"""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       :			Dict   	=logging.get_logger(__name__)


def      UpperCAmelCase__			(							lowerCAmelCase__	:nn.ModuleList   ,						lowerCAmelCase__	:nn.ModuleList   ,						lowerCAmelCase__	:List[int]						)					->       None:
      '''simple docstring'''


      lowercase								=      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       :			Optional[Any]   	={
    # 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      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:Dict						)					->       List[str]:
      '''simple docstring'''


      try:
            lowercase								=      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      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Optional[int]						)					->       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(lowerCAmelCase__						)						)
      elif n_student == 1:
            return [n_teacher - 1]
      else:
            return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, PreTrainedModel]   ,						lowerCAmelCase__	:Union[str, Path] = "student"   ,						lowerCAmelCase__	:Union[int, None] = None   ,						lowerCAmelCase__	:Union[int, None] = None   ,						lowerCAmelCase__	:Any=False   ,						lowerCAmelCase__	:Tuple=None   ,						lowerCAmelCase__	:str=None   ,						**lowerCAmelCase__	:Dict   ,						)					->       Tuple[PreTrainedModel, List[int], List[int]]:
      '''simple docstring'''


      lowercase								=      """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
            lowercase								=      AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__						).eval()
      else:
            assert isinstance(lowerCAmelCase__   ,						lowerCAmelCase__						), f'teacher must be a model or string got type {type(lowerCAmelCase__						)}'
      lowercase								=      teacher.config.to_diff_dict()

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




"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      'philschmid/bart-large-cnn-samsum'
      snake_case__     :  int				    =      (
          'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
          'and returns a summary of the text.'
      )
      snake_case__     :  Tuple				    =      'summarizer'
      snake_case__     :  int				    =      AutoTokenizer
      snake_case__     :  Any				    =      AutoModelForSeqaSeqLM

      snake_case__     :  Optional[int]				    =      ['text']
      snake_case__     :  Optional[int]				    =      ['text']

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor(__lowerCAmelCase						,			return_tensors="""pt"""						,			truncation=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.model.generate(**__lowerCAmelCase		)[0]







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor.decode(__lowerCAmelCase						,			skip_special_tokens=__lowerCAmelCase						,			clean_up_tokenization_spaces=__lowerCAmelCase		)



