




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


_lowerCamelCase     :		Any								=       logging.get_logger(__name__)







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



       """simple docstring"""

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


_lowerCamelCase     :		List[str]								=       {
    # 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))},
}
_lowerCamelCase     :		Tuple								=       {
    # 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    (	lowercase_			,    lowercase_						)       ->	Optional[int]:



       """simple docstring"""

       try:
              A__							=   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(lowercase_						)						)







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







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



       """simple docstring"""

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

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

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

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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase     :		str								=       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Dict								=       [
      """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FocalNetForImageClassification""",
      """FocalNetForMaskedImageModeling""",
      """FocalNetBackbone""",
      """FocalNetModel""",
      """FocalNetPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_focalnet import (
        FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
        FocalNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Tuple								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


