
"""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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							None:







       """simple docstring"""
       UpperCamelCase					 =		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()				)


SCREAMING_SNAKE_CASE      =     {
    # 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      =     {
    # 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 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       try:
              UpperCamelCase					 =		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 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							List[int]:







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



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_ = "student"		,      UpperCAmelCase_ = None		,      UpperCAmelCase_ = None		,      UpperCAmelCase_=False		,      UpperCAmelCase_=None		,      UpperCAmelCase_=None		,      **UpperCAmelCase_		,      )->							Tuple[PreTrainedModel, List[int], List[int]]:







       """simple docstring"""
       UpperCamelCase					 =		"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
              UpperCamelCase					 =		AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_				).eval()
       else:
              assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), F"teacher must be a model or string got type {type(UpperCAmelCase_				)}"
       UpperCamelCase					 =		teacher.config.to_diff_dict()

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







"""simple docstring"""


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE      =     {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





