



"""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


lowercase__		:				List[str]          =	logging.get_logger(__name__)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[Any]						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				str								= nn.ModuleList([src_layers[i] for i in layers_to_copy]						)
  assert len(__lowerCAmelCase						) == len(__lowerCAmelCase						), f"{len(__lowerCAmelCase						)} != {len(__lowerCAmelCase						)}"
  dest_layers.load_state_dict(layers_to_copy.state_dict()						)


lowercase__		:				str          =	{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowercase__		:				Dict          =	{
    # 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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Any						)						->   int:

  """simple docstring"""




  try:
    lowerCAmelCase_   :				Any								= 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(__lowerCAmelCase						)						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[str]						)						->   List[str]:

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				str = "student"    ,   lowerCAmelCase__      :				Dict = None    ,   lowerCAmelCase__      :				Any = None    ,   lowerCAmelCase__      :				Optional[Any]=False    ,   lowerCAmelCase__      :				str=None    ,   lowerCAmelCase__      :				int=None    ,   **lowerCAmelCase__      :				List[str]    ,   )						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= "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(__lowerCAmelCase    ,   __lowerCAmelCase						):
    AutoTokenizer.from_pretrained(__lowerCAmelCase						).save_pretrained(__lowerCAmelCase						)  # purely for convenience
    lowerCAmelCase_   :				Tuple								= AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase						).eval()
  else:
    assert isinstance(__lowerCAmelCase    ,   __lowerCAmelCase						), f"teacher must be a model or string got type {type(__lowerCAmelCase						)}"
  lowerCAmelCase_   :				Optional[int]								= teacher.config.to_diff_dict()

  try:
    lowerCAmelCase_   :				Any								= teacher.config.encoder_layers, teacher.config.decoder_layers
    if e is None:
      lowerCAmelCase_   :				Union[str, Any]								= teacher_e
    if d is None:
      lowerCAmelCase_   :				List[str]								= teacher_d
    init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}						)
  except AttributeError:  # T5
    if hasattr(teacher.config    ,   'num_encoder_layers'						):
      lowerCAmelCase_   :				Dict								= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
    else:
      lowerCAmelCase_   :				List[str]								= teacher.config.num_layers, teacher.config.num_decoder_layers
    if e is None:
      lowerCAmelCase_   :				Optional[int]								= teacher_e
    if d is None:
      lowerCAmelCase_   :				Optional[Any]								= 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(__lowerCAmelCase						)

  # Copy weights
  lowerCAmelCase_   :				List[str]								= teacher.config_class(**__lowerCAmelCase						)
  lowerCAmelCase_   :				Dict								= AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase						)
  # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
  lowerCAmelCase_   :				Dict								= student.load_state_dict(teacher.state_dict()    ,   strict=__lowerCAmelCase						)
  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_   :				Dict								= list(range(__lowerCAmelCase						)						), list(range(__lowerCAmelCase						)						)
    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(__lowerCAmelCase						)
    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_   :				List[int]								= pick_layers_to_copy(__lowerCAmelCase    ,   __lowerCAmelCase						)
  if d_layers_to_copy is None:
    lowerCAmelCase_   :				List[int]								= pick_layers_to_copy(__lowerCAmelCase    ,   __lowerCAmelCase						)

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









'''simple docstring'''


import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase__					=   logging.getLogger()

lowerCamelCase__					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Optional[int]       )      ->Tuple:



					'''simple docstring'''







					os.makedirs(lowerCamelCase__		,     exist_ok=lowerCamelCase__       )
					_UpperCAmelCase						:				List[Any]	    =					{"source": "What is love ?", "target": "life"}
					_UpperCAmelCase						:				Any	    =					{"train": 12, "val": 2, "test": 2}
					for split in ["train", "test", "val"]:
							for field in ["source", "target"]:
									_UpperCAmelCase						:				Dict	    =					"\n".join([contents[field]] * n_lines[split]       )
									with open(os.path.join(lowerCamelCase__		,     F"""{split}.{field}"""       )		,     "w"       ) as f:
											f.write(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   str = "pytorch"       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.get_auto_remove_tmp_dir()
					_UpperCAmelCase						:				int	    =					os.path.join(lowerCamelCase__		,     "output"       )
					_UpperCAmelCase						:				Tuple	    =					os.path.join(lowerCamelCase__		,     "data"       )
					self._create_dummy_data(data_dir=lowerCamelCase__       )

					_UpperCAmelCase						:				str	    =					F"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

					if gpus > 0:
							testargs.append(F"""--gpus={gpus}"""       )
							if is_apex_available():
									testargs.append("--fp16"       )
					else:
							testargs.append("--gpus=0"       )
							testargs.append("--distributed_backend=ddp_cpu"       )
							testargs.append("--num_processes=2"       )

					_UpperCAmelCase						:				str	    =					[sys.executable, str(Path(finetune_rag.__file__       ).resolve()       )] + testargs
					execute_subprocess_async(lowerCamelCase__		,     env=self.get_env()       )

					_UpperCAmelCase						:				Optional[int]	    =					os.path.join(lowerCamelCase__		,     "metrics.json"       )
					with open(lowerCamelCase__       ) as f:
							_UpperCAmelCase						:				Dict	    =					json.load(lowerCamelCase__       )
					return result




			@require_torch_gpu
			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self._run_finetune(gpus=1       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )




			@require_torch_multi_gpu
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self._run_finetune(gpus=2       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )




			@require_torch_gpu
			@require_ray
			def 							lowerCAmelCase__			(						self     :   int       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self._run_finetune(gpus=1		,     distributed_retriever="ray"       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )






			@require_torch_multi_gpu
			@require_ray
			def 							lowerCAmelCase__			(						self     :   int       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					self._run_finetune(gpus=1		,     distributed_retriever="ray"       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )



