



"""simple docstring"""
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__a      =				"\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

__a      =				"\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

__a      =				"\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION				)
class 				lowerCamelCase  (			datasets.Metric				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Dict						)   ->	MetricInfo:
											return datasets.MetricInfo(
											    description=_DESCRIPTION						,     citation=_CITATION						,     inputs_description=_KWARGS_DESCRIPTION						,     features=datasets.Features(
											        {
											            """predictions""": datasets.Sequence(datasets.Value("""string"""						,     id="""token"""						)						,     id="""sequence"""						),
											            """references""": datasets.Sequence(
											                datasets.Sequence(datasets.Value("""string"""						,     id="""token"""						)						,     id="""sequence"""						)						,     id="""references"""						),
											        }						)						,     )






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							List[List[List[str]]]						,     snake_case:							List[List[str]]						,     snake_case:							int = 1						,     snake_case:							int = 4						,     )   ->	Dict[str, float]:
											return {
											    "google_bleu": gleu_score.corpus_gleu(
											        list_of_references=snake_case						,     hypotheses=snake_case						,     min_len=snake_case						,     max_len=snake_case						)
											}





"""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()
