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


SCREAMING_SNAKE_CASE :	List[str]							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


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


SCREAMING_SNAKE_CASE :	Union[str, 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))},
}
SCREAMING_SNAKE_CASE :	Any							     =		{
    # 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		(			_A							,				_A					):



 """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(_A					)					)



def   __A		(			_A							,				_A					):



 """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(_A					)					)
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]



def   __A		(			_A							,				_A = "student"							,				_A = None							,				_A = None							,				_A=False							,				_A=None							,				_A=None							,				**_A							,				):



 """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(_A							,				_A					):
  AutoTokenizer.from_pretrained(_A					).save_pretrained(_A					)  # purely for convenience
  __a       =  AutoModelForSeqaSeqLM.from_pretrained(_A					).eval()
 else:
  assert isinstance(_A							,				_A					), f"""teacher must be a model or string got type {type(_A					)}"""
 __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(_A					)

 # Copy weights
 __a       =  teacher.config_class(**_A					)
 __a       =  AutoModelForSeqaSeqLM.from_config(_A					)
 # 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=_A					)
 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(_A					)					), list(range(_A					)					)
  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(_A					)
  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(_A							,				_A					)
 if d_layers_to_copy is None:
  __a       =  pick_layers_to_copy(_A							,				_A					)

 try:
  if hasattr(
      _A							,				"prophetnet"					):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
   copy_layers(teacher.prophetnet.encoder.layers							,				student.prophetnet.encoder.layers							,				_A					)
   copy_layers(teacher.prophetnet.decoder.layers							,				student.prophetnet.decoder.layers							,				_A					)
  else:
   copy_layers(teacher.model.encoder.layers							,				student.model.encoder.layers							,				_A					)
   copy_layers(teacher.model.decoder.layers							,				student.model.decoder.layers							,				_A					)
 except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
  copy_layers(teacher.encoder.block							,				student.encoder.block							,				_A					)
  copy_layers(teacher.decoder.block							,				student.decoder.block							,				_A					)
 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(_A					)
 # 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)



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


