




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


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	Dict							,__A:	Tuple							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							nn.ModuleList([src_layers[i] for i in layers_to_copy]				)
					assert len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				), F'''{len(__SCREAMING_SNAKE_CASE				)} != {len(__SCREAMING_SNAKE_CASE				)}'''
					dest_layers.load_state_dict(layers_to_copy.state_dict()				)


__lowerCamelCase   :						Any				=			{
    # 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))},
}
__lowerCamelCase   :						int				=			{
    # 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 		lowercase__   (		__A:	Optional[int]							,__A:	Union[str, Any]				):





					'''simple docstring'''






					try:
										__magic_name__							:	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(__SCREAMING_SNAKE_CASE				)				)







def 		lowercase__   (		__A:	Optional[Any]							,__A:	Dict				):





					'''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(__SCREAMING_SNAKE_CASE				)				)
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def 		lowercase__   (		__A:	Any							,__A:	List[str] = "student"							,__A:	Optional[Any] = None							,__A:	str = None							,__A:	Any=False							,__A:	Optional[int]=None							,__A:	int=None							,**__A:	List[Any]							,):





					'''simple docstring'''






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

					try:
										__magic_name__							,    __magic_name__							:	Tuple    							=							teacher.config.encoder_layers, teacher.config.decoder_layers
										if e is None:
															__magic_name__							:	Optional[int]    							=							teacher_e
										if d is None:
															__magic_name__							:	Dict    							=							teacher_d
										init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}				)
					except AttributeError:  # T5
										if hasattr(teacher.config							,'''num_encoder_layers'''				):
															__magic_name__							,    __magic_name__							:	Optional[Any]    							=							teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
										else:
															__magic_name__							,    __magic_name__							:	Optional[Any]    							=							teacher.config.num_layers, teacher.config.num_decoder_layers
										if e is None:
															__magic_name__							:	Tuple    							=							teacher_e
										if d is None:
															__magic_name__							:	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(__SCREAMING_SNAKE_CASE				)

					# Copy weights
					__magic_name__							:	str    							=							teacher.config_class(**__SCREAMING_SNAKE_CASE				)
					__magic_name__							:	Optional[int]    							=							AutoModelForSeqaSeqLM.from_config(__SCREAMING_SNAKE_CASE				)
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					__magic_name__							:	Union[str, Any]    							=							student.load_state_dict(teacher.state_dict()							,strict=__SCREAMING_SNAKE_CASE				)
					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
										__magic_name__							,    __magic_name__							:	Optional[Any]    							=							list(range(__SCREAMING_SNAKE_CASE				)				), list(range(__SCREAMING_SNAKE_CASE				)				)
										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(__SCREAMING_SNAKE_CASE				)
										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:
										__magic_name__							:	Tuple    							=							pick_layers_to_copy(__SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE				)
					if d_layers_to_copy is None:
										__magic_name__							:	Any    							=							pick_layers_to_copy(__SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE				)

					try:
										if hasattr(
										    __SCREAMING_SNAKE_CASE							,'''prophetnet'''				):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers							,student.prophetnet.encoder.layers							,__SCREAMING_SNAKE_CASE				)
															copy_layers(teacher.prophetnet.decoder.layers							,student.prophetnet.decoder.layers							,__SCREAMING_SNAKE_CASE				)
										else:
															copy_layers(teacher.model.encoder.layers							,student.model.encoder.layers							,__SCREAMING_SNAKE_CASE				)
															copy_layers(teacher.model.decoder.layers							,student.model.decoder.layers							,__SCREAMING_SNAKE_CASE				)
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block							,student.encoder.block							,__SCREAMING_SNAKE_CASE				)
										copy_layers(teacher.decoder.block							,student.decoder.block							,__SCREAMING_SNAKE_CASE				)
					logger.info(
					    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''				)
					__magic_name__							:	Optional[int]    							=							{
					    '''teacher_type''': teacher.config.model_type,
					    '''copied_encoder_layers''': e_layers_to_copy,
					    '''copied_decoder_layers''': d_layers_to_copy,
					}
					student.save_pretrained(__SCREAMING_SNAKE_CASE				)
					# 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)









import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 		lowercase__   (		__A:	str							,__A:	Optional[Any]							,__A:	List[Any]							,__A:	str							,__A:	List[str]=True							,__A:	List[Any]="pt"				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							{'''add_prefix_space''': True} if isinstance(__A							,__A				) and not line.startswith(''' '''				) else {}
					__magic_name__							:	List[str]    							=							padding_side
					return tokenizer(
					    [line]							,max_length=__A							,padding='''max_length''' if pad_to_max_length else None							,truncation=__A							,return_tensors=__A							,add_special_tokens=__A							,**__A							,)







def 		lowercase__   (		__A:	int							,__A:	int							,__A:	Optional[Any]=None							,):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							input_ids.ne(__A				).any(dim=0				)
					if attention_mask is None:
										return input_ids[:, keep_column_mask]
					else:
										return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[str]       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any="train"       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   List[Any]=None       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   List[Any]=""       ,						)						->   Union[str, Any]:
								super().__init__()
								__magic_name__							:	Any    							=							Path(lowerCamelCase_  ).joinpath(type_path + '''.source'''  )
								__magic_name__							:	Tuple    							=							Path(lowerCamelCase_  ).joinpath(type_path + '''.target'''  )
								__magic_name__							:	str    							=							self.get_char_lens(self.src_file  )
								__magic_name__							:	Dict    							=							max_source_length
								__magic_name__							:	Any    							=							max_target_length
								assert min(self.src_lens  ) > 0, F'''found empty line in {self.src_file}'''
								__magic_name__							:	Union[str, Any]    							=							tokenizer
								__magic_name__							:	str    							=							prefix
								if n_obs is not None:
													__magic_name__							:	Optional[int]    							=							self.src_lens[:n_obs]
								__magic_name__							:	Optional[Any]    							=							src_lang
								__magic_name__							:	Tuple    							=							tgt_lang

			def __len__( self			:   Optional[int]  )						->   List[Any]:
								return len(self.src_lens  )

			def __getitem__( self			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	Optional[int]    							=							index + 1  # linecache starts at 1
								__magic_name__							:	Optional[int]    							=							self.prefix + linecache.getline(str(self.src_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								__magic_name__							:	Dict    							=							linecache.getline(str(self.tgt_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								assert source_line, F'''empty source line for index {index}'''
								assert tgt_line, F'''empty tgt line for index {index}'''

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer       ,						lowerCamelCase_  ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								__magic_name__							:	int    							=							(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer
								)
								__magic_name__							:	Optional[Any]    							=							self.tokenizer.generator if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer

								__magic_name__							:	Tuple    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_source_length       ,						'''right'''  )
								__magic_name__							:	int    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_target_length       ,						'''right'''  )

								__magic_name__							:	Optional[Any]    							=							source_inputs['''input_ids'''].squeeze()
								__magic_name__							:	List[str]    							=							target_inputs['''input_ids'''].squeeze()
								__magic_name__							:	Optional[int]    							=							source_inputs['''attention_mask'''].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   List[str]  )						->   Dict:
								return [len(lowerCamelCase_  ) for x in Path(lowerCamelCase_  ).open().readlines()]


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[Any]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	str    							=							torch.stack([x['''input_ids'''] for x in batch]  )
								__magic_name__							:	Any    							=							torch.stack([x['''attention_mask'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							torch.stack([x['''decoder_input_ids'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Optional[Any]    							=							(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Dict    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							,    __magic_name__							:	Optional[int]    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_       ,						attention_mask=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							{
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch






__lowerCamelCase   :						Dict				=			getLogger(__name__)







def 		lowercase__   (		__A:	List[List]				):





					'''simple docstring'''






					return list(itertools.chain.from_iterable(__A				)				)







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							get_git_info()
					save_json(__A							,os.path.join(__A							,'''git_log.json'''				)				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Union[str, Any]							,__A:	Union[str, Any]=4							,**__A:	Optional[Any]				):





					'''simple docstring'''






					with open(__A							,'''w'''				) as f:
										json.dump(__A							,__A							,indent=__A							,**__A				)







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					with open(__A				) as f:
										return json.load(__A				)







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	int    							=							git.Repo(search_parent_directories=__A				)
					__magic_name__							:	str    							=							{
					    '''repo_id''': str(__A				),
					    '''repo_sha''': str(repo.head.object.hexsha				),
					    '''repo_branch''': str(repo.active_branch				),
					    '''hostname''': str(socket.gethostname()				),
					}
					return repo_infos







def 		lowercase__   (		__A:	Callable							,__A:	Iterable				):





					'''simple docstring'''






					return list(map(__A							,__A				)				)







def 		lowercase__   (		__A:	int							,__A:	Dict				):





					'''simple docstring'''






					with open(__A							,'''wb'''				) as f:
										return pickle.dump(__A							,__A				)







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






					def remove_articles(__A:	Any				):
										return re.sub(r'''\b(a|an|the)\b'''							,''' '''							,__A				)

					def white_space_fix(__A:	Optional[int]				):
										return " ".join(text.split()				)

					def remove_punc(__A:	Any				):
										__magic_name__							:	Union[str, Any]    							=							set(string.punctuation				)
										return "".join(ch for ch in text if ch not in exclude				)

					def lower(__A:	Optional[int]				):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__A				)				)				)				)







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Optional[int]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Dict    							=							Counter(__A				) & Counter(__A				)
					__magic_name__							:	Tuple    							=							sum(common.values()				)
					if num_same == 0:
										return 0
					__magic_name__							:	int    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	Tuple    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	str    							=							(2 * precision * recall) / (precision + recall)
					return fa







def 		lowercase__   (		__A:	List[Any]							,__A:	Dict				):





					'''simple docstring'''






					return normalize_answer(__A				) == normalize_answer(__A				)







def 		lowercase__   (		__A:	List[str]							,__A:	List[str]				):





					'''simple docstring'''






					assert len(__A				) == len(__A				)
					__magic_name__							:	Optional[int]    							=							0
					for hypo, pred in zip(__A							,__A				):
										em += exact_match_score(__A							,__A				)
					if len(__A				) > 0:
										em /= len(__A				)
					return {"em": em}







def 		lowercase__   (		__A:	int				):





					'''simple docstring'''






					return model_prefix.startswith('''rag'''				)







def 		lowercase__   (		__A:	Optional[Any]							,__A:	List[str]							,__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					__magic_name__							:	Any    							=							'''dropout_rate'''
					for p in extra_params:
										if getattr(__A							,__A							,__A				):
															if not hasattr(__A							,__A				) and not hasattr(__A							,equivalent_param[p]				):
																				logger.info('''config doesn\'t have a `{}` attribute'''.format(__A				)				)
																				delattr(__A							,__A				)
																				continue
															__magic_name__							:	str    							=							p if hasattr(__A							,__A				) else equivalent_param[p]
															setattr(__A							,__A							,getattr(__A							,__A				)				)
															delattr(__A							,__A				)
					return hparams, config



