






"""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


_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	nn.ModuleList([src_layers[i] for i in layers_to_copy]							)
      assert len(_snake_case							) == len(_snake_case							), F'''{len(_snake_case							)} != {len(_snake_case							)}'''
      dest_layers.load_state_dict(layers_to_copy.state_dict()							)


_UpperCamelCase					      =  {
    # 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					      =  {
    # 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			(		_snake_case				,	_snake_case							):







      """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(_snake_case							)							)

def _a			(		_snake_case				,	_snake_case							):







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

def _a			(		_snake_case				,	_snake_case = "student"				,	_snake_case = None				,	_snake_case = None				,	_snake_case=False				,	_snake_case=None				,	_snake_case=None				,	**_snake_case				,	):







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

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

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











"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case							):







      """simple docstring"""






      if isinstance(_snake_case				,	(list, tuple)							) and isinstance(videos[0]				,	(list, tuple)							) and is_valid_image(videos[0][0]							):
            return videos

      elif isinstance(_snake_case				,	(list, tuple)							) and is_valid_image(videos[0]							):
            return [videos]

      elif is_valid_image(_snake_case							):
            return [[videos]]

      raise ValueError(F'''Could not make batched video from {videos}'''							)







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      ['''pixel_values''']






    def __init__(			self     ,A = True     ,A = None     ,A = PILImageResampling.BILINEAR     ,A = True     ,A = None     ,A = True     ,A = 1 / 255     ,A = True     ,A = True     ,A = None     ,A = None     ,**A     ,):
          super().__init__(**A )
          UpperCAmelCase          =	size if size is not None else {"""shortest_edge""": 256}
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size""" )

          UpperCAmelCase          =	do_resize
          UpperCAmelCase          =	size
          UpperCAmelCase          =	do_center_crop
          UpperCAmelCase          =	crop_size
          UpperCAmelCase          =	resample
          UpperCAmelCase          =	do_rescale
          UpperCAmelCase          =	rescale_factor
          UpperCAmelCase          =	offset
          UpperCAmelCase          =	do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase          =	image_std if image_std is not None else IMAGENET_STANDARD_STD






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = PILImageResampling.BILINEAR     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          if "shortest_edge" in size:
                UpperCAmelCase          =	get_resize_output_image_size(A     ,size["""shortest_edge"""]     ,default_to_square=A )
          elif "height" in size and "width" in size:
                UpperCAmelCase          =	(size["""height"""], size["""width"""])
          else:
                raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
          return resize(A     ,size=A     ,resample=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A )
          if "height" not in size or "width" not in size:
                raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
          return center_crop(A     ,size=(size["""height"""], size["""width"""])     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = True     ,A = None     ,**A     ,):
          UpperCAmelCase          =	image.astype(np.floataa )
          if offset:
                UpperCAmelCase          =	image - (scale / 2)
          return rescale(A     ,scale=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A = None     ,**A     ,):
          return normalize(A     ,mean=A     ,std=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,):
          if do_resize and size is None or resample is None:
                raise ValueError("""Size and resample must be specified if do_resize is True.""" )

          if do_center_crop and crop_size is None:
                raise ValueError("""Crop size must be specified if do_center_crop is True.""" )

          if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )

          if do_normalize and (image_mean is None or image_std is None):
                raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )

          if offset and not do_rescale:
                raise ValueError("""For offset, do_rescale must also be set to True.""" )

          # All transformations expect numpy arrays.
          UpperCAmelCase          =	to_numpy_array(A )

          if do_resize:
                UpperCAmelCase          =	self.resize(image=A     ,size=A     ,resample=A )

          if do_center_crop:
                UpperCAmelCase          =	self.center_crop(A     ,size=A )

          if do_rescale:
                UpperCAmelCase          =	self.rescale(image=A     ,scale=A     ,offset=A )

          if do_normalize:
                UpperCAmelCase          =	self.normalize(image=A     ,mean=A     ,std=A )

          UpperCAmelCase          =	to_channel_dimension_format(A     ,A )
          return image






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,**A     ,):
          UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase          =	resample if resample is not None else self.resample
          UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase          =	offset if offset is not None else self.offset
          UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase          =	image_std if image_std is not None else self.image_std

          UpperCAmelCase          =	size if size is not None else self.size
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size""" )

          if not valid_images(A ):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray.""" )

          UpperCAmelCase          =	make_batched(A )

          UpperCAmelCase          =	[
              [
                  self._preprocess_image(
                      image=A     ,do_resize=A     ,size=A     ,resample=A     ,do_center_crop=A     ,crop_size=A     ,do_rescale=A     ,rescale_factor=A     ,offset=A     ,do_normalize=A     ,image_mean=A     ,image_std=A     ,data_format=A     ,)
                  for img in video
              ]
              for video in videos
          ]

          UpperCAmelCase          =	{"""pixel_values""": videos}
          return BatchFeature(data=A     ,tensor_type=A )



