



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 			A_      (	__a     :				Tuple		):

				"""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_      (	__a     :				Optional[Any] ,		__a     :				int		):

				"""simple docstring"""
				if args.student_type == "roberta":
								a__				      =		False
				elif args.student_type == "gpt2":
								a__				      =		False

def 			A_      (	__a     :				Tuple ,		__a     :				List[Any]		):

				"""simple docstring"""
				if args.student_type == "roberta":
								a__				      =		False

def 			A_      (	):

				"""simple docstring"""
				a__				      =		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=0.1_5 ,		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=0.0_5 ,		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=0.0_2 ,		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=500 ,		help="""Tensorboard logging interval."""		)
				parser.add_argument("""--checkpoint_interval""" ,		type=__a ,		default=4_000 ,		help="""Checkpoint interval."""		)
				a__				      =		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		)

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

				# TOKENIZER #
				a__				      =		teacher_tokenizer_class.from_pretrained(args.teacher_name		)
				a__				      =		{}
				for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
								a__				      =		tokenizer.all_special_tokens.index(__a		)
								a__				      =		tokenizer.all_special_ids[idx]
				logger.info(F'''Special tokens {special_tok_ids}'''		)
				a__				      =		special_tok_ids
				a__				      =		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:
								a__				      =		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:
												a__				      =		pickle.load(__a		)

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

				a__				      =		LmSeqsDataset(params=__a ,		data=__a		)
				logger.info("""Data loader created."""		)

				# STUDENT #
				logger.info(F'''Loading student config from {args.student_config}'''		)
				a__				      =		student_config_class.from_pretrained(args.student_config		)
				a__				      =		True

				if args.student_pretrained_weights is not None:
								logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''		)
								a__				      =		student_model_class.from_pretrained(args.student_pretrained_weights ,		config=__a		)
				else:
								a__				      =		student_model_class(__a		)

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

				# TEACHER #
				a__				      =		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()
				a__				      =		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()







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
	from .features import FeatureType


UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					False, False, False

@dataclass
class  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      Optional[int]         =					None
							UpperCamelCase__				:      bool         =					True
							UpperCamelCase__				:      bool         =					True
							UpperCamelCase__				:      Optional[str]         =					None
							# Automatically constructed
							UpperCamelCase__				:      ClassVar[str]         =					"dict"
							UpperCamelCase__				:      ClassVar[Any]         =					pa.struct({"""bytes""": pa.binary(), """path""": pa.string()})
							UpperCamelCase__				:      str         =					field(default="""Audio"""			,init=SCREAMING_SNAKE_CASE			,repr=SCREAMING_SNAKE_CASE)


							def __call__(							self		):
											return self.pa_type


							def _a      (							self  ,  a_		):
											try:
															import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
											except ImportError as err:
															raise ImportError("""To support encoding audio data, please install 'soundfile'."""		) from err
											if isinstance(a_  ,  a_		):
															return {"bytes": None, "path": value}
											elif isinstance(a_  ,  a_		):
															return {"bytes": value, "path": None}
											elif "array" in value:
															# convert the audio array to wav bytes
															a__				      =		BytesIO()
															sf.write(a_  ,  value["""array"""]  ,  value["""sampling_rate"""]  ,  format="""wav"""		)
															return {"bytes": buffer.getvalue(), "path": None}
											elif value.get("""path"""		) is not None and os.path.isfile(value["""path"""]		):
															# we set "bytes": None to not duplicate the data if they're already available locally
															if value["path"].endswith("""pcm"""		):
																			# "PCM" only has raw audio bytes
																			if value.get("""sampling_rate"""		) is None:
																							# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																							raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object"""		)
																			if value.get("""bytes"""		):
																							# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																							a__				      =		np.frombuffer(value["""bytes"""]  ,  dtype=np.intaa		).astype(np.floataa		) / 32_767
																			else:
																							a__				      =		np.memmap(value["""path"""]  ,  dtype="""h"""  ,  mode="""r"""		).astype(np.floataa		) / 32_767

																			a__				      =		BytesIO(bytes()		)
																			sf.write(a_  ,  a_  ,  value["""sampling_rate"""]  ,  format="""wav"""		)
																			return {"bytes": buffer.getvalue(), "path": None}
															else:
																			return {"bytes": None, "path": value.get("""path"""		)}
											elif value.get("""bytes"""		) is not None or value.get("""path"""		) is not None:
															# store the audio bytes, and path is used to infer the audio format using the file extension
															return {"bytes": value.get("""bytes"""		), "path": value.get("""path"""		)}
											else:
															raise ValueError(
															    F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''		)


							def _a      (							self  ,  a_  ,  a_ = None		):
											if not self.decode:
															raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""		)

											a__      ,			a__				      =		(value["""path"""], BytesIO(value["""bytes"""]		)) if value["""bytes"""] is not None else (value["""path"""], None)
											if path is None and file is None:
															raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''		)

											try:
															import librosa
															import soundfile as sf
											except ImportError as err:
															raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'."""		) from err

											a__				      =		xsplitext(a_		)[1][1:].lower() if path is not None else None
											if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
															raise RuntimeError(
															    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
															    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """		)
											elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
															raise RuntimeError(
															    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
															    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """		)

											if file is None:
															a__				      =		token_per_repo_id or {}
															a__				      =		path.split("""::"""		)[-1]
															try:
																			a__				      =		string_to_dict(a_  ,  config.HUB_DATASETS_URL		)["""repo_id"""]
																			a__				      =		token_per_repo_id[repo_id]
															except (ValueError, KeyError):
																			a__				      =		None

															with xopen(a_  ,  """rb"""  ,  use_auth_token=a_		) as f:
																			a__      ,			a__				      =		sf.read(a_		)

											else:
															a__      ,			a__				      =		sf.read(a_		)

											a__				      =		array.T
											if self.mono:
															a__				      =		librosa.to_mono(a_		)
											if self.sampling_rate and self.sampling_rate != sampling_rate:
															a__				      =		librosa.resample(a_  ,  orig_sr=a_  ,  target_sr=self.sampling_rate		)
															a__				      =		self.sampling_rate

											return {"path": path, "array": array, "sampling_rate": sampling_rate}


							def _a      (							self		):
											from .features import Value

											if self.decode:
															raise ValueError("""Cannot flatten a decoded Audio feature."""		)
											return {
											    "bytes": Value("""binary"""		),
											    "path": Value("""string"""		),
											}


							def _a      (							self  ,  a_		):
											if pa.types.is_string(storage.type		):
															a__				      =		pa.array([None] * len(a_		)  ,  type=pa.binary()		)
															a__				      =		pa.StructArray.from_arrays([bytes_array, storage]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											elif pa.types.is_binary(storage.type		):
															a__				      =		pa.array([None] * len(a_		)  ,  type=pa.string()		)
															a__				      =		pa.StructArray.from_arrays([storage, path_array]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											elif pa.types.is_struct(storage.type		) and storage.type.get_all_field_indices("""array"""		):
															a__				      =		pa.array([Audio().encode_example(a_		) if x is not None else None for x in storage.to_pylist()]		)
											elif pa.types.is_struct(storage.type		):
															if storage.type.get_field_index("""bytes"""		) >= 0:
																			a__				      =		storage.field("""bytes"""		)
															else:
																			a__				      =		pa.array([None] * len(a_		)  ,  type=pa.binary()		)
															if storage.type.get_field_index("""path"""		) >= 0:
																			a__				      =		storage.field("""path"""		)
															else:
																			a__				      =		pa.array([None] * len(a_		)  ,  type=pa.string()		)
															a__				      =		pa.StructArray.from_arrays([bytes_array, path_array]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											return array_cast(a_  ,  self.pa_type		)







							def _a      (							self  ,  a_		):

											@no_op_if_value_is_null
											def path_to_bytes(a_		):
															with xopen(a_  ,  """rb"""		) as f:
																			a__				      =		f.read()
															return bytes_

											a__				      =		pa.array(
											    [
											        (path_to_bytes(x["""path"""]		) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
											        for x in storage.to_pylist()
											    ]  ,  type=pa.binary()  ,  )
											a__				      =		pa.array(
											    [os.path.basename(a_		) if path is not None else None for path in storage.field("""path"""		).to_pylist()]  ,  type=pa.string()  ,  )
											a__				      =		pa.StructArray.from_arrays([bytes_array, path_array]  ,  ["""bytes""", """path"""]  ,  mask=bytes_array.is_null()		)
											return array_cast(a_  ,  self.pa_type		)

