






"""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 pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image


_UpperCamelCase					      =  ["""text""", """image""", """audio"""]

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]

      for input_type in input_types:
            if input_type == "text":
                  inputs.append("""Text input"""							)
            elif input_type == "image":
                  inputs.append(
                      Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""							)							) / """000000039769.png"""							).resize((512, 512)							)							)
            elif input_type == "audio":
                  inputs.append(torch.ones(3000							)							)
            elif isinstance(_snake_case				,	_snake_case							):
                  inputs.append(create_inputs(_snake_case							)							)
            else:
                  raise ValueError(F'''Invalid type requested: {input_type}'''							)

      return inputs

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]

      for output in outputs:
            if isinstance(_snake_case				,	(str, AgentText)							):
                  output_types.append("""text"""							)
            elif isinstance(_snake_case				,	(Image.Image, AgentImage)							):
                  output_types.append("""image"""							)
            elif isinstance(_snake_case				,	(torch.Tensor, AgentAudio)							):
                  output_types.append("""audio"""							)
            else:
                  raise ValueError(F'''Invalid output: {output}'''							)

      return output_types







@is_tool_test
class  lowerCamelCase__ :






    def     _UpperCamelCase							(			self ):
          self.assertTrue(hasattr(self.tool     ,"""inputs""" ) )
          self.assertTrue(hasattr(self.tool     ,"""outputs""" ) )

          UpperCAmelCase          =	self.tool.inputs
          for _input in inputs:
                if isinstance(_input     ,A ):
                      for __input in _input:
                            self.assertTrue(__input in authorized_types )
                else:
                      self.assertTrue(_input in authorized_types )

          UpperCAmelCase          =	self.tool.outputs
          for _output in outputs:
                self.assertTrue(_output in authorized_types )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )
          UpperCAmelCase          =	self.tool(*A )

          # There is a single output
          if len(self.tool.outputs ) == 1:
                UpperCAmelCase          =	[outputs]

          self.assertListEqual(output_types(A )     ,self.tool.outputs )






    def     _UpperCamelCase							(			self ):
          self.assertTrue(hasattr(self.tool     ,"""description""" ) )
          self.assertTrue(hasattr(self.tool     ,"""default_checkpoint""" ) )
          self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )
          UpperCAmelCase          =	self.tool(*A )

          if not isinstance(A     ,A ):
                UpperCAmelCase          =	[outputs]

          self.assertEqual(len(A )     ,len(self.tool.outputs ) )

          for output, output_type in zip(A     ,self.tool.outputs ):
                UpperCAmelCase          =	AGENT_TYPE_MAPPING[output_type]
                self.assertTrue(isinstance(A     ,A ) )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )

          UpperCAmelCase          =	[]

          for _input, input_type in zip(A     ,self.tool.inputs ):
                if isinstance(A     ,A ):
                      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
                else:
                      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
          UpperCAmelCase          =	self.tool(*A )

          if not isinstance(A     ,A ):
                UpperCAmelCase          =	[outputs]

          self.assertEqual(len(A )     ,len(self.tool.outputs ) )



