



"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__A      :							Any   =      logging.get_logger(__name__)


__A      :							Union[str, Any]   =      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
__A      :							Dict   =      {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

__A      :							List[Any]   =      {
    "allenai/led-base-16384": 16384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def        lowercase		(					):






				'''simple docstring'''



				_UpperCAmelCase							=			(
				    list(range(ord('''!''' )    ,   ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' )    ,   ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' )    ,   ord('''ÿ''' ) + 1 ) )
				)
				_UpperCAmelCase							=			bs[:]
				_UpperCAmelCase							=			0
				for b in range(2**8 ):
								if b not in bs:
												bs.append(_lowercase )
												cs.append(2**8 + n )
												n += 1
				_UpperCAmelCase							=			[chr(_lowercase ) for n in cs]
				return dict(zip(_lowercase    ,   _lowercase ) )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Optional[int] ):






				'''simple docstring'''



				_UpperCAmelCase							=			set()
				_UpperCAmelCase							=			word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								_UpperCAmelCase							=			char
				return pairs
class 	_a       (    _lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__						=		VOCAB_FILES_NAMES
			UpperCamelCase__						=		PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__						=		["""input_ids""", """attention_mask"""]



			def __init__(						self   :			List[str] ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Optional[int] ,      __UpperCamelCase   :			Optional[Any]="replace" ,      __UpperCamelCase   :			Optional[Any]="<s>" ,      __UpperCamelCase   :			Any="</s>" ,      __UpperCamelCase   :			int="</s>" ,      __UpperCamelCase   :			Optional[int]="<s>" ,      __UpperCamelCase   :			int="<unk>" ,      __UpperCamelCase   :			Any="<pad>" ,      __UpperCamelCase   :			str="<mask>" ,      __UpperCamelCase   :			Optional[int]=False ,      **__UpperCamelCase   :			str ,      )->List[Any]:
							_UpperCAmelCase							=			AddedToken(__UpperCamelCase ,      lstrip=__UpperCamelCase ,      rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase ,      __UpperCamelCase      ) else bos_token
							_UpperCAmelCase							=			AddedToken(__UpperCamelCase ,      lstrip=__UpperCamelCase ,      rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase ,      __UpperCamelCase      ) else eos_token
							_UpperCAmelCase							=			AddedToken(__UpperCamelCase ,      lstrip=__UpperCamelCase ,      rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase ,      __UpperCamelCase      ) else sep_token
							_UpperCAmelCase							=			AddedToken(__UpperCamelCase ,      lstrip=__UpperCamelCase ,      rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase ,      __UpperCamelCase      ) else cls_token
							_UpperCAmelCase							=			AddedToken(__UpperCamelCase ,      lstrip=__UpperCamelCase ,      rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase ,      __UpperCamelCase      ) else unk_token
							_UpperCAmelCase							=			AddedToken(__UpperCamelCase ,      lstrip=__UpperCamelCase ,      rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase ,      __UpperCamelCase      ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCAmelCase							=			AddedToken(__UpperCamelCase ,      lstrip=__UpperCamelCase ,      rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase ,      __UpperCamelCase      ) else mask_token

							super().__init__(
							    errors=__UpperCamelCase ,      bos_token=__UpperCamelCase ,      eos_token=__UpperCamelCase ,      unk_token=__UpperCamelCase ,      sep_token=__UpperCamelCase ,      cls_token=__UpperCamelCase ,      pad_token=__UpperCamelCase ,      mask_token=__UpperCamelCase ,      add_prefix_space=__UpperCamelCase ,      **__UpperCamelCase ,      )

							with open(__UpperCamelCase ,      encoding='''utf-8'''      ) as vocab_handle:
											_UpperCAmelCase							=			json.load(__UpperCamelCase      )
							_UpperCAmelCase							=			{v: k for k, v in self.encoder.items()}
							_UpperCAmelCase							=			errors  # how to handle errors in decoding
							_UpperCAmelCase							=			bytes_to_unicode()
							_UpperCAmelCase							=			{v: k for k, v in self.byte_encoder.items()}
							with open(__UpperCamelCase ,      encoding='''utf-8'''      ) as merges_handle:
											_UpperCAmelCase							=			merges_handle.read().split('''\n'''      )[1:-1]
							_UpperCAmelCase							=			[tuple(merge.split()      ) for merge in bpe_merges]
							_UpperCAmelCase							=			dict(zip(__UpperCamelCase ,      range(len(__UpperCamelCase      )      )      )      )
							_UpperCAmelCase							=			{}
							_UpperCAmelCase							=			add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							_UpperCAmelCase							=			re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''      )



			@property
			# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
			def        lowercase__  (						self   :			str      )->List[str]:
							return len(self.encoder      )



			def        lowercase__  (						self   :			Optional[int]      )->int:
							return dict(self.encoder ,      **self.added_tokens_encoder      )



			def        lowercase__  (						self   :			Tuple ,      __UpperCamelCase   :			Optional[Any]      )->Union[str, Any]:
							if token in self.cache:
											return self.cache[token]
							_UpperCAmelCase							=			tuple(__UpperCamelCase      )
							_UpperCAmelCase							=			get_pairs(__UpperCamelCase      )

							if not pairs:
											return token

							while True:
											_UpperCAmelCase							=			min(__UpperCamelCase ,      key=lambda __UpperCamelCase      : self.bpe_ranks.get(__UpperCamelCase ,      float('''inf'''      )      )      )
											if bigram not in self.bpe_ranks:
															break
											_UpperCAmelCase							=			bigram
											_UpperCAmelCase							=			[]
											_UpperCAmelCase							=			0
											while i < len(__UpperCamelCase      ):
															try:
																			_UpperCAmelCase							=			word.index(__UpperCamelCase ,      __UpperCamelCase      )
															except ValueError:
																			new_word.extend(word[i:]      )
																			break
															else:
																			new_word.extend(word[i:j]      )
																			_UpperCAmelCase							=			j

															if word[i] == first and i < len(__UpperCamelCase      ) - 1 and word[i + 1] == second:
																			new_word.append(first + second      )
																			i += 2
															else:
																			new_word.append(word[i]      )
																			i += 1
											_UpperCAmelCase							=			tuple(__UpperCamelCase      )
											_UpperCAmelCase							=			new_word
											if len(__UpperCamelCase      ) == 1:
															break
											else:
															_UpperCAmelCase							=			get_pairs(__UpperCamelCase      )
							_UpperCAmelCase							=			""" """.join(__UpperCamelCase      )
							_UpperCAmelCase							=			word
							return word



			def        lowercase__  (						self   :			Optional[int] ,      __UpperCamelCase   :			List[str]      )->str:
							_UpperCAmelCase							=			[]
							for token in re.findall(self.pat ,      __UpperCamelCase      ):
											_UpperCAmelCase							=			"""""".join(
											    self.byte_encoder[b] for b in token.encode('''utf-8'''      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase      ).split(''' '''      )      )
							return bpe_tokens



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			Tuple      )->Optional[int]:
							return self.encoder.get(__UpperCamelCase ,      self.encoder.get(self.unk_token      )      )



			def        lowercase__  (						self   :			int ,      __UpperCamelCase   :			List[str]      )->Tuple:
							return self.decoder.get(__UpperCamelCase      )



			def        lowercase__  (						self   :			Dict ,      __UpperCamelCase   :			Dict      )->str:
							_UpperCAmelCase							=			"""""".join(__UpperCamelCase      )
							_UpperCAmelCase							=			bytearray([self.byte_decoder[c] for c in text]      ).decode('''utf-8''' ,      errors=self.errors      )
							return text



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			Optional[str] = None      )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase      ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
											return
							_UpperCAmelCase							=			os.path.join(
							    __UpperCamelCase ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
							_UpperCAmelCase							=			os.path.join(
							    __UpperCamelCase ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

							with open(__UpperCamelCase ,      '''w''' ,      encoding='''utf-8'''      ) as f:
											f.write(json.dumps(self.encoder ,      indent=2 ,      sort_keys=__UpperCamelCase ,      ensure_ascii=__UpperCamelCase      ) + '''\n'''      )

							_UpperCAmelCase							=			0
							with open(__UpperCamelCase ,      '''w''' ,      encoding='''utf-8'''      ) as writer:
											writer.write('''#version: 0.2\n'''      )
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,      key=lambda __UpperCamelCase      : kv[1]      ):
															if index != token_index:
																			logger.warning(
																			    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																			    ''' Please check that the tokenizer is not corrupted!'''      )
																			_UpperCAmelCase							=			token_index
															writer.write(''' '''.join(__UpperCamelCase      ) + '''\n'''      )
															index += 1

							return vocab_file, merge_file



			def        lowercase__  (						self   :			Optional[int] ,      __UpperCamelCase   :			List[int] ,      __UpperCamelCase   :			Optional[List[int]] = None      )->List[int]:
							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_UpperCAmelCase							=			[self.cls_token_id]
							_UpperCAmelCase							=			[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			List[int] ,      __UpperCamelCase   :			Optional[List[int]] = None ,      __UpperCamelCase   :			bool = False      )->List[int]:
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__UpperCamelCase ,      token_ids_a=__UpperCamelCase ,      already_has_special_tokens=__UpperCamelCase      )

							if token_ids_a is None:
											return [1] + ([0] * len(__UpperCamelCase      )) + [1]
							return [1] + ([0] * len(__UpperCamelCase      )) + [1, 1] + ([0] * len(__UpperCamelCase      )) + [1]



			def        lowercase__  (						self   :			Optional[Any] ,      __UpperCamelCase   :			List[int] ,      __UpperCamelCase   :			Optional[List[int]] = None      )->List[int]:
							_UpperCAmelCase							=			[self.sep_token_id]
							_UpperCAmelCase							=			[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep      ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Optional[int]=False ,      **__UpperCamelCase   :			List[Any]      )->Dict:
							_UpperCAmelCase							=			kwargs.pop('''add_prefix_space''' ,      self.add_prefix_space      )
							if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase      ) > 0 and not text[0].isspace()):
											_UpperCAmelCase							=			""" """ + text
							return (text, kwargs)



			def        lowercase__  (						self   :			List[str] ,      __UpperCamelCase   :			Union[Dict[str, EncodedInput], BatchEncoding] ,      __UpperCamelCase   :			Optional[int] = None ,      __UpperCamelCase   :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,      __UpperCamelCase   :			Optional[int] = None ,      __UpperCamelCase   :			Optional[bool] = None ,      )->dict:
							_UpperCAmelCase							=			super()._pad(
							    encoded_inputs=__UpperCamelCase ,      max_length=__UpperCamelCase ,      padding_strategy=__UpperCamelCase ,      pad_to_multiple_of=__UpperCamelCase ,      return_attention_mask=__UpperCamelCase ,      )

							# Load from model defaults
							if return_attention_mask is None:
											_UpperCAmelCase							=			"""attention_mask""" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
											_UpperCAmelCase							=			encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											_UpperCAmelCase							=			len(encoded_inputs['''global_attention_mask''']      ) != len(__UpperCamelCase      )

											if needs_to_be_padded:
															_UpperCAmelCase							=			len(__UpperCamelCase      ) - len(encoded_inputs['''global_attention_mask''']      )

															if self.padding_side == "right":
																			# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																			_UpperCAmelCase							=			(
																			    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																			)
															elif self.padding_side == "left":
																			_UpperCAmelCase							=			[-1] * difference + encoded_inputs[
																			    """global_attention_mask"""
																			]
															else:
																			raise ValueError('''Invalid padding strategy:''' + str(self.padding_side      )      )

							return encoded_inputs








"""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


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







						'''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    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						if args.student_type == "roberta":
												snake_case_ :Tuple						=			False
						elif args.student_type == "gpt2":
												snake_case_ :Union[str, Any]						=			False
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						if args.student_type == "roberta":
												snake_case_ :List[str]						=			False
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			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=_lowercase,					required=_lowercase,					help="""The output directory (log, checkpoints, parameters, etc.)"""	)
						parser.add_argument(
						    """--data_file""",					type=_lowercase,					required=_lowercase,					help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""",					)

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

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

						parser.add_argument("""--temperature""",					default=2.0,					type=_lowercase,					help="""Temperature for the softmax temperature."""	)
						parser.add_argument(
						    """--alpha_ce""",					default=0.5,					type=_lowercase,					help="""Linear weight for the distillation loss. Must be >=0."""	)
						parser.add_argument(
						    """--alpha_mlm""",					default=0.0,					type=_lowercase,					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=_lowercase,					help="""Linear weight for the CLM loss. Must be >=0."""	)
						parser.add_argument("""--alpha_mse""",					default=0.0,					type=_lowercase,					help="""Linear weight of the MSE loss. Must be >=0."""	)
						parser.add_argument(
						    """--alpha_cos""",					default=0.0,					type=_lowercase,					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=_lowercase,					help="""Proportion of tokens for which we need to make a prediction.""",					)
						parser.add_argument("""--word_mask""",					default=0.8,					type=_lowercase,					help="""Proportion of tokens to mask out."""	)
						parser.add_argument("""--word_keep""",					default=0.1,					type=_lowercase,					help="""Proportion of tokens to keep."""	)
						parser.add_argument("""--word_rand""",					default=0.1,					type=_lowercase,					help="""Proportion of tokens to randomly replace."""	)
						parser.add_argument(
						    """--mlm_smoothing""",					default=0.7,					type=_lowercase,					help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""",					)
						parser.add_argument("""--token_counts""",					type=_lowercase,					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=_lowercase,					default=3,					help="""Number of pass on the whole dataset."""	)
						parser.add_argument("""--batch_size""",					type=_lowercase,					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=_lowercase,					default=50,					help="""Gradient accumulation for larger training batches.""",					)
						parser.add_argument("""--warmup_prop""",					default=0.05,					type=_lowercase,					help="""Linear warmup proportion."""	)
						parser.add_argument("""--weight_decay""",					default=0.0,					type=_lowercase,					help="""Weight decay if we apply some."""	)
						parser.add_argument("""--learning_rate""",					default=5e-4,					type=_lowercase,					help="""The initial learning rate for Adam."""	)
						parser.add_argument("""--adam_epsilon""",					default=1e-6,					type=_lowercase,					help="""Epsilon for Adam optimizer."""	)
						parser.add_argument("""--max_grad_norm""",					default=5.0,					type=_lowercase,					help="""Max gradient norm."""	)
						parser.add_argument("""--initializer_range""",					default=0.02,					type=_lowercase,					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=_lowercase,					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=_lowercase,					default=1,					help="""Number of GPUs in the node."""	)
						parser.add_argument("""--local_rank""",					type=_lowercase,					default=-1,					help="""Distributed training - Local rank"""	)
						parser.add_argument("""--seed""",					type=_lowercase,					default=56,					help="""Random seed"""	)

						parser.add_argument("""--log_interval""",					type=_lowercase,					default=500,					help="""Tensorboard logging interval."""	)
						parser.add_argument("""--checkpoint_interval""",					type=_lowercase,					default=4000,					help="""Checkpoint interval."""	)
						snake_case_ :Tuple						=			parser.parse_args()
						sanity_checks(_lowercase	)

						# ARGS #
						init_gpu_params(_lowercase	)
						set_seed(_lowercase	)
						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(_lowercase	),					_lowercase,					indent=4	)
												git_log(args.dump_path	)

						snake_case_,       snake_case_,       snake_case_ :Any						=			MODEL_CLASSES[args.student_type]
						snake_case_,       snake_case_,       snake_case_ :int						=			MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						snake_case_ :Any						=			teacher_tokenizer_class.from_pretrained(args.teacher_name	)
						snake_case_ :Optional[Any]						=			{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												snake_case_ :Union[str, Any]						=			tokenizer.all_special_tokens.index(_lowercase	)
												snake_case_ :Union[str, Any]						=			tokenizer.all_special_ids[idx]
						logger.info(f"""Special tokens {special_tok_ids}"""	)
						snake_case_ :str						=			special_tok_ids
						snake_case_ :Any						=			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:
												snake_case_ :str						=			pickle.load(_lowercase	)

						if args.mlm:
												logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""	)
												with open(args.token_counts,					"""rb"""	) as fp:
																		snake_case_ :Optional[Any]						=			pickle.load(_lowercase	)

												snake_case_ :Tuple						=			np.maximum(_lowercase,					1	) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		snake_case_ :Optional[int]						=			0.0  # do not predict special tokens
												snake_case_ :int						=			torch.from_numpy(_lowercase	)
						else:
												snake_case_ :List[str]						=			None

						snake_case_ :Optional[int]						=			LmSeqsDataset(params=_lowercase,					data=_lowercase	)
						logger.info("""Data loader created."""	)

						# STUDENT #
						logger.info(f"""Loading student config from {args.student_config}"""	)
						snake_case_ :List[Any]						=			student_config_class.from_pretrained(args.student_config	)
						snake_case_ :Union[str, Any]						=			True

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""	)
												snake_case_ :List[str]						=			student_model_class.from_pretrained(args.student_pretrained_weights,					config=_lowercase	)
						else:
												snake_case_ :Optional[int]						=			student_model_class(_lowercase	)

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

						# TEACHER #
						snake_case_ :Dict						=			teacher_model_class.from_pretrained(args.teacher_name,					output_hidden_states=_lowercase	)
						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(_lowercase,					_lowercase	)
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(_lowercase,					_lowercase	)

						# 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()
						snake_case_ :Optional[int]						=			Distiller(
						    params=_lowercase,					dataset=_lowercase,					token_probs=_lowercase,					student=_lowercase,					teacher=_lowercase	)
						distiller.train()
						logger.info("""Let's go get some drinks."""	)


if __name__ == "__main__":
						main()
