





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__						:			Tuple									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  None:
				lowerCamelCase	 =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()				)


UpperCAmelCase__						:			List[Any]									={
    # 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__						:			Optional[int]									={
    # 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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[Any]:
				try:
								lowerCamelCase	 =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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[int]:
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase = "student"			,      _UpperCAmelCase = None			,      _UpperCAmelCase = None			,      _UpperCAmelCase=False			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      **_UpperCAmelCase			,      )						->  Tuple[PreTrainedModel, List[int], List[int]]:
				lowerCamelCase	 ="""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
								lowerCamelCase	 =AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase				).eval()
				else:
								assert isinstance(_UpperCAmelCase			,      _UpperCAmelCase				), F"""teacher must be a model or string got type {type(_UpperCAmelCase				)}"""
				lowerCamelCase	 =teacher.config.to_diff_dict()

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












from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class       __A     (						a    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =SMALL_MODEL_IDENTIFIER
								lowerCamelCase	 ="""pt"""
								lowerCamelCase	 ="""tf"""


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =AutoModel.from_pretrained(self.test_model							)
								model_pt.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =TFAutoModel.from_pretrained(self.test_model   , from_pt=UpperCAmelCase_							)
								model_tf.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""mock_framework"""

								# Framework provided - return whatever the user provides
								lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model   , UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# PyTorch not in environment -> use TensorFlow
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Both in environment -> use PyTorch
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# Both not in environment -> raise error
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)





