


"""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


lowerCamelCase_   :   Union[str, Any]									=  logging.get_logger(__name__)
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=nn.ModuleList([src_layers[i] for i in layers_to_copy]		)
    assert len(lowercase		) == len(lowercase		), f'''{len(lowercase		)} != {len(lowercase		)}'''
    dest_layers.load_state_dict(layers_to_copy.state_dict()		)


lowerCamelCase_   :   List[str]									=  {
    # 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_   :   Union[str, 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]},
    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						(						lowercase       ,    lowercase		):







    """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(lowercase		)		)
def _A						(						lowercase       ,    lowercase		):







    """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(lowercase		)		)
    elif n_student == 1:
        return [n_teacher - 1]
    else:
        return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _A						(						lowercase       ,    lowercase = "student"       ,    lowercase = None       ,    lowercase = None       ,    lowercase=False       ,    lowercase=None       ,    lowercase=None       ,    **lowercase       ,    ):







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

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

    try:
        if hasattr(
            lowercase       ,    '''prophetnet'''		):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
            copy_layers(teacher.prophetnet.encoder.layers       ,    student.prophetnet.encoder.layers       ,    lowercase		)
            copy_layers(teacher.prophetnet.decoder.layers       ,    student.prophetnet.decoder.layers       ,    lowercase		)
        else:
            copy_layers(teacher.model.encoder.layers       ,    student.model.encoder.layers       ,    lowercase		)
            copy_layers(teacher.model.decoder.layers       ,    student.model.decoder.layers       ,    lowercase		)
    except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
        copy_layers(teacher.encoder.block       ,    student.encoder.block       ,    lowercase		)
        copy_layers(teacher.decoder.block       ,    student.decoder.block       ,    lowercase		)
    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(lowercase		)
    # 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								=	{
    """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								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """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								=	[
         """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								=	[
         """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								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




