


'''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


__snake_case											=					logging.get_logger(__name__)


def       a				(      __a			, __a			, __a       )  ->     None:



						'''simple docstring'''






						UpperCamelCase__       :Optional[Any]							 =			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()       )


__snake_case											=					{
    # 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))},
}
__snake_case											=					{
    # 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				(      __a			, __a       )  ->     Tuple:



						'''simple docstring'''






						try:
												UpperCamelCase__       :List[Any]							 =			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       a				(      __a			, __a       )  ->     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(UpperCamelCase__       )       )
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def       a				(      __a			, __a = "student"			, __a = None			, __a = None			, __a=False			, __a=None			, __a=None			, **__a			, )  ->     Tuple[PreTrainedModel, List[int], List[int]]:



						'''simple docstring'''






						UpperCamelCase__       :Optional[Any]							 =			'''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
												UpperCamelCase__       :List[Any]							 =			AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__       ).eval()
						else:
												assert isinstance(UpperCamelCase__			, UpperCamelCase__       ), f'''teacher must be a model or string got type {type(UpperCamelCase__       )}'''
						UpperCamelCase__       :Any							 =			teacher.config.to_diff_dict()

						try:
												UpperCamelCase__ ,    UpperCamelCase__       :int							 =			teacher.config.encoder_layers, teacher.config.decoder_layers
												if e is None:
																		UpperCamelCase__       :str							 =			teacher_e
												if d is None:
																		UpperCamelCase__       :List[str]							 =			teacher_d
												init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}       )
						except AttributeError:  # T5
												if hasattr(teacher.config			, '''num_encoder_layers'''       ):
																		UpperCamelCase__ ,    UpperCamelCase__       :Optional[Any]							 =			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
												else:
																		UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			teacher.config.num_layers, teacher.config.num_decoder_layers
												if e is None:
																		UpperCamelCase__       :Dict							 =			teacher_e
												if d is None:
																		UpperCamelCase__       :List[Any]							 =			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
						UpperCamelCase__       :Tuple							 =			teacher.config_class(**UpperCamelCase__       )
						UpperCamelCase__       :List[Any]							 =			AutoModelForSeqaSeqLM.from_config(UpperCamelCase__       )
						# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
						UpperCamelCase__       :Optional[int]							 =			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
												UpperCamelCase__ ,    UpperCamelCase__       :Optional[Any]							 =			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:
												UpperCamelCase__       :Union[str, Any]							 =			pick_layers_to_copy(UpperCamelCase__			, UpperCamelCase__       )
						if d_layers_to_copy is None:
												UpperCamelCase__       :Tuple							 =			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}'''       )
						UpperCamelCase__       :Tuple							 =			{
						    '''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)



'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

__snake_case											=					get_logger()

__snake_case											=					None
class        lowercase     (							TensorFormatter[Mapping, 'jax.Array', Mapping] ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(features=UpperCamelCase_							)
							import jax
							from jaxlib.xla_client import Device

							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
												raise ValueError(
												    F'''Expected {device} to be a `str` not {type(UpperCamelCase_							)}, as `jaxlib.xla_extension.Device` '''
												    '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
												    '''the device with `str()` to get its string identifier that will be internally mapped '''
												    '''to the actual `jaxlib.xla_extension.Device`.'''							)
							UpperCamelCase__       :Tuple							 =			device if isinstance(UpperCamelCase_   ,       UpperCamelCase_							) else str(jax.devices()[0]							)
							# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
							# with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
												UpperCamelCase__       :Optional[Any]							 =			self._map_devices_to_str()
							if self.device not in list(DEVICE_MAPPING.keys()							):
												logger.warning(
												    F'''Device with string identifier {self.device} not listed among the available '''
												    F'''devices: {list(DEVICE_MAPPING.keys()							)}, so falling back to the default '''
												    F'''device: {str(jax.devices()[0]							)}.'''							)
												UpperCamelCase__       :Optional[int]							 =			str(jax.devices()[0]							)
							UpperCamelCase__       :Tuple							 =			jnp_array_kwargs




		@staticmethod
		def    lowerCAmelCase__    (		):

							'''simple docstring'''


							import jax

							return {str(UpperCamelCase_							): device for device in jax.devices()}




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax
							import jax.numpy as jnp

							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							) and column:
												if all(
												    isinstance(UpperCamelCase_   ,       jax.Array							) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column							):
																	return jnp.stack(UpperCamelCase_   ,       axis=0							)
							return column




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax
							import jax.numpy as jnp

							if isinstance(UpperCamelCase_   ,       (str, bytes, type(UpperCamelCase_							))							):
												return value
							elif isinstance(UpperCamelCase_   ,       (np.character, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.character							):
												return value.tolist()

							UpperCamelCase__       :Optional[int]							 =			{}

							if isinstance(UpperCamelCase_   ,       (np.number, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.integer							):
												# the default int precision depends on the jax config
												# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
												if jax.config.jax_enable_xaa:
																	UpperCamelCase__       :List[str]							 =			{'''dtype''': jnp.intaa}
												else:
																	UpperCamelCase__       :Union[str, Any]							 =			{'''dtype''': jnp.intaa}
							elif isinstance(UpperCamelCase_   ,       (np.number, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.floating							):
												UpperCamelCase__       :Optional[Any]							 =			{'''dtype''': jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(UpperCamelCase_   ,       PIL.Image.Image							):
																	UpperCamelCase__       :str							 =			np.asarray(UpperCamelCase_							)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
												UpperCamelCase__       :Dict							 =			self._map_devices_to_str()

							with jax.default_device(DEVICE_MAPPING[self.device]							):
												# calling jnp.array on a np.ndarray does copy the data
												# see https://github.com/google/jax/issues/4486
												return jnp.array(UpperCamelCase_   ,       **{**default_dtype, **self.jnp_array_kwargs}							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax

							# support for torch, tf, jax etc.
							if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(UpperCamelCase_   ,       torch.Tensor							):
																	return self._tensorize(data_struct.detach().cpu().numpy()[()]							)
							if hasattr(UpperCamelCase_   ,       '''__array__'''							) and not isinstance(UpperCamelCase_   ,       jax.Array							):
												UpperCamelCase__       :int							 =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(UpperCamelCase_   ,       np.ndarray							):
												if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																	return self._consolidate([self.recursive_tensorize(UpperCamelCase_							) for substruct in data_struct]							)
							elif isinstance(UpperCamelCase_   ,       (list, tuple)							):
												return self._consolidate([self.recursive_tensorize(UpperCamelCase_							) for substruct in data_struct]							)
							return self._tensorize(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return map_nested(self._recursive_tensorize   ,       UpperCamelCase_   ,       map_list=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_row(UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			self.python_features_decoder.decode_row(UpperCamelCase_							)
							return self.recursive_tensorize(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_column(UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			self.python_features_decoder.decode_column(UpperCamelCase_   ,       pa_table.column_names[0]							)
							UpperCamelCase__       :Dict							 =			self.recursive_tensorize(UpperCamelCase_							)
							UpperCamelCase__       :str							 =			self._consolidate(UpperCamelCase_							)
							return column




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.numpy_arrow_extractor().extract_batch(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			self.python_features_decoder.decode_batch(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			self.recursive_tensorize(UpperCamelCase_							)
							for column_name in batch:
												UpperCamelCase__       :Optional[int]							 =			self._consolidate(batch[column_name]							)
							return batch