


import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A      = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
    A      = json.load(f)


@require_torch
class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					List[str] ):




         """simple docstring"""







         return FSMTTokenizer.from_pretrained(_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     FSMTForConditionalGeneration.from_pretrained(_lowercase ).to(_lowercase )
         if torch_device == "cuda":
                model.half()
         return model






  @parameterized.expand(
      [
          ["en-ru", 2_6.0],
          ["ru-en", 2_2.0],
          ["en-de", 2_2.0],
          ["de-en", 2_9.0],
      ] )
  @slow
  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     F"""facebook/wmt19-{pair}"""
         UpperCAmelCase__										=     self.get_tokenizer(_lowercase )
         UpperCAmelCase__										=     self.get_model(_lowercase )

         UpperCAmelCase__										=     bleu_data[pair]["src"]
         UpperCAmelCase__										=     bleu_data[pair]["tgt"]

         UpperCAmelCase__										=     tokenizer(_lowercase		,						return_tensors="pt"		,						truncation=_lowercase		,						padding="longest" ).to(_lowercase )
         UpperCAmelCase__										=     model.generate(
             input_ids=batch.input_ids		,						num_beams=8		,						)
         UpperCAmelCase__										=     tokenizer.batch_decode(
             _lowercase		,						skip_special_tokens=_lowercase		,						clean_up_tokenization_spaces=_lowercase )
         UpperCAmelCase__										=     calculate_bleu(_lowercase		,						_lowercase )
         print(_lowercase )
         self.assertGreaterEqual(scores["bleu"]		,						_lowercase )









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


A      = logging.get_logger(__name__)





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     nn.ModuleList([src_layers[i] for i in layers_to_copy]			)
       assert len(__A			) == len(__A			), F"""{len(__A			)} != {len(__A			)}"""
       dest_layers.load_state_dict(layers_to_copy.state_dict()			)


A      = {
    # 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))},
}
A      = {
    # 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 __UpperCAmelCase       (		__A ,						__A			)						->					int:



       '''simple docstring'''





       try:
              UpperCAmelCase__										=     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(__A			)			)





def __UpperCAmelCase       (		__A ,						__A			)						->					List[int]:



       '''simple docstring'''





       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(__A			)			)
       elif n_student == 1:
              return [n_teacher - 1]
       else:
              return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def __UpperCAmelCase       (		__A ,						__A = "student" ,						__A = None ,						__A = None ,						__A=False ,						__A=None ,						__A=None ,						**__A ,						)						->					Tuple[PreTrainedModel, List[int], List[int]]:



       '''simple docstring'''





       UpperCAmelCase__										=     "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(__A ,						__A			):
              AutoTokenizer.from_pretrained(__A			).save_pretrained(__A			)  # purely for convenience
              UpperCAmelCase__										=     AutoModelForSeqaSeqLM.from_pretrained(__A			).eval()
       else:
              assert isinstance(__A ,						__A			), F"""teacher must be a model or string got type {type(__A			)}"""
       UpperCAmelCase__										=     teacher.config.to_diff_dict()

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

       # Copy weights
       UpperCAmelCase__										=     teacher.config_class(**__A			)
       UpperCAmelCase__										=     AutoModelForSeqaSeqLM.from_config(__A			)
       # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
       UpperCAmelCase__										=     student.load_state_dict(teacher.state_dict() ,						strict=__A			)
       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
              UpperCAmelCase__						,			UpperCAmelCase__										=     list(range(__A			)			), list(range(__A			)			)
              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(__A			)
              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:
              UpperCAmelCase__										=     pick_layers_to_copy(__A ,						__A			)
       if d_layers_to_copy is None:
              UpperCAmelCase__										=     pick_layers_to_copy(__A ,						__A			)

       try:
              if hasattr(
                  __A ,						"prophetnet"			):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
                     copy_layers(teacher.prophetnet.encoder.layers ,						student.prophetnet.encoder.layers ,						__A			)
                     copy_layers(teacher.prophetnet.decoder.layers ,						student.prophetnet.decoder.layers ,						__A			)
              else:
                     copy_layers(teacher.model.encoder.layers ,						student.model.encoder.layers ,						__A			)
                     copy_layers(teacher.model.decoder.layers ,						student.model.decoder.layers ,						__A			)
       except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
              copy_layers(teacher.encoder.block ,						student.encoder.block ,						__A			)
              copy_layers(teacher.decoder.block ,						student.decoder.block ,						__A			)
       logger.info(
           F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""			)
       UpperCAmelCase__										=     {
           "teacher_type": teacher.config.model_type,
           "copied_encoder_layers": e_layers_to_copy,
           "copied_decoder_layers": d_layers_to_copy,
       }
       student.save_pretrained(__A			)
       # 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)





