



"""simple docstring"""
import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     BlenderbotSmallTokenizer
					_A		:			List[Any]						=     False






					def    lowerCAmelCase_				(       self:							Any						)   ->	Dict:
											super().setUp()

											snake_case_ :Dict						=			["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
											snake_case_ :Dict						=			dict(zip(snake_case						,     range(len(snake_case						)						)						)						)

											snake_case_ :Union[str, Any]						=			["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
											snake_case_ :Dict						=			{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

											snake_case_ :Any						=			os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]						)
											snake_case_ :str						=			os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""merges_file"""]						)
											with open(self.vocab_file						,     """w"""						,     encoding="""utf-8"""						) as fp:
																	fp.write(json.dumps(snake_case						) + """\n"""						)
											with open(self.merges_file						,     """w"""						,     encoding="""utf-8"""						) as fp:
																	fp.write("""\n""".join(snake_case						)						)






					def    lowerCAmelCase_				(       self:							List[Any]						,     **snake_case:							Optional[int]						)   ->	str:
											kwargs.update(self.special_tokens_map						)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Optional[Any]						)   ->	Any:
											snake_case_ :Any						=			"""adapt act apte"""
											snake_case_ :int						=			"""adapt act apte"""
											return input_text, output_text






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											snake_case_ :Any						=			BlenderbotSmallTokenizer(self.vocab_file						,     self.merges_file						,     **self.special_tokens_map						)
											snake_case_ :Dict						=			"""adapt act apte"""
											snake_case_ :Optional[Any]						=			["""adapt""", """act""", """ap@@""", """te"""]
											snake_case_ :List[str]						=			tokenizer.tokenize(snake_case						)
											self.assertListEqual(snake_case						,     snake_case						)

											snake_case_ :int						=			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											snake_case_ :str						=			[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case						)						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Union[str, Any]:
											snake_case_ :List[Any]						=			BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""						)
											assert tok("""sam"""						).input_ids == [1_384]
											snake_case_ :str						=			"""I am a small frog."""
											snake_case_ :Dict						=			tok([src_text]						,     padding=snake_case						,     truncation=snake_case						)["""input_ids"""]
											snake_case_ :Optional[Any]						=			tok.batch_decode(snake_case						,     skip_special_tokens=snake_case						,     clean_up_tokenization_spaces=snake_case						)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."






					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :Optional[int]						=			BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""						)
											snake_case_ :str						=			"""I am a small frog ."""
											snake_case_ :Dict						=			"""."""
											snake_case_ :int						=			tok(snake_case						)["""input_ids"""]
											snake_case_ :List[Any]						=			tok(snake_case						)["""input_ids"""]

											assert encoded[-1] == encoded_dot[0]





"""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()
