



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						_snake_case		=  "platform"
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		PegasusConfig
							lowerCamelCase__   			=		{}
							lowerCamelCase__   			=		'gelu'







							def __init__(   self,			__a,			__a=13,			__a=7,			__a=True,			__a=False,			__a=99,			__a=32,			__a=5,			__a=4,			__a=37,			__a=0.1,			__a=0.1,			__a=20,			__a=2,			__a=1,			__a=0,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    parent
														_lowerCAmelCase     :							int    =    batch_size
														_lowerCAmelCase     :							int    =    seq_length
														_lowerCAmelCase     :							Dict    =    is_training
														_lowerCAmelCase     :							List[str]    =    use_labels
														_lowerCAmelCase     :							int    =    vocab_size
														_lowerCAmelCase     :							Tuple    =    hidden_size
														_lowerCAmelCase     :							Dict    =    num_hidden_layers
														_lowerCAmelCase     :							Dict    =    num_attention_heads
														_lowerCAmelCase     :							int    =    intermediate_size

														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							List[Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    max_position_embeddings
														_lowerCAmelCase     :							List[str]    =    eos_token_id
														_lowerCAmelCase     :							Any    =    pad_token_id
														_lowerCAmelCase     :							Optional[Any]    =    bos_token_id







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ids_tensor([self.batch_size, self.seq_length - 1],			self.vocab_size).clip(3,			self.vocab_size)
														_lowerCAmelCase     :							str    =    np.expand_dims(np.array([self.eos_token_id] * self.batch_size),			1)
														_lowerCAmelCase     :							Optional[Any]    =    np.concatenate([input_ids, eos_tensor],			axis=1)

														_lowerCAmelCase     :							Dict    =    ids_tensor([self.batch_size, self.seq_length],			self.vocab_size)

														_lowerCAmelCase     :							str    =    self.config_cls(
														    vocab_size=self.vocab_size,			d_model=self.hidden_size,			encoder_layers=self.num_hidden_layers,			decoder_layers=self.num_hidden_layers,			encoder_attention_heads=self.num_attention_heads,			decoder_attention_heads=self.num_attention_heads,			encoder_ffn_dim=self.intermediate_size,			decoder_ffn_dim=self.intermediate_size,			dropout=self.hidden_dropout_prob,			attention_dropout=self.attention_probs_dropout_prob,			max_position_embeddings=self.max_position_embeddings,			eos_token_ids=[2],			bos_token_id=self.bos_token_id,			pad_token_id=self.pad_token_id,			decoder_start_token_id=self.pad_token_id,			**self.config_updates,			)
														_lowerCAmelCase     :							int    =    prepare_pegasus_inputs_dict(__a,			__a,			__a)
														return config, inputs_dict







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    20
														_lowerCAmelCase     :							str    =    model_class_name(__a)

														_lowerCAmelCase     :							int    =    model.encode(inputs_dict["input_ids"])

														_lowerCAmelCase   ,    _lowerCAmelCase     :							List[Any]    =    (
														    inputs_dict["decoder_input_ids"],
														    inputs_dict["decoder_attention_mask"],
														)

														_lowerCAmelCase     :							Union[str, Any]    =    model.init_cache(decoder_input_ids.shape[0],			__a,			__a)
														_lowerCAmelCase     :							List[Any]    =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length),			dtype="i4")

														_lowerCAmelCase     :							Dict    =    jnp.broadcast_to(
														    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),			)
														_lowerCAmelCase     :							Dict    =    model.decode(
														    decoder_input_ids[:, :-1],			__a,			decoder_attention_mask=__a,			past_key_values=__a,			decoder_position_ids=__a,			)

														_lowerCAmelCase     :							str    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],			dtype="i4")
														_lowerCAmelCase     :							Any    =    model.decode(
														    decoder_input_ids[:, -1:],			__a,			decoder_attention_mask=__a,			past_key_values=outputs_cache.past_key_values,			decoder_position_ids=__a,			)

														_lowerCAmelCase     :							List[str]    =    model.decode(__a,			__a)

														_lowerCAmelCase     :							str    =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
														self.parent.assertTrue(diff < 1E-3,			msg=f"Max diff is {diff}")







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    20
														_lowerCAmelCase     :							Any    =    model_class_name(__a)

														_lowerCAmelCase     :							List[str]    =    model.encode(inputs_dict["input_ids"])

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    (
														    inputs_dict["decoder_input_ids"],
														    inputs_dict["decoder_attention_mask"],
														)

														_lowerCAmelCase     :							Optional[int]    =    jnp.concatenate(
														    [
														        decoder_attention_mask,
														        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
														    ],			axis=-1,			)

														_lowerCAmelCase     :							Tuple    =    model.init_cache(decoder_input_ids.shape[0],			__a,			__a)
														_lowerCAmelCase     :							Union[str, Any]    =    jnp.broadcast_to(
														    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),			)

														_lowerCAmelCase     :							Optional[int]    =    model.decode(
														    decoder_input_ids[:, :-1],			__a,			decoder_attention_mask=__a,			past_key_values=__a,			decoder_position_ids=__a,			)
														_lowerCAmelCase     :							str    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],			dtype="i4")
														_lowerCAmelCase     :							Union[str, Any]    =    model.decode(
														    decoder_input_ids[:, -1:],			__a,			past_key_values=outputs_cache.past_key_values,			decoder_attention_mask=__a,			decoder_position_ids=__a,			)

														_lowerCAmelCase     :							Optional[int]    =    model.decode(__a,			__a,			decoder_attention_mask=__a)

														_lowerCAmelCase     :							Union[str, Any]    =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
														self.parent.assertTrue(diff < 1E-3,			msg=f"Max diff is {diff}")






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=None				, _lowerCamelCase=None				, ):





							'''simple docstring'''




							if attention_mask is None:
														_lowerCAmelCase     :							Tuple    =    np.not_equal(_lowerCamelCase				, config.pad_token_id  ).astype(np.inta  )
							if decoder_attention_mask is None:
														_lowerCAmelCase     :							str    =    np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape				, dtype=np.inta  ),
														        np.not_equal(decoder_input_ids[:, 1:]				, config.pad_token_id  ).astype(np.inta  ),
														    ]				, axis=-1				, )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}



@require_flax
class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							lowerCamelCase__   			=		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							lowerCamelCase__   			=		True
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    FlaxPegasusModelTester(self)
														_lowerCAmelCase     :							int    =    ConfigTester(self,			config_class=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    self.model_tester.prepare_config_and_inputs_for_common()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs_for_common()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward_with_attn_mask(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__):
																												_lowerCAmelCase     :							Dict    =    self._prepare_for_class(__a,			__a)
																												_lowerCAmelCase     :							Optional[int]    =    model_class(__a)

																												@jax.jit
																												def encode_jitted(__a,			__a=None,			**__a):
																																			return model.encode(input_ids=__a,			attention_mask=__a)

																												with self.subTest("JIT Enabled"):
																																			_lowerCAmelCase     :							List[Any]    =    encode_jitted(**__a).to_tuple()

																												with self.subTest("JIT Disabled"):
																																			with jax.disable_jit():
																																										_lowerCAmelCase     :							Optional[int]    =    encode_jitted(**__a).to_tuple()

																												self.assertEqual(len(__a),			len(__a))
																												for jitted_output, output in zip(__a,			__a):
																																			self.assertEqual(jitted_output.shape,			output.shape)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__):
																												_lowerCAmelCase     :							List[str]    =    model_class(__a)
																												_lowerCAmelCase     :							Optional[int]    =    model.encode(inputs_dict["input_ids"],			inputs_dict["attention_mask"])

																												_lowerCAmelCase     :							str    =    {
																												    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																												    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																												    "encoder_outputs": encoder_outputs,
																												}

																												@jax.jit
																												def decode_jitted(__a,			__a,			__a):
																																			return model.decode(
																																			    decoder_input_ids=__a,			decoder_attention_mask=__a,			encoder_outputs=__a,			)

																												with self.subTest("JIT Enabled"):
																																			_lowerCAmelCase     :							str    =    decode_jitted(**__a).to_tuple()

																												with self.subTest("JIT Disabled"):
																																			with jax.disable_jit():
																																										_lowerCAmelCase     :							str    =    decode_jitted(**__a).to_tuple()

																												self.assertEqual(len(__a),			len(__a))
																												for jitted_output, output in zip(__a,			__a):
																																			self.assertEqual(jitted_output.shape,			output.shape)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_class_name in self.all_model_classes:
																					_lowerCAmelCase     :							Optional[int]    =    model_class_name.from_pretrained("google/pegasus-large",			from_pt=__a)
																					_lowerCAmelCase     :							Dict    =    np.ones((1, 1))
																					_lowerCAmelCase     :							List[str]    =    model(__a)
																					self.assertIsNotNone(__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
														_lowerCAmelCase     :							Any    =    PegasusTokenizer.from_pretrained("google/pegasus-xsum")

														_lowerCAmelCase     :							Optional[int]    =    [
														    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
														    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
														]

														_lowerCAmelCase     :							List[Any]    =    [
														    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
														    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
														]

														_lowerCAmelCase     :							int    =    tokenizer(__a,			return_tensors="np",			truncation=__a,			max_length=512,			padding=__a)
														_lowerCAmelCase     :							Union[str, Any]    =    model.generate(**__a,			num_beams=2).sequences
														_lowerCAmelCase     :							Union[str, Any]    =    tokenizer.batch_decode(__a,			skip_special_tokens=__a)
														assert tgt_text == decoded








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


_a      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  None:





						"""simple docstring"""



						_UpperCAmelCase									=			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()	)


_a      	=  {
    # 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))},
}
_a      	=  {
    # 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    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""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(__lowerCAmelCase	)	)


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





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

						# Copy weights
						_UpperCAmelCase									=			teacher.config_class(**__lowerCAmelCase	)
						_UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase	)
						# 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=__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
												_UpperCAmelCase					, _UpperCAmelCase									=			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:
												_UpperCAmelCase									=			pick_layers_to_copy(__lowerCAmelCase ,		__lowerCAmelCase	)
						if d_layers_to_copy is None:
												_UpperCAmelCase									=			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}"""	)
						_UpperCAmelCase									=			{
						    '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)
