





"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A											=						logging.get_logger(__name__)

__A											=						{
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   """switch_transformers"""
       __magic_name__	:Optional[Any]						    =   ["""past_key_values"""]
       __magic_name__	:str						    =   {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






       def __init__(       self       ,					__UpperCAmelCase=3_2_1_2_8       ,					__UpperCAmelCase=7_6_8       ,					__UpperCAmelCase=6_4       ,					__UpperCAmelCase=2_0_4_8       ,					__UpperCAmelCase=6_4       ,					__UpperCAmelCase=1_2       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=1_2       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=1_2       ,					__UpperCAmelCase=8       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=0.01       ,					__UpperCAmelCase="float32"       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=1_2_8       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=1E-6       ,					__UpperCAmelCase=0.0_01       ,					__UpperCAmelCase=0.0_01       ,					__UpperCAmelCase=1.0       ,					__UpperCAmelCase="relu"       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=1       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				vocab_size
         lowerCAmelCase__     :int											=				d_model
         lowerCAmelCase__     :Union[str, Any]											=				d_kv
         lowerCAmelCase__     :Dict											=				d_ff

         lowerCAmelCase__     :List[str]											=				num_sparse_encoder_layers

         lowerCAmelCase__     :Union[str, Any]											=				num_layers
         lowerCAmelCase__     :int											=				(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowerCAmelCase__     :List[Any]											=				num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
           lowerCAmelCase__     :Optional[Any]											=				self.num_layers // self.num_sparse_encoder_layers
         else:
           lowerCAmelCase__     :Optional[int]											=				self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
           lowerCAmelCase__     :List[str]											=				self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
           lowerCAmelCase__     :Optional[int]											=				self.num_decoder_layers  # HACK: this will create 0 sparse layers

         lowerCAmelCase__     :List[str]											=				num_heads
         lowerCAmelCase__     :Dict											=				num_experts
         lowerCAmelCase__     :Optional[Any]											=				expert_capacity
         lowerCAmelCase__     :Union[str, Any]											=				router_bias
         lowerCAmelCase__     :Optional[Any]											=				router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
           raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}" )
         lowerCAmelCase__     :str											=				router_dtype

         lowerCAmelCase__     :int											=				router_ignore_padding_tokens
         lowerCAmelCase__     :Dict											=				relative_attention_num_buckets
         lowerCAmelCase__     :Dict											=				relative_attention_max_distance

         lowerCAmelCase__     :str											=				dropout_rate
         lowerCAmelCase__     :List[Any]											=				layer_norm_epsilon
         lowerCAmelCase__     :List[str]											=				initializer_factor
         lowerCAmelCase__     :List[Any]											=				feed_forward_proj
         lowerCAmelCase__     :str											=				use_cache
         lowerCAmelCase__     :Any											=				add_router_probs

         lowerCAmelCase__     :List[Any]											=				router_z_loss_coef
         lowerCAmelCase__     :List[str]											=				router_aux_loss_coef

         lowerCAmelCase__     :Union[str, Any]											=				self.feed_forward_proj.split('-' )
         lowerCAmelCase__     :Dict											=				act_info[-1]
         lowerCAmelCase__     :str											=				act_info[0] == 'gated'

         if len(__UpperCAmelCase ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase ) > 2:
           raise ValueError(
               F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
               'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
               '\'gated-gelu\' or \'relu\'' )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
           lowerCAmelCase__     :Tuple											=				'gelu_new'

         super().__init__(
             pad_token_id=__UpperCAmelCase       ,					eos_token_id=__UpperCAmelCase       ,					is_encoder_decoder=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)







"""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)





