


'''simple docstring'''



from abc import ABC, abstractmethod
from typing import List, Optional






class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				Dict   )->  Dict:

										"""simple docstring"""




										self.test()




				def   a__(	self    :				Tuple   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			0
										UpperCAmelCase         =			False
										while not completed:
																if counter == 1:
																						self.reset()
																UpperCAmelCase         =			self.advance()
																if not self.does_advance(lowerCAmelCase   ):
																						raise Exception(
																						    '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''   )

																UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			self.update(lowerCAmelCase   )
																counter += 1

																if counter > 10000:
																						raise Exception('''update() does not fulfill the constraint.'''   )

										if self.remaining() != 0:
																raise Exception('''Custom Constraint is not defined correctly.'''   )




				@abstractmethod
				def   a__(	self    :				str   )->  Any:

										"""simple docstring"""




										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""   )




				@abstractmethod
				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				int   )->  int:

										"""simple docstring"""




										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""   )




				@abstractmethod
				def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				int   )->  Tuple:

										"""simple docstring"""




										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""   )




				@abstractmethod
				def   a__(	self    :				List[Any]   )->  Any:

										"""simple docstring"""




										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""   )




				@abstractmethod
				def   a__(	self    :				List[str]   )->  Dict:

										"""simple docstring"""




										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""   )





				@abstractmethod
				def   a__(	self    :				int   ,					lowerCAmelCase    :				Tuple=False   )->  Optional[int]:

										"""simple docstring"""




										raise NotImplementedError(
										    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""   )







class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				Optional[int]   ,					lowerCAmelCase    :				List[int]   )->  List[str]:

										"""simple docstring"""




										super(lowerCAmelCase   ,					self   ).__init__()

										if not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) or len(lowerCAmelCase   ) == 0:
																raise ValueError(F"""`token_ids` has to be a non-empty list, but is {token_ids}."""   )
										if any((not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) or token_id < 0) for token_id in token_ids   ):
																raise ValueError(F"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."""   )

										UpperCAmelCase         =			token_ids

										UpperCAmelCase         =			len(self.token_ids   )
										UpperCAmelCase         =			-1  # the index of the currently fulfilled step
										UpperCAmelCase         =			False




				def   a__(	self    :				Optional[Any]   )->  Dict:

										"""simple docstring"""




										if self.completed:
																return None
										return self.token_ids[self.fulfilled_idx + 1]




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				int   )->  List[str]:

										"""simple docstring"""




										if not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(lowerCAmelCase   )}"""   )

										if self.completed:
																return False

										return token_id == self.token_ids[self.fulfilled_idx + 1]




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				int   )->  Optional[int]:

										"""simple docstring"""




										if not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(lowerCAmelCase   )}"""   )

										UpperCAmelCase         =			False
										UpperCAmelCase         =			False
										UpperCAmelCase         =			False

										if self.does_advance(lowerCAmelCase   ):
																self.fulfilled_idx += 1
																UpperCAmelCase         =			True
																if self.fulfilled_idx == (self.seqlen - 1):
																						UpperCAmelCase         =			True
																UpperCAmelCase         =			completed
										else:
																# failed to make progress.
																UpperCAmelCase         =			True
																self.reset()
										return stepped, completed, reset




				def   a__(	self    :				Dict   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			False
										UpperCAmelCase         =			0




				def   a__(	self    :				int   )->  List[str]:

										"""simple docstring"""




										return self.seqlen - (self.fulfilled_idx + 1)





				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				str=False   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			PhrasalConstraint(self.token_ids   )

										if stateful:
																UpperCAmelCase         =			self.seqlen
																UpperCAmelCase         =			self.fulfilled_idx
																UpperCAmelCase         =			self.completed

										return new_constraint







class  UpperCamelCase__:




				def __init__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				List[List[int]]   ,					lowerCAmelCase    :				Dict=True   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			max([len(lowerCAmelCase   ) for one in nested_token_ids]   )

										UpperCAmelCase         =			{}
										for token_ids in nested_token_ids:
																UpperCAmelCase         =			root
																for tidx, token_id in enumerate(lowerCAmelCase   ):
																						if token_id not in level:
																												UpperCAmelCase         =			{}

																						UpperCAmelCase         =			level[token_id]

										if no_subsets and self.has_subsets(lowerCAmelCase   ,					lowerCAmelCase   ):
																raise ValueError(
																    '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
																    F""" {nested_token_ids}."""   )

										UpperCAmelCase         =			root




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.trie

										for current_token in current_seq:
																UpperCAmelCase         =			start[current_token]

										UpperCAmelCase         =			list(start.keys()   )

										return next_tokens




				def   a__(	self    :				int   ,					lowerCAmelCase    :				Dict   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.next_tokens(lowerCAmelCase   )

										return len(lowerCAmelCase   ) == 0




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			list(root.values()   )
										if len(lowerCAmelCase   ) == 0:
																return 1
										else:
																return sum([self.count_leaves(lowerCAmelCase   ) for nn in next_nodes]   )





				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				int   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.count_leaves(lowerCAmelCase   )
										return len(lowerCAmelCase   ) != leaf_count







class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				List[Any]   ,					lowerCAmelCase    :				List[List[int]]   )->  int:

										"""simple docstring"""




										super(lowerCAmelCase   ,					self   ).__init__()

										if not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) or len(lowerCAmelCase   ) == 0:
																raise ValueError(F"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."""   )
										if any(not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) for token_ids in nested_token_ids   ):
																raise ValueError(F"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."""   )
										if any(
										    any((not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) or token_id < 0) for token_id in token_ids   )
										    for token_ids in nested_token_ids   ):
																raise ValueError(
																    F"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."""   )

										UpperCAmelCase         =			DisjunctiveTrie(lowerCAmelCase   )
										UpperCAmelCase         =			nested_token_ids

										UpperCAmelCase         =			self.trie.max_height
										UpperCAmelCase         =			[]
										UpperCAmelCase         =			False




				def   a__(	self    :				Dict   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.trie.next_tokens(self.current_seq   )

										if len(lowerCAmelCase   ) == 0:
																return None
										else:
																return token_list




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				int   )->  int:

										"""simple docstring"""




										if not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCAmelCase   )}"""   )

										UpperCAmelCase         =			self.trie.next_tokens(self.current_seq   )

										return token_id in next_tokens




				def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				int   )->  Tuple:

										"""simple docstring"""




										if not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCAmelCase   )}"""   )

										UpperCAmelCase         =			False
										UpperCAmelCase         =			False
										UpperCAmelCase         =			False

										if self.does_advance(lowerCAmelCase   ):
																self.current_seq.append(lowerCAmelCase   )
																UpperCAmelCase         =			True
										else:
																UpperCAmelCase         =			True
																self.reset()

										UpperCAmelCase         =			self.trie.reached_leaf(self.current_seq   )
										UpperCAmelCase         =			completed

										return stepped, completed, reset




				def   a__(	self    :				str   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			False
										UpperCAmelCase         =			[]




				def   a__(	self    :				Dict   )->  str:

										"""simple docstring"""




										if self.completed:
																# since this can be completed without reaching max height
																return 0
										else:
																return self.seqlen - len(self.current_seq   )





				def   a__(	self    :				int   ,					lowerCAmelCase    :				Any=False   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			DisjunctiveConstraint(self.token_ids   )

										if stateful:
																UpperCAmelCase         =			self.seqlen
																UpperCAmelCase         =			self.current_seq
																UpperCAmelCase         =			self.completed

										return new_constraint







class  UpperCamelCase__:




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				List[Constraint]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			constraints

										# max # of steps required to fulfill a given constraint
										UpperCAmelCase         =			max([c.seqlen for c in constraints]   )
										UpperCAmelCase         =			len(lowerCAmelCase   )
										UpperCAmelCase         =			False

										self.init_state()




				def   a__(	self    :				Optional[Any]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[]
										UpperCAmelCase         =			None
										UpperCAmelCase         =			[constraint.copy(stateful=lowerCAmelCase   ) for constraint in self.constraints]




				def   a__(	self    :				Optional[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			0
										if self.inprogress_constraint:
																# extra points for having a constraint mid-fulfilled
																add += self.max_seqlen - self.inprogress_constraint.remaining()

										return (len(self.complete_constraints   ) * self.max_seqlen) + add




				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			[]
										if self.inprogress_constraint is None:
																for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
																						UpperCAmelCase         =			constraint.advance()
																						if isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																												token_list.append(lowerCAmelCase   )
																						elif isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																												token_list.extend(lowerCAmelCase   )
										else:
																UpperCAmelCase         =			self.inprogress_constraint.advance()
																if isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																						token_list.append(lowerCAmelCase   )
																elif isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																						token_list.extend(lowerCAmelCase   )

										if len(lowerCAmelCase   ) == 0:
																return None
										else:
																return token_list




				def   a__(	self    :				str   ,					lowerCAmelCase    :				Optional[List[int]]   )->  str:

										"""simple docstring"""




										self.init_state()

										if token_ids is not None:
																for token in token_ids:
																						# completes or steps **one** constraint
																						UpperCAmelCase       ,       UpperCAmelCase         =			self.add(lowerCAmelCase   )

																						# the entire list of constraints are fulfilled
																						if self.completed:
																												break




				def   a__(	self    :				str   ,					lowerCAmelCase    :				int   )->  Dict:

										"""simple docstring"""




										if not isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`."""   )

										UpperCAmelCase       ,       UpperCAmelCase         =			False, False

										if self.completed:
																UpperCAmelCase         =			True
																UpperCAmelCase         =			False
																return complete, stepped

										if self.inprogress_constraint is not None:
																# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
																# job, simply update the state

																UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			self.inprogress_constraint.update(lowerCAmelCase   )
																if reset:
																						# 1. If the next token breaks the progress, then we must restart.
																						#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

																						#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
																						#     constraint, not the full list of constraints.

																						self.pending_constraints.append(self.inprogress_constraint.copy(stateful=lowerCAmelCase   )   )
																						UpperCAmelCase         =			None

																if complete:
																						# 2. If the next token completes the constraint, move it to completed list, set
																						#     inprogress to None. If there are no pending constraints either, then this full list of constraints
																						#     is complete.

																						self.complete_constraints.append(self.inprogress_constraint   )
																						UpperCAmelCase         =			None

																						if len(self.pending_constraints   ) == 0:
																												# we're done!
																												UpperCAmelCase         =			True

										else:
																# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
																# of constraints?

																for cidx, pending_constraint in enumerate(self.pending_constraints   ):
																						if pending_constraint.does_advance(lowerCAmelCase   ):
																												UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			pending_constraint.update(lowerCAmelCase   )

																												if not stepped:
																																		raise Exception(
																																		    '''`constraint.update(token_id)` is not yielding incremental progress, '''
																																		    '''even though `constraint.does_advance(token_id)` is true.'''   )

																												if complete:
																																		self.complete_constraints.append(lowerCAmelCase   )
																																		UpperCAmelCase         =			None

																												if not complete and stepped:
																																		UpperCAmelCase         =			pending_constraint

																												if complete or stepped:
																																		# If we made any progress at all, then it's at least not a "pending constraint".

																																		UpperCAmelCase         =			(
																																		    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																																		)

																																		if len(self.pending_constraints   ) == 0 and self.inprogress_constraint is None:
																																								# If there's no longer any pending after this and no inprogress either, then we must be
																																								# complete.

																																								UpperCAmelCase         =			True

																																		break  # prevent accidentally stepping through multiple constraints with just one token.

										return complete, stepped





				def   a__(	self    :				str   ,					lowerCAmelCase    :				str=True   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			ConstraintListState(self.constraints   )  # we actually never though self.constraints objects
										# throughout this process. So it's at initialization state.

										if stateful:
																UpperCAmelCase         =			[
																    constraint.copy(stateful=lowerCAmelCase   ) for constraint in self.complete_constraints
																]
																if self.inprogress_constraint is not None:
																						UpperCAmelCase         =			self.inprogress_constraint.copy(stateful=lowerCAmelCase   )
																UpperCAmelCase         =			[constraint.copy() for constraint in self.pending_constraints]

										return new_state









'''simple docstring'''



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_lowercase    :    Tuple			     =  {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def    lowerCamelCase__   ( A     :    Optional[Any]  ):




						'''simple docstring'''






						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts  )
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config  )
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights  )

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def    lowerCamelCase__   ( A     :    Any   ,     A     :    str  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False
						elif args.student_type == "gpt2":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    List[str]  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Training'''  )
						parser.add_argument('''--force'''   ,     action='''store_true'''   ,     help='''Overwrite dump_path if it already exists.'''  )

						parser.add_argument(
						    '''--dump_path'''   ,     type=A   ,     required=A   ,     help='''The output directory (log, checkpoints, parameters, etc.)'''  )
						parser.add_argument(
						    '''--data_file'''   ,     type=A   ,     required=A   ,     help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''   ,     )

						parser.add_argument(
						    '''--student_type'''   ,     type=A   ,     choices=['''distilbert''', '''roberta''', '''gpt2''']   ,     required=A   ,     help='''The student type (DistilBERT, RoBERTa).'''   ,     )
						parser.add_argument('''--student_config'''   ,     type=A   ,     required=A   ,     help='''Path to the student configuration.'''  )
						parser.add_argument(
						    '''--student_pretrained_weights'''   ,     default=A   ,     type=A   ,     help='''Load student initialization checkpoint.'''  )

						parser.add_argument(
						    '''--teacher_type'''   ,     choices=['''bert''', '''roberta''', '''gpt2''']   ,     required=A   ,     help='''Teacher type (BERT, RoBERTa).'''  )
						parser.add_argument('''--teacher_name'''   ,     type=A   ,     required=A   ,     help='''The teacher model.'''  )

						parser.add_argument('''--temperature'''   ,     default=2.0   ,     type=A   ,     help='''Temperature for the softmax temperature.'''  )
						parser.add_argument(
						    '''--alpha_ce'''   ,     default=0.5   ,     type=A   ,     help='''Linear weight for the distillation loss. Must be >=0.'''  )
						parser.add_argument(
						    '''--alpha_mlm'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''   ,     )
						parser.add_argument('''--alpha_clm'''   ,     default=0.5   ,     type=A   ,     help='''Linear weight for the CLM loss. Must be >=0.'''  )
						parser.add_argument('''--alpha_mse'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight of the MSE loss. Must be >=0.'''  )
						parser.add_argument(
						    '''--alpha_cos'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight of the cosine embedding loss. Must be >=0.'''  )

						parser.add_argument(
						    '''--mlm'''   ,     action='''store_true'''   ,     help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''  )
						parser.add_argument(
						    '''--mlm_mask_prop'''   ,     default=0.15   ,     type=A   ,     help='''Proportion of tokens for which we need to make a prediction.'''   ,     )
						parser.add_argument('''--word_mask'''   ,     default=0.8   ,     type=A   ,     help='''Proportion of tokens to mask out.'''  )
						parser.add_argument('''--word_keep'''   ,     default=0.1   ,     type=A   ,     help='''Proportion of tokens to keep.'''  )
						parser.add_argument('''--word_rand'''   ,     default=0.1   ,     type=A   ,     help='''Proportion of tokens to randomly replace.'''  )
						parser.add_argument(
						    '''--mlm_smoothing'''   ,     default=0.7   ,     type=A   ,     help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''   ,     )
						parser.add_argument('''--token_counts'''   ,     type=A   ,     help='''The token counts in the data_file for MLM.'''  )

						parser.add_argument(
						    '''--restrict_ce_to_mask'''   ,     action='''store_true'''   ,     help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''   ,     )
						parser.add_argument(
						    '''--freeze_pos_embs'''   ,     action='''store_true'''   ,     help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''   ,     )
						parser.add_argument(
						    '''--freeze_token_type_embds'''   ,     action='''store_true'''   ,     help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''   ,     )

						parser.add_argument('''--n_epoch'''   ,     type=A   ,     default=3   ,     help='''Number of pass on the whole dataset.'''  )
						parser.add_argument('''--batch_size'''   ,     type=A   ,     default=5   ,     help='''Batch size (for each process).'''  )
						parser.add_argument(
						    '''--group_by_size'''   ,     action='''store_false'''   ,     help='''If true, group sequences that have similar length into the same batch. Default is true.'''   ,     )

						parser.add_argument(
						    '''--gradient_accumulation_steps'''   ,     type=A   ,     default=50   ,     help='''Gradient accumulation for larger training batches.'''   ,     )
						parser.add_argument('''--warmup_prop'''   ,     default=0.05   ,     type=A   ,     help='''Linear warmup proportion.'''  )
						parser.add_argument('''--weight_decay'''   ,     default=0.0   ,     type=A   ,     help='''Weight decay if we apply some.'''  )
						parser.add_argument('''--learning_rate'''   ,     default=5E-4   ,     type=A   ,     help='''The initial learning rate for Adam.'''  )
						parser.add_argument('''--adam_epsilon'''   ,     default=1E-6   ,     type=A   ,     help='''Epsilon for Adam optimizer.'''  )
						parser.add_argument('''--max_grad_norm'''   ,     default=5.0   ,     type=A   ,     help='''Max gradient norm.'''  )
						parser.add_argument('''--initializer_range'''   ,     default=0.02   ,     type=A   ,     help='''Random initialization range.'''  )

						parser.add_argument(
						    '''--fp16'''   ,     action='''store_true'''   ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,     )
						parser.add_argument(
						    '''--fp16_opt_level'''   ,     type=A   ,     default='''O1'''   ,     help=(
						        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
						        '''See details at https://nvidia.github.io/apex/amp.html'''
						    )   ,     )
						parser.add_argument('''--n_gpu'''   ,     type=A   ,     default=1   ,     help='''Number of GPUs in the node.'''  )
						parser.add_argument('''--local_rank'''   ,     type=A   ,     default=-1   ,     help='''Distributed training - Local rank'''  )
						parser.add_argument('''--seed'''   ,     type=A   ,     default=56   ,     help='''Random seed'''  )

						parser.add_argument('''--log_interval'''   ,     type=A   ,     default=5_00   ,     help='''Tensorboard logging interval.'''  )
						parser.add_argument('''--checkpoint_interval'''   ,     type=A   ,     default=40_00   ,     help='''Checkpoint interval.'''  )
						UpperCAmelCase         =			parser.parse_args()
						sanity_checks(A  )

						# ARGS #
						init_gpu_params(A  )
						set_seed(A  )
						if args.is_master:
												if os.path.exists(args.dump_path  ):
																		if not args.force:
																								raise ValueError(
																								    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																								    ''' itUse `--force` if you want to overwrite it'''  )
																		else:
																								shutil.rmtree(args.dump_path  )

												if not os.path.exists(args.dump_path  ):
																		os.makedirs(args.dump_path  )
												logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""  )

												# SAVE PARAMS #
												logger.info(f"""Param: {args}"""  )
												with open(os.path.join(args.dump_path   ,     '''parameters.json'''  )   ,     '''w'''  ) as f:
																		json.dump(vars(A  )   ,     A   ,     indent=4  )
												git_log(args.dump_path  )

						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.student_type]
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						UpperCAmelCase         =			teacher_tokenizer_class.from_pretrained(args.teacher_name  )
						UpperCAmelCase         =			{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												UpperCAmelCase         =			tokenizer.all_special_tokens.index(A  )
												UpperCAmelCase         =			tokenizer.all_special_ids[idx]
						logger.info(f"""Special tokens {special_tok_ids}"""  )
						UpperCAmelCase         =			special_tok_ids
						UpperCAmelCase         =			tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(f"""Loading data from {args.data_file}"""  )
						with open(args.data_file   ,     '''rb'''  ) as fp:
												UpperCAmelCase         =			pickle.load(A  )

						if args.mlm:
												logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""  )
												with open(args.token_counts   ,     '''rb'''  ) as fp:
																		UpperCAmelCase         =			pickle.load(A  )

												UpperCAmelCase         =			np.maximum(A   ,     1  ) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		UpperCAmelCase         =			0.0  # do not predict special tokens
												UpperCAmelCase         =			torch.from_numpy(A  )
						else:
												UpperCAmelCase         =			None

						UpperCAmelCase         =			LmSeqsDataset(params=A   ,     data=A  )
						logger.info('''Data loader created.'''  )

						# STUDENT #
						logger.info(f"""Loading student config from {args.student_config}"""  )
						UpperCAmelCase         =			student_config_class.from_pretrained(args.student_config  )
						UpperCAmelCase         =			True

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
												UpperCAmelCase         =			student_model_class.from_pretrained(args.student_pretrained_weights   ,     config=A  )
						else:
												UpperCAmelCase         =			student_model_class(A  )

						if args.n_gpu > 0:
												student.to(f"""cuda:{args.local_rank}"""  )
						logger.info('''Student loaded.'''  )

						# TEACHER #
						UpperCAmelCase         =			teacher_model_class.from_pretrained(args.teacher_name   ,     output_hidden_states=A  )
						if args.n_gpu > 0:
												teacher.to(f"""cuda:{args.local_rank}"""  )
						logger.info(f"""Teacher loaded from {args.teacher_name}."""  )

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(A   ,     A  )
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(A   ,     A  )

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0  ) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						UpperCAmelCase         =			Distiller(
						    params=A   ,     dataset=A   ,     token_probs=A   ,     student=A   ,     teacher=A  )
						distiller.train()
						logger.info('''Let\'s go get some drinks.'''  )


if __name__ == "__main__":
	main()





