

import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_PRETRAINING_MAPPING,
					    MegatronBertForCausalLM,
					    MegatronBertForMaskedLM,
					    MegatronBertForMultipleChoice,
					    MegatronBertForNextSentencePrediction,
					    MegatronBertForPreTraining,
					    MegatronBertForQuestionAnswering,
					    MegatronBertForSequenceClassification,
					    MegatronBertForTokenClassification,
					    MegatronBertModel,
					)

class 				a  :


						def __init__(       self						,					A_						,					A_=13						,					A_=7						,					A_=True						,					A_=True						,					A_=True						,					A_=True						,					A_=99						,					A_=64						,					A_=32						,					A_=5						,					A_=4						,					A_=37						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=16						,					A_=2						,					A_=0.02						,					A_=3						,					A_=4						,					A_=None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    parent
										_UpperCAmelCase       :		Dict 		=    batch_size
										_UpperCAmelCase       :		List[str] 		=    seq_length
										_UpperCAmelCase       :		int 		=    is_training
										_UpperCAmelCase       :		str 		=    use_input_mask
										_UpperCAmelCase       :		Tuple 		=    use_token_type_ids
										_UpperCAmelCase       :		Any 		=    use_labels
										_UpperCAmelCase       :		List[str] 		=    vocab_size
										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    embedding_size
										_UpperCAmelCase       :		Tuple 		=    num_hidden_layers
										_UpperCAmelCase       :		int 		=    num_attention_heads
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		List[str] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		List[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		Optional[Any] 		=    type_vocab_size
										_UpperCAmelCase       :		str 		=    type_sequence_label_size
										_UpperCAmelCase       :		str 		=    initializer_range
										_UpperCAmelCase       :		List[str] 		=    num_labels
										_UpperCAmelCase       :		Union[str, Any] 		=    num_choices
										_UpperCAmelCase       :		Dict 		=    scope


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										_UpperCAmelCase       :		str 		=    None
										if self.use_input_mask:
														_UpperCAmelCase       :		Union[str, Any] 		=    random_attention_mask([self.batch_size, self.seq_length]	)

										_UpperCAmelCase       :		List[str] 		=    None
										if self.use_token_type_ids:
														_UpperCAmelCase       :		Optional[int] 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.type_vocab_size	)

										_UpperCAmelCase       :		Union[str, Any] 		=    None
										_UpperCAmelCase       :		str 		=    None
										_UpperCAmelCase       :		List[str] 		=    None
										if self.use_labels:
														_UpperCAmelCase       :		int 		=    ids_tensor([self.batch_size]						,					self.type_sequence_label_size	)
														_UpperCAmelCase       :		str 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels	)
														_UpperCAmelCase       :		Dict 		=    ids_tensor([self.batch_size]						,					self.num_choices	)

										_UpperCAmelCase       :		Union[str, Any] 		=    self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return MegatronBertConfig(
										    vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					num_hidden_layers=self.num_hidden_layers						,					num_attention_heads=self.num_attention_heads						,					intermediate_size=self.intermediate_size						,					embedding_size=self.embedding_size						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					max_position_embeddings=self.max_position_embeddings						,					type_vocab_size=self.type_vocab_size						,					is_decoder=__A						,					initializer_range=self.initializer_range						,					)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    MegatronBertModel(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		Tuple 		=    model(__A						,					attention_mask=__A						,					token_type_ids=__A	)
										_UpperCAmelCase       :		Any 		=    model(__A						,					token_type_ids=__A	)
										_UpperCAmelCase       :		List[str] 		=    model(__A	)

										self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)	)
										self.parent.assertEqual(result.pooler_output.shape						,					(self.batch_size, self.hidden_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    MegatronBertForMaskedLM(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		Tuple 		=    model(__A						,					attention_mask=__A						,					token_type_ids=__A						,					labels=__A	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    MegatronBertForCausalLM(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		str 		=    model(__A						,					attention_mask=__A						,					token_type_ids=__A						,					labels=__A	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    MegatronBertForNextSentencePrediction(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		Union[str, Any] 		=    model(
										    __A						,					attention_mask=__A						,					token_type_ids=__A						,					labels=__A						,					)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, 2)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    MegatronBertForPreTraining(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		int 		=    model(
										    __A						,					attention_mask=__A						,					token_type_ids=__A						,					labels=__A						,					next_sentence_label=__A						,					)
										self.parent.assertEqual(result.prediction_logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)	)
										self.parent.assertEqual(result.seq_relationship_logits.shape						,					(self.batch_size, 2)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    MegatronBertForQuestionAnswering(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		Tuple 		=    model(
										    __A						,					attention_mask=__A						,					token_type_ids=__A						,					start_positions=__A						,					end_positions=__A						,					)
										self.parent.assertEqual(result.start_logits.shape						,					(self.batch_size, self.seq_length)	)
										self.parent.assertEqual(result.end_logits.shape						,					(self.batch_size, self.seq_length)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.num_labels
										_UpperCAmelCase       :		int 		=    MegatronBertForSequenceClassification(__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		Union[str, Any] 		=    model(__A						,					attention_mask=__A						,					token_type_ids=__A						,					labels=__A	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.num_labels
										_UpperCAmelCase       :		int 		=    MegatronBertForTokenClassification(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		Tuple 		=    model(__A						,					attention_mask=__A						,					token_type_ids=__A						,					labels=__A	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.num_choices
										_UpperCAmelCase       :		Optional[int] 		=    MegatronBertForMultipleChoice(config=__A	)
										model.to(__A	)
										model.eval()
										_UpperCAmelCase       :		Tuple 		=    input_ids.unsqueeze(1	).expand(-1						,					self.num_choices						,					-1	).contiguous()
										_UpperCAmelCase       :		List[Any] 		=    token_type_ids.unsqueeze(1	).expand(-1						,					self.num_choices						,					-1	).contiguous()
										_UpperCAmelCase       :		Dict 		=    input_mask.unsqueeze(1	).expand(-1						,					self.num_choices						,					-1	).contiguous()
										_UpperCAmelCase       :		str 		=    model(
										    __A						,					attention_mask=__A						,					token_type_ids=__A						,					labels=__A						,					)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_choices)	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.prepare_config_and_inputs()
										(
										    (
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							
										)       :		Dict 		=    config_and_inputs
										_UpperCAmelCase       :		Dict 		=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_torch
class 				a  (	UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(
						    (
						        MegatronBertModel,
						        MegatronBertForMaskedLM,
						        MegatronBertForCausalLM,
						        MegatronBertForMultipleChoice,
						        MegatronBertForNextSentencePrediction,
						        MegatronBertForPreTraining,
						        MegatronBertForQuestionAnswering,
						        MegatronBertForSequenceClassification,
						        MegatronBertForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowercase    						=			(
						    {
						        "feature-extraction": MegatronBertModel,
						        "fill-mask": MegatronBertForMaskedLM,
						        "question-answering": MegatronBertForQuestionAnswering,
						        "text-classification": MegatronBertForSequenceClassification,
						        "text-generation": MegatronBertForCausalLM,
						        "token-classification": MegatronBertForTokenClassification,
						        "zero-shot": MegatronBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowercase    						=			True
						# test_resize_embeddings = False
						_lowercase    						=			False


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_=False	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    super()._prepare_for_class(__A						,					__A						,					return_labels=__A	)

										if return_labels:
														if model_class in get_values(__A	):
																		_UpperCAmelCase       :		int 		=    torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.seq_length)						,					dtype=torch.long						,					device=__A	)
																		_UpperCAmelCase       :		List[Any] 		=    torch.zeros(
																		    self.model_tester.batch_size						,					dtype=torch.long						,					device=__A	)
										return inputs_dict


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    MegatronBertModelTester(self	)
										_UpperCAmelCase       :		Tuple 		=    ConfigTester(self						,					config_class=__A						,					hidden_size=37	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_model(*__A	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__A	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__A	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__A	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_for_pretraining(*__A	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_for_question_answering(*__A	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__A	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_megatron_bert_for_token_classification(*__A	)







def 							__SCREAMING_SNAKE_CASE		(				lowerCAmelCase:  Optional[int] )   ->       int:
				return torch.tensor(
				    _lowercase  ,      dtype=torch.long  ,      device=_lowercase  ,      )


SCREAMING_SNAKE_CASE_        =      1e-4





@require_torch
@require_sentencepiece
@require_tokenizers
class 				a  (	unittest.TestCase				):


						@slow
						@unittest.skip("Model is not available."	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    "nvidia/megatron-bert-uncased-345m"
										if "MYDIR" in os.environ:
														_UpperCAmelCase       :		int 		=    os.path.join(os.environ["MYDIR"]						,					__A	)
										_UpperCAmelCase       :		int 		=    MegatronBertModel.from_pretrained(__A	)
										model.to(__A	)
										model.half()
										_UpperCAmelCase       :		Optional[int] 		=    _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]	)
										with torch.no_grad():
														_UpperCAmelCase       :		Optional[Any] 		=    model(__A	)[0]
										_UpperCAmelCase       :		Any 		=    torch.Size((1, 9, 1024)	)
										self.assertEqual(output.shape						,					__A	)

										_UpperCAmelCase       :		int 		=    [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
										for ii in range(3	):
														for jj in range(3	):
																		_UpperCAmelCase       :		List[Any] 		=    output[0, ii, jj]
																		_UpperCAmelCase       :		Tuple 		=    expected[3 * ii + jj]
																		_UpperCAmelCase       :		int 		=    "ii={} jj={} a={} b={}".format(__A						,					__A						,					__A						,					__A	)
																		self.assertTrue(math.isclose(__A						,					__A						,					rel_tol=__A						,					abs_tol=__A	)						,					msg=__A	)





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  List[int] )   ->       None:
				_UpperCAmelCase       :		str 		=    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() )


SCREAMING_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))},
}
SCREAMING_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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				try:
								_UpperCAmelCase       :		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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       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(lowerCAmelCase ) )
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, PreTrainedModel]  ,      lowerCAmelCase:  Union[str, Path] = "student"  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  str=False  ,      lowerCAmelCase:  Union[str, Any]=None  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  str  ,      )   ->       Tuple[PreTrainedModel, List[int], List[int]]:
				_UpperCAmelCase       :		Dict 		=    "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       :		Dict 		=    AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
				else:
								assert isinstance(lowerCAmelCase  ,      lowerCAmelCase ), F'teacher must be a model or string got type {type(lowerCAmelCase )}'
				_UpperCAmelCase       :		List[str] 		=    teacher.config.to_diff_dict()

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


