


'''simple docstring'''



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class  UpperCamelCase__(     nn.Module ):
				__magic_name__       :   int
				__magic_name__       :   int
				__magic_name__       :   float							= 0.0
				__magic_name__       :   int							= 1
				__magic_name__       :   int							= 1
				__magic_name__       :   bool							= True
				__magic_name__       :   bool							= False
				__magic_name__       :   bool							= False
				__magic_name__       :   bool							= False
				__magic_name__       :   jnp.dtype							= jnp.floataa




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

										"""simple docstring"""




										UpperCAmelCase         =			[]
										UpperCAmelCase         =			[]

										for i in range(self.num_layers   ):
																UpperCAmelCase         =			self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase         =			FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase   ,					out_channels=self.out_channels   ,					dropout_prob=self.dropout   ,					dtype=self.dtype   ,					)
																resnets.append(lowerCAmelCase   )

																UpperCAmelCase         =			FlaxTransformeraDModel(
																    in_channels=self.out_channels   ,					n_heads=self.num_attention_heads   ,					d_head=self.out_channels // self.num_attention_heads   ,					depth=1   ,					use_linear_projection=self.use_linear_projection   ,					only_cross_attention=self.only_cross_attention   ,					use_memory_efficient_attention=self.use_memory_efficient_attention   ,					dtype=self.dtype   ,					)
																attentions.append(lowerCAmelCase   )

										UpperCAmelCase         =			resnets
										UpperCAmelCase         =			attentions

										if self.add_downsample:
																UpperCAmelCase         =			FlaxDownsampleaD(self.out_channels   ,					dtype=self.dtype   )





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

										"""simple docstring"""




										UpperCAmelCase         =			()

										for resnet, attn in zip(self.resnets   ,					self.attentions   ):
																UpperCAmelCase         =			resnet(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )
																UpperCAmelCase         =			attn(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase         =			self.downsamplers_a(lowerCAmelCase   )
																output_states += (hidden_states,)

										return hidden_states, output_states







class  UpperCamelCase__(     nn.Module ):
				__magic_name__       :   int
				__magic_name__       :   int
				__magic_name__       :   float							= 0.0
				__magic_name__       :   int							= 1
				__magic_name__       :   bool							= True
				__magic_name__       :   jnp.dtype							= jnp.floataa




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

										"""simple docstring"""




										UpperCAmelCase         =			[]

										for i in range(self.num_layers   ):
																UpperCAmelCase         =			self.in_channels if i == 0 else self.out_channels

																UpperCAmelCase         =			FlaxResnetBlockaD(
																    in_channels=lowerCAmelCase   ,					out_channels=self.out_channels   ,					dropout_prob=self.dropout   ,					dtype=self.dtype   ,					)
																resnets.append(lowerCAmelCase   )
										UpperCAmelCase         =			resnets

										if self.add_downsample:
																UpperCAmelCase         =			FlaxDownsampleaD(self.out_channels   ,					dtype=self.dtype   )





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

										"""simple docstring"""




										UpperCAmelCase         =			()

										for resnet in self.resnets:
																UpperCAmelCase         =			resnet(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )
																output_states += (hidden_states,)

										if self.add_downsample:
																UpperCAmelCase         =			self.downsamplers_a(lowerCAmelCase   )
																output_states += (hidden_states,)

										return hidden_states, output_states







class  UpperCamelCase__(     nn.Module ):
				__magic_name__       :   int
				__magic_name__       :   int
				__magic_name__       :   int
				__magic_name__       :   float							= 0.0
				__magic_name__       :   int							= 1
				__magic_name__       :   int							= 1
				__magic_name__       :   bool							= True
				__magic_name__       :   bool							= False
				__magic_name__       :   bool							= False
				__magic_name__       :   bool							= False
				__magic_name__       :   jnp.dtype							= jnp.floataa




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

										"""simple docstring"""




										UpperCAmelCase         =			[]
										UpperCAmelCase         =			[]

										for i in range(self.num_layers   ):
																UpperCAmelCase         =			self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase         =			self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase         =			FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels   ,					out_channels=self.out_channels   ,					dropout_prob=self.dropout   ,					dtype=self.dtype   ,					)
																resnets.append(lowerCAmelCase   )

																UpperCAmelCase         =			FlaxTransformeraDModel(
																    in_channels=self.out_channels   ,					n_heads=self.num_attention_heads   ,					d_head=self.out_channels // self.num_attention_heads   ,					depth=1   ,					use_linear_projection=self.use_linear_projection   ,					only_cross_attention=self.only_cross_attention   ,					use_memory_efficient_attention=self.use_memory_efficient_attention   ,					dtype=self.dtype   ,					)
																attentions.append(lowerCAmelCase   )

										UpperCAmelCase         =			resnets
										UpperCAmelCase         =			attentions

										if self.add_upsample:
																UpperCAmelCase         =			FlaxUpsampleaD(self.out_channels   ,					dtype=self.dtype   )





				def __call__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Any=True   )->  Tuple:

										"""simple docstring"""




										for resnet, attn in zip(self.resnets   ,					self.attentions   ):
																# pop res hidden states
																UpperCAmelCase         =			res_hidden_states_tuple[-1]
																UpperCAmelCase         =			res_hidden_states_tuple[:-1]
																UpperCAmelCase         =			jnp.concatenate((hidden_states, res_hidden_states)   ,					axis=-1   )

																UpperCAmelCase         =			resnet(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )
																UpperCAmelCase         =			attn(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )

										if self.add_upsample:
																UpperCAmelCase         =			self.upsamplers_a(lowerCAmelCase   )

										return hidden_states







class  UpperCamelCase__(     nn.Module ):
				__magic_name__       :   int
				__magic_name__       :   int
				__magic_name__       :   int
				__magic_name__       :   float							= 0.0
				__magic_name__       :   int							= 1
				__magic_name__       :   bool							= True
				__magic_name__       :   jnp.dtype							= jnp.floataa




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

										"""simple docstring"""




										UpperCAmelCase         =			[]

										for i in range(self.num_layers   ):
																UpperCAmelCase         =			self.in_channels if (i == self.num_layers - 1) else self.out_channels
																UpperCAmelCase         =			self.prev_output_channel if i == 0 else self.out_channels

																UpperCAmelCase         =			FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels   ,					out_channels=self.out_channels   ,					dropout_prob=self.dropout   ,					dtype=self.dtype   ,					)
																resnets.append(lowerCAmelCase   )

										UpperCAmelCase         =			resnets

										if self.add_upsample:
																UpperCAmelCase         =			FlaxUpsampleaD(self.out_channels   ,					dtype=self.dtype   )





				def __call__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Tuple=True   )->  List[Any]:

										"""simple docstring"""




										for resnet in self.resnets:
																# pop res hidden states
																UpperCAmelCase         =			res_hidden_states_tuple[-1]
																UpperCAmelCase         =			res_hidden_states_tuple[:-1]
																UpperCAmelCase         =			jnp.concatenate((hidden_states, res_hidden_states)   ,					axis=-1   )

																UpperCAmelCase         =			resnet(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )

										if self.add_upsample:
																UpperCAmelCase         =			self.upsamplers_a(lowerCAmelCase   )

										return hidden_states







class  UpperCamelCase__(     nn.Module ):
				__magic_name__       :   int
				__magic_name__       :   float							= 0.0
				__magic_name__       :   int							= 1
				__magic_name__       :   int							= 1
				__magic_name__       :   bool							= False
				__magic_name__       :   bool							= False
				__magic_name__       :   jnp.dtype							= jnp.floataa




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

										"""simple docstring"""




										UpperCAmelCase         =			[
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels   ,					out_channels=self.in_channels   ,					dropout_prob=self.dropout   ,					dtype=self.dtype   ,					)
										]

										UpperCAmelCase         =			[]

										for _ in range(self.num_layers   ):
																UpperCAmelCase         =			FlaxTransformeraDModel(
																    in_channels=self.in_channels   ,					n_heads=self.num_attention_heads   ,					d_head=self.in_channels // self.num_attention_heads   ,					depth=1   ,					use_linear_projection=self.use_linear_projection   ,					use_memory_efficient_attention=self.use_memory_efficient_attention   ,					dtype=self.dtype   ,					)
																attentions.append(lowerCAmelCase   )

																UpperCAmelCase         =			FlaxResnetBlockaD(
																    in_channels=self.in_channels   ,					out_channels=self.in_channels   ,					dropout_prob=self.dropout   ,					dtype=self.dtype   ,					)
																resnets.append(lowerCAmelCase   )

										UpperCAmelCase         =			resnets
										UpperCAmelCase         =			attentions





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

										"""simple docstring"""




										UpperCAmelCase         =			self.resnets[0](lowerCAmelCase   ,					lowerCAmelCase   )
										for attn, resnet in zip(self.attentions   ,					self.resnets[1:]   ):
																UpperCAmelCase         =			attn(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )
																UpperCAmelCase         =			resnet(lowerCAmelCase   ,					lowerCAmelCase   ,					deterministic=lowerCAmelCase   )

										return hidden_states









'''simple docstring'''



import argparse
import json
import os
import pickle
import shutil

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

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


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


def    lowerCamelCase__   ( A     :    Optional[Any]  ):




						'''simple docstring'''






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

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

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

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


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




						'''simple docstring'''






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


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




						'''simple docstring'''






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


def    lowerCamelCase__   ( ):




						'''simple docstring'''






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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
	main()





