


'''simple docstring'''



import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    Optional[Any]=None  ):




						'''simple docstring'''






						UpperCAmelCase         =			None
						if token is not None:
												UpperCAmelCase         =			{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

						UpperCAmelCase         =			f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						UpperCAmelCase         =			requests.get(A   ,     headers=A  ).json()
						UpperCAmelCase         =			{}

						try:
												job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}  )
												UpperCAmelCase         =			math.ceil((result['''total_count'''] - 1_00) / 1_00  )

												for i in range(A  ):
																		UpperCAmelCase         =			requests.get(url + f"""&page={i + 2}"""   ,     headers=A  ).json()
																		job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}  )

												return job_links
						except Exception:
												print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""  )

						return {}


def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    Optional[int]=None  ):




						'''simple docstring'''






						UpperCAmelCase         =			None
						if token is not None:
												UpperCAmelCase         =			{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

						UpperCAmelCase         =			f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
						UpperCAmelCase         =			requests.get(A   ,     headers=A  ).json()
						UpperCAmelCase         =			{}

						try:
												artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}  )
												UpperCAmelCase         =			math.ceil((result['''total_count'''] - 1_00) / 1_00  )

												for i in range(A  ):
																		UpperCAmelCase         =			requests.get(url + f"""&page={i + 2}"""   ,     headers=A  ).json()
																		artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}  )

												return artifacts
						except Exception:
												print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""  )

						return {}


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Any   ,     A     :    str   ,     A     :    Dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			None
						if token is not None:
												UpperCAmelCase         =			{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

						UpperCAmelCase         =			requests.get(A   ,     headers=A   ,     allow_redirects=A  )
						UpperCAmelCase         =			result.headers['''Location''']
						UpperCAmelCase         =			requests.get(A   ,     allow_redirects=A  )
						UpperCAmelCase         =			os.path.join(A   ,     f"""{artifact_name}.zip"""  )
						with open(A   ,     '''wb'''  ) as fp:
												fp.write(response.content  )


def    lowerCamelCase__   ( A     :    Dict   ,     A     :    List[str]=None  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]
						UpperCAmelCase         =			[]
						UpperCAmelCase         =			None

						with zipfile.ZipFile(A  ) as z:
												for filename in z.namelist():
																		if not os.path.isdir(A  ):
																								# read the file
																								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																														with z.open(A  ) as f:
																																				for line in f:
																																										UpperCAmelCase         =			line.decode('''UTF-8'''  ).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						UpperCAmelCase         =			line[: line.index(''': '''  )]
																																																						UpperCAmelCase         =			line[line.index(''': '''  ) + len(''': '''  ) :]
																																																						errors.append([error_line, error]  )
																																																except Exception:
																																																						# skip un-related lines
																																																						pass
																																										elif filename == "summary_short.txt" and line.startswith('''FAILED '''  ):
																																																# `test` is the test method that failed
																																																UpperCAmelCase         =			line[len('''FAILED '''  ) :]
																																																failed_tests.append(A  )
																																										elif filename == "job_name.txt":
																																																UpperCAmelCase         =			line

						if len(A  ) != len(A  ):
												raise ValueError(
												    f"""`errors` and `failed_tests` should have the same number of elements. Got {len(A  )} for `errors` """
												    f"""and {len(A  )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
												    ''' problem.'''  )

						UpperCAmelCase         =			None
						if job_name and job_links:
												UpperCAmelCase         =			job_links.get(A   ,     A  )

						# A list with elements of the form (line of error, error, failed test)
						UpperCAmelCase         =			[x + [y] + [job_link] for x, y in zip(A   ,     A  )]

						return result


def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    Optional[Any]=None  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]

						UpperCAmelCase         =			[os.path.join(A   ,     A  ) for p in os.listdir(A  ) if p.endswith('''.zip'''  )]
						for p in paths:
												errors.extend(get_errors_from_single_artifact(A   ,     job_links=A  )  )

						return errors


def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    Tuple=None  ):




						'''simple docstring'''






						UpperCAmelCase         =			Counter()
						counter.update([x[1] for x in logs]  )
						UpperCAmelCase         =			counter.most_common()
						UpperCAmelCase         =			{}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		UpperCAmelCase         =			{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

						UpperCAmelCase         =			dict(sorted(r.items()   ,     key=lambda A  : item[1]["count"]   ,     reverse=A  )  )
						return r


def    lowerCamelCase__   ( A     :    Optional[int]  ):




						'''simple docstring'''






						UpperCAmelCase         =			test.split('''::'''  )[0]
						if test.startswith('''tests/models/'''  ):
												UpperCAmelCase         =			test.split('''/'''  )[2]
						else:
												UpperCAmelCase         =			None

						return test


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    Union[str, Any]=None  ):




						'''simple docstring'''






						UpperCAmelCase         =			[(x[0], x[1], get_model(x[2]  )) for x in logs]
						UpperCAmelCase         =			[x for x in logs if x[2] is not None]
						UpperCAmelCase         =			{x[2] for x in logs}

						UpperCAmelCase         =			{}
						for test in tests:
												UpperCAmelCase         =			Counter()
												# count by errors in `test`
												counter.update([x[1] for x in logs if x[2] == test]  )
												UpperCAmelCase         =			counter.most_common()
												UpperCAmelCase         =			{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
												UpperCAmelCase         =			sum(error_counts.values()  )
												if n_errors > 0:
																		UpperCAmelCase         =			{'''count''': n_errors, '''errors''': error_counts}

						UpperCAmelCase         =			dict(sorted(r.items()   ,     key=lambda A  : item[1]["count"]   ,     reverse=A  )  )
						return r


def    lowerCamelCase__   ( A     :    Optional[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			'''| no. | error | status |'''
						UpperCAmelCase         =			'''|-:|:-|:-|'''
						UpperCAmelCase         =			[header, sep]
						for error in reduced_by_error:
												UpperCAmelCase         =			reduced_by_error[error]['''count''']
												UpperCAmelCase         =			f"""| {count} | {error[:1_00]} |  |"""
												lines.append(A  )

						return "\n".join(A  )


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			'''| model | no. of errors | major error | count |'''
						UpperCAmelCase         =			'''|-:|-:|-:|-:|'''
						UpperCAmelCase         =			[header, sep]
						for model in reduced_by_model:
												UpperCAmelCase         =			reduced_by_model[model]['''count''']
												UpperCAmelCase       ,       UpperCAmelCase         =			list(reduced_by_model[model]['''errors'''].items()  )[0]
												UpperCAmelCase         =			f"""| {model} | {count} | {error[:60]} | {_count} |"""
												lines.append(A  )

						return "\n".join(A  )


if __name__ == "__main__":
	_lowercase    :    List[str]			     =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
	parser.add_argument(
	    """--output_dir""",
	    type=str,
	    required=True,
	    help="""Where to store the downloaded artifacts and other result files.""",
	)
	parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
	_lowercase    :    Optional[int]			     =  parser.parse_args()

	os.makedirs(args.output_dir, exist_ok=True)

	_lowercase    :    Optional[Any]			     =  get_job_links(args.workflow_run_id, token=args.token)
	_lowercase    :    Any			     =  {}
	# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
	# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
	if _job_links:
		for k, v in _job_links.items():
			# This is how GitHub actions combine job names.
			if " / " in k:
				_lowercase    :    List[str]			     =  k.find(""" / """)
				_lowercase    :    str			     =  k[index + len(""" / """) :]
			_lowercase    :    Optional[Any]			     =  v
	with open(os.path.join(args.output_dir, """job_links.json"""), """w""", encoding="""UTF-8""") as fp:
		json.dump(job_links, fp, ensure_ascii=False, indent=4)

	_lowercase    :    List[Any]			     =  get_artifacts_links(args.workflow_run_id, token=args.token)
	with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
		json.dump(artifacts, fp, ensure_ascii=False, indent=4)

	for idx, (name, url) in enumerate(artifacts.items()):
		download_artifact(name, url, args.output_dir, args.token)
		# Be gentle to GitHub
		time.sleep(1)

	_lowercase    :    Dict			     =  get_all_errors(args.output_dir, job_links=job_links)

	# `e[1]` is the error
	_lowercase    :    List[Any]			     =  Counter()
	counter.update([e[1] for e in errors])

	# print the top 30 most common test errors
	_lowercase    :    Optional[int]			     =  counter.most_common(30)
	for item in most_common:
		print(item)

	with open(os.path.join(args.output_dir, """errors.json"""), """w""", encoding="""UTF-8""") as fp:
		json.dump(errors, fp, ensure_ascii=False, indent=4)

	_lowercase    :    Optional[int]			     =  reduce_by_error(errors)
	_lowercase    :    Optional[Any]			     =  reduce_by_model(errors)

	_lowercase    :    Tuple			     =  make_github_table(reduced_by_error)
	_lowercase    :    Union[str, Any]			     =  make_github_table_per_model(reduced_by_model)

	with open(os.path.join(args.output_dir, """reduced_by_error.txt"""), """w""", encoding="""UTF-8""") as fp:
		fp.write(sa)
	with open(os.path.join(args.output_dir, """reduced_by_model.txt"""), """w""", encoding="""UTF-8""") as fp:
		fp.write(sa)









'''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()





