

'''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


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






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]						)				->  List[Any]:
						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			(   __magic_name__						:     List[Any]   , __magic_name__						:     Optional[int]						)				->  Tuple:
						if args.student_type == "roberta":
												lowercase :							List[Any]    		=False
						elif args.student_type == "gpt2":
												lowercase :							Dict    		=False






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]   , __magic_name__						:     int						)				->  str:
						if args.student_type == "roberta":
												lowercase :							str    		=False






def 				_lowerCAmelCase			(   )				->  List[Any]:
						lowercase :							int    		=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=__magic_name__   , required=__magic_name__   , help='''The output directory (log, checkpoints, parameters, etc.)'''						)
						parser.add_argument(
						    '''--data_file'''   , type=__magic_name__   , required=__magic_name__   , help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''   , )

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

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

						parser.add_argument('''--temperature'''   , default=2.0   , type=__magic_name__   , help='''Temperature for the softmax temperature.'''						)
						parser.add_argument(
						    '''--alpha_ce'''   , default=0.5   , type=__magic_name__   , help='''Linear weight for the distillation loss. Must be >=0.'''						)
						parser.add_argument(
						    '''--alpha_mlm'''   , default=0.0   , type=__magic_name__   , 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=__magic_name__   , help='''Linear weight for the CLM loss. Must be >=0.'''						)
						parser.add_argument('''--alpha_mse'''   , default=0.0   , type=__magic_name__   , help='''Linear weight of the MSE loss. Must be >=0.'''						)
						parser.add_argument(
						    '''--alpha_cos'''   , default=0.0   , type=__magic_name__   , 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=0.1_5   , type=__magic_name__   , help='''Proportion of tokens for which we need to make a prediction.'''   , )
						parser.add_argument('''--word_mask'''   , default=0.8   , type=__magic_name__   , help='''Proportion of tokens to mask out.'''						)
						parser.add_argument('''--word_keep'''   , default=0.1   , type=__magic_name__   , help='''Proportion of tokens to keep.'''						)
						parser.add_argument('''--word_rand'''   , default=0.1   , type=__magic_name__   , help='''Proportion of tokens to randomly replace.'''						)
						parser.add_argument(
						    '''--mlm_smoothing'''   , default=0.7   , type=__magic_name__   , help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''   , )
						parser.add_argument('''--token_counts'''   , type=__magic_name__   , 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=__magic_name__   , default=3   , help='''Number of pass on the whole dataset.'''						)
						parser.add_argument('''--batch_size'''   , type=__magic_name__   , 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=__magic_name__   , default=50   , help='''Gradient accumulation for larger training batches.'''   , )
						parser.add_argument('''--warmup_prop'''   , default=0.0_5   , type=__magic_name__   , help='''Linear warmup proportion.'''						)
						parser.add_argument('''--weight_decay'''   , default=0.0   , type=__magic_name__   , help='''Weight decay if we apply some.'''						)
						parser.add_argument('''--learning_rate'''   , default=5E-4   , type=__magic_name__   , help='''The initial learning rate for Adam.'''						)
						parser.add_argument('''--adam_epsilon'''   , default=1E-6   , type=__magic_name__   , help='''Epsilon for Adam optimizer.'''						)
						parser.add_argument('''--max_grad_norm'''   , default=5.0   , type=__magic_name__   , help='''Max gradient norm.'''						)
						parser.add_argument('''--initializer_range'''   , default=0.0_2   , type=__magic_name__   , 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=__magic_name__   , 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=__magic_name__   , default=1   , help='''Number of GPUs in the node.'''						)
						parser.add_argument('''--local_rank'''   , type=__magic_name__   , default=-1   , help='''Distributed training - Local rank'''						)
						parser.add_argument('''--seed'''   , type=__magic_name__   , default=56   , help='''Random seed'''						)

						parser.add_argument('''--log_interval'''   , type=__magic_name__   , default=500   , help='''Tensorboard logging interval.'''						)
						parser.add_argument('''--checkpoint_interval'''   , type=__magic_name__   , default=4000   , help='''Checkpoint interval.'''						)
						lowercase :							Dict    		=parser.parse_args()
						sanity_checks(__magic_name__						)

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

						lowercase		,	lowercase		,	lowercase :							Optional[int]    		=MODEL_CLASSES[args.student_type]
						lowercase		,	lowercase		,	lowercase :							str    		=MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						lowercase :							Optional[Any]    		=teacher_tokenizer_class.from_pretrained(args.teacher_name						)
						lowercase :							Tuple    		={}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												lowercase :							List[str]    		=tokenizer.all_special_tokens.index(__magic_name__						)
												lowercase :							int    		=tokenizer.all_special_ids[idx]
						logger.info(f'''Special tokens {special_tok_ids}'''						)
						lowercase :							Optional[int]    		=special_tok_ids
						lowercase :							str    		=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:
												lowercase :							List[str]    		=pickle.load(__magic_name__						)

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

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

						lowercase :							int    		=LmSeqsDataset(params=__magic_name__   , data=__magic_name__						)
						logger.info('''Data loader created.'''						)

						# STUDENT #
						logger.info(f'''Loading student config from {args.student_config}'''						)
						lowercase :							Optional[int]    		=student_config_class.from_pretrained(args.student_config						)
						lowercase :							List[Any]    		=True

						if args.student_pretrained_weights is not None:
												logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''						)
												lowercase :							Optional[int]    		=student_model_class.from_pretrained(args.student_pretrained_weights   , config=__magic_name__						)
						else:
												lowercase :							int    		=student_model_class(__magic_name__						)

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

						# TEACHER #
						lowercase :							List[Any]    		=teacher_model_class.from_pretrained(args.teacher_name   , output_hidden_states=__magic_name__						)
						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(__magic_name__   , __magic_name__						)
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(__magic_name__   , __magic_name__						)

						# 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()
						lowercase :							Optional[Any]    		=Distiller(
						    params=__magic_name__   , dataset=__magic_name__   , token_probs=__magic_name__   , student=__magic_name__   , teacher=__magic_name__						)
						distiller.train()
						logger.info('''Let\'s go get some drinks.'''						)


if __name__ == "__main__":
							main()












import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "OwlViTImageProcessor"
							lowerCamelCase_							     =       ("CLIPTokenizer", "CLIPTokenizerFast")


							def __init__(				self     :Optional[Any]				,					__A     :int=None				,					__A     :Optional[int]=None				,					**__A     :str		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""				,					__A				,					)
											SCREAMING_SNAKE_CASE__								=   kwargs.pop("""feature_extractor"""		)

									SCREAMING_SNAKE_CASE__								=   image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""		)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""		)

									super().__init__(__A				,					__A		)


							def __call__(				self     :str				,					__A     :Dict=None				,					__A     :List[str]=None				,					__A     :str=None				,					__A     :Optional[int]="max_length"				,					__A     :Tuple="np"				,					**__A     :int		) -> Tuple:



									"""simple docstring"""



									if text is None and query_images is None and images is None:
											raise ValueError(
											    """You have to specify at least one text or query image or image. All three cannot be none."""		)

									if text is not None:
											if isinstance(__A				,					__A		) or (isinstance(__A				,					__A		) and not isinstance(text[0]				,					__A		)):
													SCREAMING_SNAKE_CASE__								=   [self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)]

											elif isinstance(__A				,					__A		) and isinstance(text[0]				,					__A		):
													SCREAMING_SNAKE_CASE__								=   []

													# Maximum number of queries across batch
													SCREAMING_SNAKE_CASE__								=   max([len(__A		) for t in text]		)

													# Pad all batch samples to max number of text queries
													for t in text:
															if len(__A		) != max_num_queries:
																	SCREAMING_SNAKE_CASE__								=   t + [""" """] * (max_num_queries - len(__A		))

															SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)
															encodings.append(__A		)
											else:
													raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""		)

											if return_tensors == "np":
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "pt" and is_torch_available():
													import torch

													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""input_ids"""] for encoding in encodings]				,					dim=0		)
													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""attention_mask"""] for encoding in encodings]				,					dim=0		)

											elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											else:
													raise ValueError("""Target return tensor type could not be returned"""		)

											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   input_ids
											SCREAMING_SNAKE_CASE__								=   attention_mask

									if query_images is not None:
											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   self.image_processor(
											    __A				,					return_tensors=__A				,					**__A		).pixel_values
											SCREAMING_SNAKE_CASE__								=   query_pixel_values

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif query_images is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None or query_images is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :List[Any]				,					*__A     :Dict				,					**__A     :Dict		) -> Optional[int]:



									"""simple docstring"""



									return self.image_processor.post_process(*__A				,					**__A		)


							def   _snake_case	(				self     :Optional[int]				,					*__A     :Dict				,					**__A     :List[str]		) -> Optional[Any]:



									"""simple docstring"""



									return self.image_processor.post_process_object_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									return self.image_processor.post_process_image_guided_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :List[str]				,					**__A     :List[str]		) -> int:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :Dict				,					**__A     :List[str]		) -> str:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


							@property
							def   _snake_case	(				self     :List[Any]		) -> Optional[int]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,					__A				,					)
									return self.image_processor_class


							@property
							def   _snake_case	(				self     :Any		) -> Optional[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,					__A				,					)
									return self.image_processor