCase    				= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
  else:
   lowerCAmelCase				,lowerCAmelCase    				= teacher.config.num_layers, teacher.config.num_decoder_layers
  if e is None:
   lowerCAmelCase    				= teacher_e
  if d is None:
   lowerCAmelCase    				= teacher_d
  if hasattr(teacher.config  ,     'num_encoder_layers'			):
   init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d}			)
  else:
   init_kwargs.update({'num_layers': e, 'num_decoder_layers': d}			)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
 init_kwargs.update(_UpperCAmelCase			)

 # Copy weights
 lowerCAmelCase    				= teacher.config_class(**_UpperCAmelCase			)
 lowerCAmelCase    				= AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase			)
 # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
 lowerCAmelCase    				= student.load_state_dict(teacher.state_dict()  ,     strict=_UpperCAmelCase			)
 assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

 if copy_first_teacher_layers:  # Our copying is done. We just log and save
  lowerCAmelCase				,lowerCAmelCase    				= list(range(_UpperCAmelCase			)			), list(range(_UpperCAmelCase			)			)
  logger.info(
      F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
      F' {save_path}'			)
  student.save_pretrained(_UpperCAmelCase			)
  return student, e_layers_to_copy, d_layers_to_copy

 # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
 if e_layers_to_copy is None:
  lowerCAmelCase    				= pick_layers_to_copy(_UpperCAmelCase  ,     _UpperCAmelCase			)
 if d_layers_to_copy is None:
  lowerCAmelCase    				= pick_layers_to_copy(_UpperCAmelCase  ,     _UpperCAmelCase			)

 try:
  if hasattr(
      _UpperCAmelCase  ,     'prophetnet'			):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
   copy_layers(teacher.prophetnet.encoder.layers  ,     student.prophetnet.encoder.layers  ,     _UpperCAmelCase			)
   copy_layers(teacher.prophetnet.decoder.layers  ,     student.prophetnet.decoder.layers  ,     _UpperCAmelCase			)
  else:
   copy_layers(teacher.model.encoder.layers  ,     student.model.encoder.layers  ,     _UpperCAmelCase			)
   copy_layers(teacher.model.decoder.layers  ,     student.model.decoder.layers  ,     _UpperCAmelCase			)
 except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
  copy_layers(teacher.encoder.block  ,     student.encoder.block  ,     _UpperCAmelCase			)
  copy_layers(teacher.decoder.block  ,     student.decoder.block  ,     _UpperCAmelCase			)
 logger.info(
     F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'			)
 lowerCAmelCase    				= {
     'teacher_type': teacher.config.model_type,
     'copied_encoder_layers': e_layers_to_copy,
     'copied_decoder_layers': d_layers_to_copy,
 }
 student.save_pretrained(_UpperCAmelCase			)
 # Save information about copying for easier reproducibility

 return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
       fire.Fire(create_student_by_copying_alternating_layers)





