


'''simple docstring'''






from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFTransfoXLForSequenceClassification,
							    TFTransfoXLLMHeadModel,
							    TFTransfoXLModel,
							)
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:List[Any]  ,):
									snake_case_		:							str			       =						parent
									snake_case_		:							str			       =						1_3
									snake_case_		:							Dict			       =						7
									snake_case_		:							Optional[int]			       =						3_0
									snake_case_		:							Union[str, Any]			       =						self.seq_length + self.mem_len
									snake_case_		:							Tuple			       =						1_5
									snake_case_		:							Any			       =						True
									snake_case_		:							List[str]			       =						True
									snake_case_		:							Tuple			       =						9_9
									snake_case_		:							Optional[int]			       =						[1_0, 5_0, 8_0]
									snake_case_		:							Optional[Any]			       =						3_2
									snake_case_		:							int			       =						3_2
									snake_case_		:							Dict			       =						4
									snake_case_		:							Tuple			       =						8
									snake_case_		:							Dict			       =						1_2_8
									snake_case_		:							str			       =						2
									snake_case_		:							str			       =						2
									snake_case_		:							str			       =						None
									snake_case_		:							Optional[Any]			       =						1
									snake_case_		:							int			       =						0
									snake_case_		:							Dict			       =						3
									snake_case_		:							List[str]			       =						self.vocab_size - 1
									snake_case_		:							Tuple			       =						0.01





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Any			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size			)
									snake_case_		:							Any			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size			)

									snake_case_		:							Optional[Any]			       =						None
									if self.use_labels:
											snake_case_		:							Optional[int]			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size			)

									snake_case_		:							Union[str, Any]			       =						TransfoXLConfig(
									    vocab_size=self.vocab_size  ,mem_len=self.mem_len  ,clamp_len=self.clamp_len  ,cutoffs=self.cutoffs  ,d_model=self.hidden_size  ,d_embed=self.d_embed  ,n_head=self.num_attention_heads  ,d_head=self.d_head  ,d_inner=self.d_inner  ,div_val=self.div_val  ,n_layer=self.num_hidden_layers  ,eos_token_id=self.eos_token_id  ,pad_token_id=self.vocab_size - 1  ,init_range=self.init_range  ,num_labels=self.num_labels  ,)

									return (config, input_ids_a, input_ids_a, lm_labels)





							def 		a__   (  self		:int			):
									random.seed(self.seed			)
									tf.random.set_seed(self.seed			)





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Optional[int]			       =						TFTransfoXLModel(_a			)

									snake_case_		:							Optional[Any]			       =						model(_a			).to_tuple()

									snake_case_		:							Dict			       =						{"""input_ids""": input_ids_a, """mems""": mems_a}

									snake_case_		:							Optional[int]			       =						model(_a			).to_tuple()

									self.parent.assertEqual(hidden_states_a.shape  ,(self.batch_size, self.seq_length, self.hidden_size)			)
									self.parent.assertEqual(hidden_states_a.shape  ,(self.batch_size, self.seq_length, self.hidden_size)			)
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]  ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers  ,)
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]  ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers  ,)





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							Optional[int]			       =						TFTransfoXLLMHeadModel(_a			)

									snake_case_		:							Tuple			       =						model(_a			).to_tuple()

									snake_case_		:							Dict			       =						{"""input_ids""": input_ids_a, """labels""": lm_labels}
									snake_case_		:							Dict			       =						model(_a			).to_tuple()

									snake_case_		:							int			       =						model([input_ids_a, mems_a]			).to_tuple()

									snake_case_		:							Optional[Any]			       =						{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

									snake_case_		:							int			       =						model(_a			).to_tuple()

									self.parent.assertEqual(lm_logits_a.shape  ,(self.batch_size, self.seq_length, self.vocab_size)			)
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]  ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers  ,)

									self.parent.assertEqual(lm_logits_a.shape  ,(self.batch_size, self.seq_length, self.vocab_size)			)
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]  ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers  ,)





							def 		a__   (  self		:int  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:List[str]			):
									snake_case_		:							int			       =						TFTransfoXLForSequenceClassification(_a			)
									snake_case_		:							List[str]			       =						model(_a			)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							Optional[Any]			       =						self.prepare_config_and_inputs()
									(snake_case_)		:							Optional[Any]			       =						config_and_inputs
									snake_case_		:							List[str]			       =						{"""input_ids""": input_ids_a}
									return config, inputs_dict





@require_tf
class 						__UpperCamelCase	(						UpperCamelCase_						,  UpperCamelCase_						,  unittest.TestCase							):
							lowercase			:		Union[str, Any]   		=  (
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							lowercase			:		Union[str, Any]   		=  () if is_tf_available() else ()
							lowercase			:		Tuple   		=  (
							    {
							        """feature-extraction""": TFTransfoXLModel,
							        """text-classification""": TFTransfoXLForSequenceClassification,
							        """text-generation""": TFTransfoXLLMHeadModel,
							        """zero-shot""": TFTransfoXLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
							lowercase			:		List[str]   		=  False
							lowercase			:		Any   		=  False
							lowercase			:		Dict   		=  False
							lowercase			:		int   		=  False





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[Any]			):


									if pipeline_test_casse_name == "TextGenerationPipelineTests":
											# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
											# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
											# tokenizer.
											return True

									return False





							def 		a__   (  self		:int			):
									snake_case_		:							List[str]			       =						TFTransfoXLModelTester(self			)
									snake_case_		:							Optional[Any]			       =						ConfigTester(self  ,config_class=_a  ,d_embed=3_7			)





							def 		a__   (  self		:Any			):
									self.config_tester.run_common_tests()





							def 		a__   (  self		:Any			):
									self.model_tester.set_seed()
									snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*_a			)





							def 		a__   (  self		:Any			):
									self.model_tester.set_seed()
									snake_case_		:							Any			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*_a			)





							def 		a__   (  self		:str			):
									snake_case_		:							Dict			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_a			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Optional[Any]			       =						self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_		:							Optional[int]			       =						[TFTransfoXLForSequenceClassification]

									for model_class in self.all_model_classes:
											snake_case_		:							str			       =						model_class(_a			)
											assert isinstance(model.get_input_embeddings()  ,tf.keras.layers.Layer			)
											if model_class in list_other_models_with_output_ebd:
													snake_case_		:							List[Any]			       =						model.get_output_embeddings()
													assert isinstance(_a  ,tf.keras.layers.Layer			)
													snake_case_		:							Any			       =						model.get_bias()
													assert name is None
											else:
													snake_case_		:							Any			       =						model.get_output_embeddings()
													assert x is None
													snake_case_		:							List[Any]			       =						model.get_bias()
													assert name is None





							def 		a__   (  self		:Any			):
									pass





							@slow
							def 		a__   (  self		:Union[str, Any]			):


									for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:							Tuple			       =						TFTransfoXLModel.from_pretrained(_a			)
											self.assertIsNotNone(_a			)





							@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""			)
							def 		a__   (  self		:List[str]			):
									pass





@require_tf
class 						__UpperCamelCase	(						unittest.TestCase							):





							@unittest.skip("""Skip test until #12651 is resolved."""			)
							@slow
							def 		a__   (  self		:List[str]			):
									snake_case_		:							Tuple			       =						TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""			)
									# fmt: off
									snake_case_		:							Optional[int]			       =						tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]  ,dtype=tf.intaa			)  # noqa: E231
									# fmt: on
									#  In 1991 , the remains of Russian Tsar Nicholas II and his family
									#  ( except for Alexei and Maria ) are discovered .
									#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
									#  remainder of the story . 1883 Western Siberia ,
									#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
									#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
									#  father initially slaps him for making such an accusation , Rasputin watches as the
									#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
									#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
									#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

									# fmt: off
									snake_case_		:							int			       =						[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
									# fmt: on
									#  In 1991, the remains of Russian Tsar Nicholas II and his family (
									#  except for Alexei and Maria ) are discovered. The voice of young son,
									#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
									#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
									#  and a group of men to perform magic. Rasputin has a vision and
									#  denounces one of the men as a horse thief. Although his father initially
									#  slaps him for making such an accusation, Rasputin watches as the man
									#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
									#  of the Virgin Mary, prompting him to become a priest.
									#  Rasputin quickly becomes famous, with people, even a bishop, begging for
									#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
									# Nicholas II and his family were discovered. The voice of <unk> young son,
									# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

									snake_case_		:							Optional[Any]			       =						model.generate(_a  ,max_length=2_0_0  ,do_sample=_a			)
									self.assertListEqual(output_ids[0].numpy().tolist()  ,_a			)



'''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


__A	:	List[Any]     =     logging.get_logger(__name__)





def      UpperCAmelCase      (		lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :List[int] ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						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	:	Optional[int]     =     {
    # 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	:	Dict     =     {
    # 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      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		try:
				snake_case_		:							int			       =						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      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[Any] ):







		'''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      UpperCAmelCase      (		lowerCamelCase_  :Union[str, PreTrainedModel]				,      lowerCamelCase_  :Union[str, Path] = "student"				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :List[str]=False				,      lowerCamelCase_  :Optional[int]=None				,      lowerCamelCase_  :Optional[Any]=None				,      **lowerCamelCase_  :Dict				,      ):







		'''simple docstring'''
		snake_case_		:							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(lowerCamelCase_				,      lowerCamelCase_ ):
				AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ )  # purely for convenience
				snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ ).eval()
		else:
				assert isinstance(lowerCamelCase_				,      lowerCamelCase_ ), F'''teacher must be a model or string got type {type(lowerCamelCase_ )}'''
		snake_case_		:							Any			       =						teacher.config.to_diff_dict()

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