

'''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    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







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


__UpperCAmelCase    				=     {
    # 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))},
}
__UpperCAmelCase    				=     {
    # 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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     try:
          _lowerCAmelCase   :					Union[str, 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(SCREAMING_SNAKE_CASE				)				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      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(SCREAMING_SNAKE_CASE				)				)
     elif n_student == 1:
          return [n_teacher - 1]
     else:
          return LAYERS_TO_SUPERVISE[n_teacher][n_student]







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

     """simple docstring"""







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

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

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

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









from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


__UpperCAmelCase    				=     logging.get_logger(__name__)





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						[
     '''no_inference''',
     '''no_cuda''',
     '''no_tpu''',
     '''no_speed''',
     '''no_memory''',
     '''no_env_print''',
     '''no_multi_process''',
 ]

 def __init__(		self :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                _lowerCAmelCase   :					str        =    deprecated_arg[3:]
                _lowerCAmelCase   :					List[Any]        =    not kwargs.pop(A_       )
                logger.warning(
                    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                    F''' {positive_arg}={kwargs[positive_arg]}'''       )
      _lowerCAmelCase   :					Any        =    kwargs.pop("tpu_name"     ,						self.tpu_name       )
      _lowerCAmelCase   :					str        =    kwargs.pop("device_idx"     ,						self.device_idx       )
      _lowerCAmelCase   :					str        =    kwargs.pop("eager_mode"     ,						self.eager_mode       )
      _lowerCAmelCase   :					Tuple        =    kwargs.pop("use_xla"     ,						self.use_xla       )
      super().__init__(**A_       )

 _lowercase     :					str      =						field(
     default=A  ,		metadata={'''help''': '''Name of TPU'''}  ,		)
 _lowercase     :					int      =						field(
     default=0  ,		metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}  ,		)
 _lowercase     :					bool      =						field(default=A  ,		metadata={'''help''': '''Benchmark models in eager model.'''} )
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={
         '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
     }  ,		)

 @cached_property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      _lowerCAmelCase   :					Any        =    None
      if self.tpu:
           try:
                if self.tpu_name:
                     _lowerCAmelCase   :					Union[str, Any]        =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
                else:
                     _lowerCAmelCase   :					List[str]        =    tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
                _lowerCAmelCase   :					Optional[int]        =    None
      return tpu

 @cached_property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu       )
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

           _lowerCAmelCase   :					List[Any]        =    tf.distribute.TPUStrategy(self._setup_tpu       )
      else:
           # currently no multi gpu is allowed
           if self.is_gpu:
                # TODO: Currently only single GPU is supported
                tf.config.set_visible_devices(self.gpu_list[self.device_idx]     ,						"GPU"       )
                _lowerCAmelCase   :					Dict        =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
           else:
                tf.config.set_visible_devices([]     ,						"GPU"       )  # disable GPU
                _lowerCAmelCase   :					Optional[Any]        =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

      return strategy

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_tpu is not None

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_strategy

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return tf.config.list_physical_devices("GPU"       )

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.cuda:
           return len(self.gpu_list       )
      return 0


 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.n_gpu > 0






