
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					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase		:	int							=      nn.ModuleList([src_layers[i] for i in layers_to_copy]							)
 assert len(lowerCamelCase							) == len(lowerCamelCase							), F'''{len(lowerCamelCase							)} != {len(lowerCamelCase							)}'''
 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
    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))},
}
_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]},
    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     _a					(						lowerCamelCase,	lowerCamelCase							):
 try:
  lowerCamelCase		:	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(lowerCamelCase							)							)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 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(lowerCamelCase							)							)
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def     _a					(						lowerCamelCase,	lowerCamelCase = "student",	lowerCamelCase = None,	lowerCamelCase = None,	lowerCamelCase=False,	lowerCamelCase=None,	lowerCamelCase=None,	**lowerCamelCase,	):
 lowerCamelCase		:	Optional[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(lowerCamelCase,	lowerCamelCase							):
  AutoTokenizer.from_pretrained(lowerCamelCase							).save_pretrained(lowerCamelCase							)  # purely for convenience
  lowerCamelCase		:	int							=      AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase							).eval()
 else:
  assert isinstance(lowerCamelCase,	lowerCamelCase							), F'''teacher must be a model or string got type {type(lowerCamelCase							)}'''
 lowerCamelCase		:	Tuple							=      teacher.config.to_diff_dict()

 try:
  lowerCamelCase			,    lowerCamelCase		:	List[str]							=      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			,    lowerCamelCase		:	Dict							=      teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
  else:
   lowerCamelCase			,    lowerCamelCase		:	int							=      teacher.config.num_layers, teacher.config.num_decoder_layers
  if e is None:
   lowerCamelCase		:	Any							=      teacher_e
  if d is None:
   lowerCamelCase		:	List[str]							=      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(lowerCamelCase							)

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

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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			"""camembert"""
      def __init__(      self    ,  __magic_name__=3_0_5_2_2    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=1e-12    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	int							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      intermediate_size
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Optional[int]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[int]							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	int							=      layer_norm_eps
       lowerCamelCase		:	Any							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      use_cache
       lowerCamelCase		:	Union[str, Any]							=      classifier_dropout





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]      )




