


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	:       Optional[int]    	=							logging.get_logger(__name__)







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   None:
  __snake_case:						Optional[int]			  =						nn.ModuleList([src_layers[i] for i in layers_to_copy])
  assert len(SCREAMING_SNAKE_CASE__) == len(SCREAMING_SNAKE_CASE__), F'''{len(SCREAMING_SNAKE_CASE__)} != {len(SCREAMING_SNAKE_CASE__)}'''
  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       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Union[str, Any]:
  try:
    __snake_case:						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(SCREAMING_SNAKE_CASE__))







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   List[int]:
  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(SCREAMING_SNAKE_CASE__))
  elif n_student == 1:
    return [n_teacher - 1]
  else:
    return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "student" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ , )       ->   Tuple[PreTrainedModel, List[int], List[int]]:
  __snake_case:						Optional[int]			  =						"""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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__).save_pretrained(SCREAMING_SNAKE_CASE__)  # purely for convenience
    __snake_case:						str			  =						AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__).eval()
  else:
    assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__), F'''teacher must be a model or string got type {type(SCREAMING_SNAKE_CASE__)}'''
  __snake_case:						Union[str, Any]			  =						teacher.config.to_diff_dict()

  try:
    __snake_case:						Dict			  =						teacher.config.encoder_layers, teacher.config.decoder_layers
    if e is None:
      __snake_case:						Optional[Any]			  =						teacher_e
    if d is None:
      __snake_case:						List[Any]			  =						teacher_d
    init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d})
  except AttributeError:  # T5
    if hasattr(teacher.config , """num_encoder_layers"""):
      __snake_case:						List[Any]			  =						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
    else:
      __snake_case:						Any			  =						teacher.config.num_layers, teacher.config.num_decoder_layers
    if e is None:
      __snake_case:						int			  =						teacher_e
    if d is None:
      __snake_case:						Union[str, 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(SCREAMING_SNAKE_CASE__)

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

  try:
    if hasattr(
        SCREAMING_SNAKE_CASE__ , """prophetnet"""):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
      copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , SCREAMING_SNAKE_CASE__)
      copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , SCREAMING_SNAKE_CASE__)
    else:
      copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , SCREAMING_SNAKE_CASE__)
      copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , SCREAMING_SNAKE_CASE__)
  except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
    copy_layers(teacher.encoder.block , student.encoder.block , SCREAMING_SNAKE_CASE__)
    copy_layers(teacher.decoder.block , student.decoder.block , SCREAMING_SNAKE_CASE__)
  logger.info(
      F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''')
  __snake_case:						List[str]			  =						{
      """teacher_type""": teacher.config.model_type,
      """copied_encoder_layers""": e_layers_to_copy,
      """copied_decoder_layers""": d_layers_to_copy,
  }
  student.save_pretrained(SCREAMING_SNAKE_CASE__)
  # 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






