





"""simple docstring"""





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__					=   logging.getLogger()


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       argparse.ArgumentParser()
      parser.add_argument("""-f"""			)
      lowerCAmelCase  						=       parser.parse_args()
      return args.f


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      lowerCAmelCase  						=       {}
      lowerCAmelCase  						=       os.path.join(SCREAMING_SNAKE_CASE							,						"""all_results.json"""			)
      if os.path.exists(SCREAMING_SNAKE_CASE			):
            with open(SCREAMING_SNAKE_CASE							,						"""r"""			) as f:
                  lowerCAmelCase  						=       json.load(SCREAMING_SNAKE_CASE			)
      else:
            raise ValueError(F'can\'t find {path}'			)
      return results


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       torch.cuda.is_available() and torch_device == """cuda"""
      return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE__					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		lowercase					(					_UpperCAmelCase					):





      @classmethod
      def     _snake_case			(       cls )		->     List[str]:
            # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
            lowerCAmelCase  						=       tempfile.mkdtemp()
            lowerCAmelCase  						=       os.path.join(cls.tmpdir      ,						"""default_config.yml""" )
            write_basic_config(save_location=cls.configPath )
            lowerCAmelCase  						=       ["""accelerate""", """launch""", """--config_file""", cls.configPath]





      @classmethod
      def     _snake_case			(       cls )		->     List[Any]:
            shutil.rmtree(cls.tmpdir )





      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

            if is_cuda_and_apex_available():
                  testargs.append("""--fp16""" )

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertGreaterEqual(result["""eval_accuracy"""]      ,						0.75 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""epoch_0""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""glue_no_trainer""" ) ) )





      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

            if torch.cuda.device_count() > 1:
                  # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                  return

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertLess(result["""perplexity"""]      ,						100 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""epoch_0""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""clm_no_trainer""" ) ) )





      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertLess(result["""perplexity"""]      ,						42 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""epoch_0""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""mlm_no_trainer""" ) ) )





      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     Dict:
            # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
            lowerCAmelCase  						=       7 if get_gpu_count() > 1 else 2

            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertGreaterEqual(result["""eval_accuracy"""]      ,						0.75 )
            self.assertLess(result["""train_loss"""]      ,						0.5 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""epoch_0""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""ner_no_trainer""" ) ) )





      @unittest.skip(reason="""Fix me @muellerzr""" )
      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
            self.assertGreaterEqual(result["""eval_f1"""]      ,						28 )
            self.assertGreaterEqual(result["""eval_exact"""]      ,						28 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""epoch_0""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""qa_no_trainer""" ) ) )





      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertGreaterEqual(result["""eval_accuracy"""]      ,						0.8 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""swag_no_trainer""" ) ) )





      @slow
      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertGreaterEqual(result["""eval_rouge1"""]      ,						10 )
            self.assertGreaterEqual(result["""eval_rouge2"""]      ,						2 )
            self.assertGreaterEqual(result["""eval_rougeL"""]      ,						7 )
            self.assertGreaterEqual(result["""eval_rougeLsum"""]      ,						7 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""epoch_0""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""summarization_no_trainer""" ) ) )





      @slow
      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertGreaterEqual(result["""eval_bleu"""]      ,						30 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""epoch_0""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""translation_no_trainer""" ) ) )





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       logging.StreamHandler(sys.stdout )
            logger.addHandler(lowercase )

            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            self.assertGreaterEqual(result["""eval_overall_accuracy"""]      ,						0.10 )





      @mock.patch.dict(os.environ      ,						{"""WANDB_MODE""": """offline"""} )
      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.get_auto_remove_tmp_dir()
            lowerCAmelCase  						=       f'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

            if is_cuda_and_apex_available():
                  testargs.append("""--fp16""" )

            run_command(self._launch_args + testargs )
            lowerCAmelCase  						=       get_results(lowercase )
            # The base model scores a 25%
            self.assertGreaterEqual(result["""eval_accuracy"""]      ,						0.6 )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""step_1""" ) ) )
            self.assertTrue(os.path.exists(os.path.join(lowercase      ,						"""image_classification_no_trainer""" ) ) )










"""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


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







def   __lowerCAmelCase						(						lowercase  :  Any )	->						Union[str, 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						(						lowercase  :  int						,							lowercase  :  Dict )	->						Tuple:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case    :      List[str]      					=    False
    elif args.student_type == "gpt2":
        snake_case    :      Optional[int]      					=    False







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Optional[int] )	->						Optional[Any]:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case    :      Optional[Any]      					=    False







def   __lowerCAmelCase						(						)	->						int:


    """simple docstring"""





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

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

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

    snake_case    :      str      					=    LmSeqsDataset(params=lowercase						,							data=lowercase )
    logger.info("Data loader created." )

    # STUDENT #
    logger.info(F'Loading student config from {args.student_config}' )
    snake_case    :      str      					=    student_config_class.from_pretrained(args.student_config )
    snake_case    :      Optional[int]      					=    True

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

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

    # TEACHER #
    snake_case    :      List[Any]      					=    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[Any]      					=    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()





