
"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_lowercase							      = [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 					_snake_case (						snake_case__      :   Tuple		,					snake_case__      :   Tuple		,					snake_case__      :   str		,					snake_case__      :   Dict		,					snake_case__      :   List[Any]   ):
	for attribute in key.split('.'   ):
		A        =      getattr(snake_case__		,					snake_case__   )

	if weight_type is not None:
		A        =      getattr(snake_case__		,					snake_case__   ).shape
	else:
		A        =      hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
	    F' {value.shape} for {full_name}'
	)

	if weight_type == "weight":
		A        =      value
	elif weight_type == "weight_g":
		A        =      value
	elif weight_type == "weight_v":
		A        =      value
	elif weight_type == "bias":
		A        =      value
	else:
		A        =      value

	logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'   )






def 					_snake_case (						snake_case__      :   Dict		,					snake_case__      :   List[str]   ):
	A        =      []
	A        =      fairseq_model.state_dict()

	A        =      hf_model.feature_extractor

	# if encoder has different dim to decoder -> use proj_weight
	A        =      None

	for name, value in fairseq_dict.items():
		A        =      False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					hf_model.config.feat_extract_norm == 'group'		,					)
			A        =      True
		elif name.split('.'   )[0] == "proj":
			A        =      fairseq_model.proj
			A        =      True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
					A        =      True
					if "*" in mapped_key:
						A        =      name.split(snake_case__   )[0].split('.'   )[-2]
						A        =      mapped_key.replace('*'		,					snake_case__   )
					if "weight_g" in name:
						A        =      'weight_g'
					elif "weight_v" in name:
						A        =      'weight_v'
					elif "bias" in name:
						A        =      'bias'
					elif "weight" in name:
						A        =      'weight'
					else:
						A        =      None
					set_recursively(snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__   )
				continue
		if not is_used:
			unused_weights.append(snake_case__   )

	logger.warning(F'Unused weights: {unused_weights}'   )

	return proj_weight






def 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   str		,					snake_case__      :   Dict		,					snake_case__      :   List[Any]		,					snake_case__      :   List[Any]   ):
	A        =      full_name.split('conv_layers.'   )[-1]
	A        =      name.split('.'   )
	A        =      int(items[0]   )
	A        =      int(items[1]   )

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
			)
			A        =      value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
			)
			A        =      value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
			    " found."
			)
			A        =      value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
			)
			A        =      value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
	else:
		unused_weights.append(snake_case__   )






def 					_snake_case (						snake_case__      :   Optional[int]   ):
	A      ,					A        =      emb.weight.shape
	A        =      nn.Linear(snake_case__		,					snake_case__		,					bias=snake_case__   )
	A        =      emb.weight.data
	return lin_layer






def 					_snake_case (						snake_case__      :   str   ):
	with open(snake_case__		,					'r'		,					encoding='utf-8'   ) as f:
		A        =      f.readlines()
		A        =      [line.split(' '   )[0] for line in lines]

	A        =      len(snake_case__   )

	A        =      {
	    '<s>': 0,
	    '<pad>': 1,
	    '</s>': 2,
	    '<unk>': 3,
	}

	vocab_dict.update(dict(zip(snake_case__		,					range(4		,					num_words + 4   )   )   )   )
	return vocab_dict






@torch.no_grad()
def 					_snake_case (						snake_case__      :   List[Any]		,					snake_case__      :   Optional[int]		,					snake_case__      :   Any		,					snake_case__      :   int		,					snake_case__      :   List[str]		,					snake_case__      :   List[Any]		,					snake_case__      :   Optional[Any]		,					):
	A        =      WavaVecaConfig.from_pretrained(snake_case__   )
	A        =      SpeechaTextaConfig.from_pretrained(
	    snake_case__		,					vocab_size=snake_case__		,					decoder_layers=snake_case__		,					do_stable_layer_norm=snake_case__   )

	A        =      WavaVecaFeatureExtractor(
	    feature_size=1		,					sampling_rate=1_6000		,					padding_value=0		,					do_normalize=snake_case__		,					return_attention_mask=snake_case__		,					)

	A      ,					A      ,					A        =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]		,					arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
	A        =      model[0].eval()

	# set weights for wav2vec2 encoder
	A        =      WavaVecaModel(snake_case__   )
	A        =      recursively_load_weights_wavaveca(model.encoder		,					snake_case__   )

	A        =      SpeechaTextaForCausalLM(snake_case__   )
	A      ,					A        =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,					strict=snake_case__   )

	# set output linear layer
	unexpected_keys.remove('embed_out'   )
	A        =      nn.Parameter(model.decoder.embed_out.detach()   )

	# layer norm is init to identity matrix so leaving it is fine
	logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'   )
	logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'   )

	A        =      SpeechEncoderDecoderModel(encoder=snake_case__		,					decoder=snake_case__   )
	A        =      False

	# add projection layer
	A        =      nn.Parameter(projection_layer.weight   )
	A        =      nn.Parameter(projection_layer.bias   )

	A        =      create_vocab_dict(snake_case__   )

	with open(os.path.join(snake_case__		,					'vocab.json'   )		,					'w'   ) as fp:
		json.dump(snake_case__		,					snake_case__   )

	A        =      SpeechaTextaTokenizer(os.path.join(snake_case__		,					'vocab.json'   )   )
	tokenizer.save_pretrained(snake_case__   )

	A        =      hf_wavavec.config.to_dict()
	A        =      tokenizer.pad_token_id
	A        =      tokenizer.bos_token_id
	A        =      tokenizer.eos_token_id
	A        =      'speech_to_text_2'
	A        =      'wav2vec2'

	A        =      SpeechEncoderDecoderConfig.from_dict(snake_case__   )

	hf_wavavec.save_pretrained(snake_case__   )
	feature_extractor.save_pretrained(snake_case__   )


if __name__ == "__main__":
		_lowercase							      = argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument(
		    '''--encoder_config_path''',
		    default='''facebook/wav2vec2-large-lv60''',
		    type=str,
		    help='''Path to hf encoder wav2vec2 checkpoint config''',
		)
		parser.add_argument(
		    '''--decoder_config_path''',
		    default='''facebook/s2t-small-mustc-en-fr-st''',
		    type=str,
		    help='''Path to hf decoder s2t checkpoint config''',
		)
		parser.add_argument('''--vocab_size''', default=1_02_24, type=int, help='''Vocab size of decoder''')
		parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

		_lowercase							      = parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    vocab_size=args.vocab_size,
		    num_decoder_layers=args.num_decoder_layers,
		)




"""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()
