


'''simple docstring'''






import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)
class 						__UpperCamelCase	:





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Tuple=1_4  ,_UpperCamelCase		:Union[str, Any]=7  ,_UpperCamelCase		:Any=True  ,_UpperCamelCase		:List[Any]=True  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:Optional[Any]=True  ,_UpperCamelCase		:List[Any]=True  ,_UpperCamelCase		:str=9_9  ,_UpperCamelCase		:List[Any]=3_2  ,_UpperCamelCase		:List[str]=5  ,_UpperCamelCase		:Optional[int]=4  ,_UpperCamelCase		:Optional[int]=3_7  ,_UpperCamelCase		:int="gelu"  ,_UpperCamelCase		:str=0.1  ,_UpperCamelCase		:Optional[int]=0.1  ,_UpperCamelCase		:int=5_1_2  ,_UpperCamelCase		:Union[str, Any]=1_6  ,_UpperCamelCase		:Union[str, Any]=2  ,_UpperCamelCase		:List[Any]=0.02  ,_UpperCamelCase		:Any=3  ,_UpperCamelCase		:Tuple=4  ,_UpperCamelCase		:str=None  ,):
									snake_case_		:							Dict			       =						parent
									snake_case_		:							Optional[int]			       =						batch_size
									snake_case_		:							Union[str, Any]			       =						seq_length
									snake_case_		:							Union[str, Any]			       =						is_training
									snake_case_		:							Tuple			       =						use_token_type_ids
									snake_case_		:							Union[str, Any]			       =						use_input_mask
									snake_case_		:							Dict			       =						use_labels
									snake_case_		:							Optional[int]			       =						use_mc_token_ids
									snake_case_		:							Dict			       =						vocab_size
									snake_case_		:							List[Any]			       =						hidden_size
									snake_case_		:							List[Any]			       =						num_hidden_layers
									snake_case_		:							Union[str, Any]			       =						num_attention_heads
									snake_case_		:							Optional[Any]			       =						intermediate_size
									snake_case_		:							str			       =						hidden_act
									snake_case_		:							int			       =						hidden_dropout_prob
									snake_case_		:							Dict			       =						attention_probs_dropout_prob
									snake_case_		:							Union[str, Any]			       =						max_position_embeddings
									snake_case_		:							Tuple			       =						type_vocab_size
									snake_case_		:							Any			       =						type_sequence_label_size
									snake_case_		:							Dict			       =						initializer_range
									snake_case_		:							Tuple			       =						num_labels
									snake_case_		:							str			       =						num_choices
									snake_case_		:							int			       =						scope
									snake_case_		:							str			       =						self.vocab_size - 1





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

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

									snake_case_		:							Tuple			       =						None
									if self.use_token_type_ids:
											snake_case_		:							Optional[int]			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.type_vocab_size			)

									snake_case_		:							Any			       =						None
									if self.use_mc_token_ids:
											snake_case_		:							int			       =						ids_tensor([self.batch_size, self.num_choices]  ,self.seq_length			)

									snake_case_		:							Optional[Any]			       =						None
									snake_case_		:							str			       =						None
									snake_case_		:							Optional[Any]			       =						None
									if self.use_labels:
											snake_case_		:							int			       =						ids_tensor([self.batch_size]  ,self.type_sequence_label_size			)
											snake_case_		:							int			       =						ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels			)
											snake_case_		:							Tuple			       =						ids_tensor([self.batch_size]  ,self.num_choices			)

									snake_case_		:							Any			       =						self.get_config()

									snake_case_		:							Optional[Any]			       =						ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,2			)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)





							def 		a__   (  self		:Tuple			):
									return CTRLConfig(
									    vocab_size=self.vocab_size  ,n_embd=self.hidden_size  ,n_layer=self.num_hidden_layers  ,n_head=self.num_attention_heads  ,n_positions=self.max_position_embeddings  ,pad_token_id=self.pad_token_id  ,)





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:int  ,*_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Tuple			       =						CTRLModel(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()

									model(_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,head_mask=_UpperCamelCase			)
									model(_UpperCamelCase  ,token_type_ids=_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						model(_UpperCamelCase			)
									self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)			)
									self.parent.assertEqual(len(result.past_key_values			)  ,config.n_layer			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:List[Any]  ,*_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						CTRLLMHeadModel(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()

									snake_case_		:							Tuple			       =						model(_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,labels=_UpperCamelCase			)
									self.parent.assertEqual(result.loss.shape  ,()			)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Union[str, Any]			       =						self.prepare_config_and_inputs()

									(
									    snake_case_      
									)		:							List[str]			       =						config_and_inputs

									snake_case_		:							Union[str, Any]			       =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

									return config, inputs_dict





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[int]  ,*_UpperCamelCase		:int			):
									snake_case_		:							Tuple			       =						self.num_labels
									snake_case_		:							List[Any]			       =						CTRLForSequenceClassification(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							Optional[Any]			       =						ids_tensor([self.batch_size]  ,self.type_sequence_label_size			)
									snake_case_		:							str			       =						model(_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,labels=_UpperCamelCase			)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)			)





@require_torch
class 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		Optional[int]   		=  (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							lowercase			:		List[str]   		=  (CTRLLMHeadModel,) if is_torch_available() else ()
							lowercase			:		List[str]   		=  (
							    {
							        'feature-extraction': CTRLModel,
							        'text-classification': CTRLForSequenceClassification,
							        'text-generation': CTRLLMHeadModel,
							        'zero-shot': CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase			:		Dict   		=  True
							lowercase			:		Optional[Any]   		=  False
							lowercase			:		int   		=  False





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Optional[Any]			):
									if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
											# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
											# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
											# config could not be created.
											return True

									return False





							def 		a__   (  self		:Any			):
									snake_case_		:							int			       =						CTRLModelTester(self			)
									snake_case_		:							Dict			       =						ConfigTester(self  ,config_class=_UpperCamelCase  ,n_embd=3_7			)





							def 		a__   (  self		:Dict			):
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									torch.cuda.empty_cache()





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





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_ctrl_model(*_UpperCamelCase			)





							def 		a__   (  self		:int			):
									snake_case_		:							Optional[Any]			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(*_UpperCamelCase			)





							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
							def 		a__   (  self		:List[Any]			):
									pass





							@slow
							def 		a__   (  self		:int			):
									for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:							Any			       =						CTRLModel.from_pretrained(_UpperCamelCase			)
											self.assertIsNotNone(_UpperCamelCase			)





							@unittest.skip("""The model doesn't support left padding"""			)  # and it's not used enough to be worth fixing :)
							def 		a__   (  self		:str			):
									pass





@require_torch
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:Dict			):
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									torch.cuda.empty_cache()





							@slow
							def 		a__   (  self		:str			):
									snake_case_		:							Optional[int]			       =						CTRLLMHeadModel.from_pretrained("""ctrl"""			)
									model.to(_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						torch.tensor(
									    [[1_1_8_5_9, 0, 1_6_1_1, 8]]  ,dtype=torch.long  ,device=_UpperCamelCase			)  # Legal the president is
									snake_case_		:							List[str]			       =						[
									    1_1_8_5_9,
									    0,
									    1_6_1_1,
									    8,
									    5,
									    1_5_0,
									    2_6_4_4_9,
									    2,
									    1_9,
									    3_4_8,
									    4_6_9,
									    3,
									    2_5_9_5,
									    4_8,
									    2_0_7_4_0,
									    2_4_6_5_3_3,
									    2_4_6_5_3_3,
									    1_9,
									    3_0,
									    5,
									]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

									snake_case_		:							Dict			       =						model.generate(_UpperCamelCase  ,do_sample=_UpperCamelCase			)
									self.assertListEqual(output_ids[0].tolist()  ,_UpperCamelCase			)



'''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)