
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}





class     __UpperCamelCase (							a__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''dpt'''



     def __init__(				self							:							Any     ,						_A							:							Dict=768     ,						_A							:							str=12     ,						_A							:							str=12     ,						_A							:							Union[str, Any]=3072     ,						_A							:							List[Any]="gelu"     ,						_A							:							str=0.0     ,						_A							:							Union[str, Any]=0.0     ,						_A							:							List[Any]=0.02     ,						_A							:							str=1e-12     ,						_A							:							Union[str, Any]=384     ,						_A							:							List[str]=16     ,						_A							:							Optional[Any]=3     ,						_A							:							Tuple=False     ,						_A							:							Any=True     ,						_A							:							List[str]=[2, 5, 8, 11]     ,						_A							:							str="project"     ,						_A							:							Union[str, Any]=[4, 2, 1, 0.5]     ,						_A							:							List[Any]=[96, 192, 384, 768]     ,						_A							:							Any=256     ,						_A							:							Any=-1     ,						_A							:							Dict=False     ,						_A							:							List[Any]=True     ,						_A							:							Dict=0.4     ,						_A							:							Optional[int]=255     ,						_A							:							str=0.1     ,						_A							:							Dict=[1, 1024, 24, 24]     ,						_A							:							str=[0, 1]     ,						_A							:							Optional[int]=None     ,						**_A							:							Optional[int]     ,						):







       """simple docstring"""




       super().__init__(**_snake_case )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		hidden_size
       __SCREAMING_SNAKE_CASE      :	Tuple					=		is_hybrid

       if self.is_hybrid:
         if backbone_config is None:
           logger.info('''Initializing the config with a `BiT` backbone.''' )
           __SCREAMING_SNAKE_CASE      :	int					=		{
               '''global_padding''': '''same''',
               '''layer_type''': '''bottleneck''',
               '''depths''': [3, 4, 9],
               '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
               '''embedding_dynamic_padding''': True,
           }
           __SCREAMING_SNAKE_CASE      :	Dict					=		BitConfig(**_snake_case )
         elif isinstance(_snake_case     ,						_snake_case ):
           logger.info('''Initializing the config with a `BiT` backbone.''' )
           __SCREAMING_SNAKE_CASE      :	Tuple					=		BitConfig(**_snake_case )
         elif isinstance(_snake_case     ,						_snake_case ):
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		backbone_config
         else:
           raise ValueError(
               F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )

         __SCREAMING_SNAKE_CASE      :	List[str]					=		backbone_featmap_shape
         __SCREAMING_SNAKE_CASE      :	str					=		neck_ignore_stages

         if readout_type != "project":
           raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.''' )
       else:
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		None
         __SCREAMING_SNAKE_CASE      :	Any					=		None
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		[]

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		num_hidden_layers
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		num_attention_heads
       __SCREAMING_SNAKE_CASE      :	List[str]					=		intermediate_size
       __SCREAMING_SNAKE_CASE      :	List[str]					=		hidden_act
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		hidden_dropout_prob
       __SCREAMING_SNAKE_CASE      :	Tuple					=		attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE      :	Dict					=		initializer_range
       __SCREAMING_SNAKE_CASE      :	List[str]					=		layer_norm_eps
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		image_size
       __SCREAMING_SNAKE_CASE      :	Dict					=		patch_size
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		num_channels
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		qkv_bias
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		backbone_out_indices
       if readout_type not in ["ignore", "add", "project"]:
         raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']''' )
       __SCREAMING_SNAKE_CASE      :	int					=		readout_type
       __SCREAMING_SNAKE_CASE      :	str					=		reassemble_factors
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		neck_hidden_sizes
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		fusion_hidden_size
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		head_in_index
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		use_batch_norm_in_fusion_residual
       # auxiliary head attributes (semantic segmentation)
       __SCREAMING_SNAKE_CASE      :	str					=		use_auxiliary_head
       __SCREAMING_SNAKE_CASE      :	str					=		auxiliary_loss_weight
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		semantic_loss_ignore_index
       __SCREAMING_SNAKE_CASE      :	Any					=		semantic_classifier_dropout




     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		copy.deepcopy(self.__dict__ )

       if output["backbone_config"] is not None:
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.backbone_config.to_dict()

       __SCREAMING_SNAKE_CASE      :	Any					=		self.__class__.model_type
       return output







"""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     :						List[Any]						       =  logging.get_logger(__name__)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    List[int]			):
 lowerCAmelCase    				= 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()			)


__UpperCamelCase     :						Optional[Any]						       =  {
    # 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))},
}
__UpperCamelCase     :						int						       =  {
    # 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			(_UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    List[Any]			):
 try:
  lowerCAmelCase    				= 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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, PreTrainedModel]  ,     _UpperCAmelCase					:    Union[str, Path] = "student"  ,     _UpperCAmelCase					:    Union[int, None] = None  ,     _UpperCAmelCase					:    Union[int, None] = None  ,     _UpperCAmelCase					:    List[str]=False  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     **_UpperCAmelCase					:    str  ,     ):
 lowerCAmelCase    				= '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
  lowerCAmelCase    				= AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase			).eval()
 else:
  assert isinstance(_UpperCAmelCase  ,     _UpperCAmelCase			), F'teacher must be a model or string got type {type(_UpperCAmelCase			)}'
 lowerCAmelCase    				= teacher.config.to_diff_dict()

 try:
  lowerCAmelCase				,lowerCAmelCase    				= teacher.config.encoder_layers, teacher.config.decoder_layers
  if e is None:
   lowerCAmelCase    				= teacher_e
  if d is None:
   lowerCAmelCase    				= teacher_d
  init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}			)
 except AttributeError:  # T5
  if hasattr(teacher.config  ,     'num_encoder_layers'			):
   lowerCAmelCase				,lowerCAmelCase    				= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
  else:
   lowerCAmelCase				,lowerCAmelCase    				= teacher.config.num_layers, teacher.config.num_decoder_layers
  if e is None:
   lowerCAmelCase    				= teacher_e
  if d is None:
   lowerCAmelCase    				= 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
 lowerCAmelCase    				= teacher.config_class(**_UpperCAmelCase			)
 lowerCAmelCase    				= AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase			)
 # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
 lowerCAmelCase    				= 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
  lowerCAmelCase				,lowerCAmelCase    				= 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:
  lowerCAmelCase    				= pick_layers_to_copy(_UpperCAmelCase  ,     _UpperCAmelCase			)
 if d_layers_to_copy is None:
  lowerCAmelCase    				= 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}'			)
 lowerCAmelCase    				= {
     '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)





