


'''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


__lowerCamelCase												=						logging.get_logger(__name__)


def        a__     (					UpperCamelCase_						:      nn.ModuleList,   UpperCamelCase_						:      nn.ModuleList,   UpperCamelCase_						:      List[int]						):
   UpperCAmelCase__	:Optional[int]      =      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()						)


__lowerCamelCase												=						{
    # 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												=						{
    # 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        a__     (					UpperCamelCase_						:      Tuple,   UpperCamelCase_						:      Dict						):
   try:
      UpperCAmelCase__	:List[str]      =      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        a__     (					UpperCamelCase_						:      int,   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        a__     (					UpperCamelCase_						:      Union[str, PreTrainedModel],   UpperCamelCase_						:      Union[str, Path] = "student",   UpperCamelCase_						:      Union[int, None] = None,   UpperCamelCase_						:      Union[int, None] = None,   UpperCamelCase_						:      Optional[int]=False,   UpperCamelCase_						:      Optional[Any]=None,   UpperCamelCase_						:      Tuple=None,   **UpperCamelCase_						:      str,   ):
   UpperCAmelCase__	:Union[str, 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(UpperCamelCase_,   UpperCamelCase_						):
      AutoTokenizer.from_pretrained(UpperCamelCase_						).save_pretrained(UpperCamelCase_						)  # purely for convenience
      UpperCAmelCase__	:List[Any]      =      AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_						).eval()
   else:
      assert isinstance(UpperCamelCase_,   UpperCamelCase_						), F'''teacher must be a model or string got type {type(UpperCamelCase_						)}'''
   UpperCAmelCase__	:List[Any]      =      teacher.config.to_diff_dict()

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

   # Copy weights
   UpperCAmelCase__	:Any      =      teacher.config_class(**UpperCamelCase_						)
   UpperCAmelCase__	:List[str]      =      AutoModelForSeqaSeqLM.from_config(UpperCamelCase_						)
   # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
   UpperCAmelCase__	:str      =      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
      UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      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:
      UpperCAmelCase__	:List[int]      =      pick_layers_to_copy(UpperCamelCase_,   UpperCamelCase_						)
   if d_layers_to_copy is None:
      UpperCAmelCase__	:List[int]      =      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}'''						)
   UpperCAmelCase__	:Dict      =      {
       '''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)







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



