


"""simple docstring"""



import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       int							  =  GPTaTokenizer
   __UpperCAmelCase :       Union[str, Any]							  =  GPTaTokenizerFast
   __UpperCAmelCase :       Optional[Any]							  =  True
   __UpperCAmelCase :       int							  =  {'''add_prefix_space''': True}
   __UpperCAmelCase :       Union[str, Any]							  =  False







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       snake_case    :      List[str]      					=    [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
           "<|endoftext|>",
       ]
       snake_case    :      Any      					=    dict(zip(UpperCamelCase__						,	range(len(UpperCamelCase__						)						)						)						)
       snake_case    :      Any      					=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       snake_case    :      List[Any]      					=    {"unk_token": "<unk>"}

       snake_case    :      Dict      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]						)
       snake_case    :      int      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["merges_file"]						)
       with open(self.vocab_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)
       with open(self.merges_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write("\n".join(UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       kwargs.update(self.special_tokens_map						)
       return GPTaTokenizer.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Dict:

       '''simple docstring'''




       kwargs.update(self.special_tokens_map						)
       return GPTaTokenizerFast.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Dict:

       '''simple docstring'''




       snake_case    :      List[str]      					=    "lower newer"
       snake_case    :      Union[str, Any]      					=    "lower newer"
       return input_text, output_text







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    GPTaTokenizer(self.vocab_file						,	self.merges_file						,	**self.special_tokens_map						)
       snake_case    :      List[str]      					=    "lower newer"
       snake_case    :      Optional[int]      					=    ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
       snake_case    :      Any      					=    tokenizer.tokenize(UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)

       snake_case    :      Tuple      					=    tokens + [tokenizer.unk_token]
       snake_case    :      Optional[int]      					=    [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__						)						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       if not self.test_rust_tokenizer:
           return

       snake_case    :      Union[str, Any]      					=    self.get_tokenizer()
       snake_case    :      int      					=    self.get_rust_tokenizer(add_prefix_space=UpperCamelCase__						)

       snake_case    :      Union[str, Any]      					=    "lower newer"

       # Testing tokenization
       snake_case    :      Union[str, Any]      					=    tokenizer.tokenize(UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)
       snake_case    :      Dict      					=    rust_tokenizer.tokenize(UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)

       # Testing conversion to ids without special tokens
       snake_case    :      Any      					=    tokenizer.encode(UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)
       snake_case    :      str      					=    rust_tokenizer.encode(UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)

       # Testing conversion to ids with special tokens
       snake_case    :      Optional[int]      					=    self.get_rust_tokenizer(add_prefix_space=UpperCamelCase__						)
       snake_case    :      Any      					=    tokenizer.encode(UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)
       snake_case    :      List[str]      					=    rust_tokenizer.encode(UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)

       # Testing the unknown token
       snake_case    :      Dict      					=    tokens + [rust_tokenizer.unk_token]
       snake_case    :      Dict      					=    [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCamelCase__						)						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       pass







   def  lowerCamelCase		(	self						,	UpperCamelCase__=15						)	->		Optional[Any]:

       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
               snake_case    :      str      					=    self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,	**UpperCamelCase__						)

               # Simple input
               snake_case    :      str      					=    "This is a simple input"
               snake_case    :      Optional[Any]      					=    ["This is a simple input 1", "This is a simple input 2"]
               snake_case    :      List[Any]      					=    ("This is a simple input", "This is a pair")
               snake_case    :      List[Any]      					=    [
                   ("This is a simple input 1", "This is a simple input 2"),
                   ("This is a simple pair 1", "This is a simple pair 2"),
               ]

               # Simple input tests
               self.assertRaises(UpperCamelCase__						,	tokenizer_r.encode						,	UpperCamelCase__						,	max_length=UpperCamelCase__						,	padding="max_length"						)

               # Simple input
               self.assertRaises(UpperCamelCase__						,	tokenizer_r.encode_plus						,	UpperCamelCase__						,	max_length=UpperCamelCase__						,	padding="max_length"						)

               # Simple input
               self.assertRaises(
                   UpperCamelCase__						,	tokenizer_r.batch_encode_plus						,	UpperCamelCase__						,	max_length=UpperCamelCase__						,	padding="max_length"						,	)

               # Pair input
               self.assertRaises(UpperCamelCase__						,	tokenizer_r.encode						,	UpperCamelCase__						,	max_length=UpperCamelCase__						,	padding="max_length"						)

               # Pair input
               self.assertRaises(UpperCamelCase__						,	tokenizer_r.encode_plus						,	UpperCamelCase__						,	max_length=UpperCamelCase__						,	padding="max_length"						)

               # Pair input
               self.assertRaises(
                   UpperCamelCase__						,	tokenizer_r.batch_encode_plus						,	UpperCamelCase__						,	max_length=UpperCamelCase__						,	padding="max_length"						,	)







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

       '''simple docstring'''




       snake_case    :      str      					=    GPTaTokenizer.from_pretrained(self.tmpdirname						,	pad_token="<pad>"						)

       # Simple input
       snake_case    :      Any      					=    "This is a simple input"
       snake_case    :      Tuple      					=    ["This is a simple input looooooooong", "This is a simple input"]
       snake_case    :      Dict      					=    ("This is a simple input", "This is a pair")
       snake_case    :      Dict      					=    [
           ("This is a simple input loooooong", "This is a simple input"),
           ("This is a simple pair loooooong", "This is a simple pair"),
       ]

       snake_case    :      Tuple      					=    tokenizer.pad_token_id

       snake_case    :      Union[str, Any]      					=    tokenizer(UpperCamelCase__						,	padding="max_length"						,	max_length=30						,	return_tensors="np"						)
       snake_case    :      Optional[Any]      					=    tokenizer(UpperCamelCase__						,	padding=UpperCamelCase__						,	truncate=UpperCamelCase__						,	return_tensors="np"						)
       snake_case    :      int      					=    tokenizer(*UpperCamelCase__						,	padding="max_length"						,	max_length=60						,	return_tensors="np"						)
       snake_case    :      str      					=    tokenizer(UpperCamelCase__						,	padding=UpperCamelCase__						,	truncate=UpperCamelCase__						,	return_tensors="np"						)

       # s
       # test single string max_length padding
       self.assertEqual(out_s["input_ids"].shape[-1]						,	30						)
       self.assertTrue(pad_token_id in out_s["input_ids"]						)
       self.assertTrue(0 in out_s["attention_mask"]						)

       # s2
       # test automatic padding
       self.assertEqual(out_sa["input_ids"].shape[-1]						,	33						)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa["input_ids"][0]						)
       self.assertFalse(0 in out_sa["attention_mask"][0]						)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa["input_ids"][1]						)
       self.assertTrue(0 in out_sa["attention_mask"][1]						)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p["input_ids"].shape[-1]						,	60						)
       self.assertTrue(pad_token_id in out_p["input_ids"]						)
       self.assertTrue(0 in out_p["attention_mask"]						)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa["input_ids"].shape[-1]						,	52						)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa["input_ids"][0]						)
       self.assertFalse(0 in out_pa["attention_mask"][0]						)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa["input_ids"][1]						)
       self.assertTrue(0 in out_pa["attention_mask"][1]						)







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Any      					=    "$$$"
       snake_case    :      List[Any]      					=    GPTaTokenizer.from_pretrained(self.tmpdirname						,	bos_token=UpperCamelCase__						,	add_bos_token=UpperCamelCase__						)

       snake_case    :      Optional[int]      					=    "This is a simple input"
       snake_case    :      str      					=    ["This is a simple input 1", "This is a simple input 2"]

       snake_case    :      List[Any]      					=    tokenizer.bos_token_id

       snake_case    :      Any      					=    tokenizer(UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    tokenizer(UpperCamelCase__						)

       self.assertEqual(out_s.input_ids[0]						,	UpperCamelCase__						)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

       snake_case    :      Union[str, Any]      					=    tokenizer.decode(out_s.input_ids						)
       snake_case    :      Tuple      					=    tokenizer.batch_decode(out_sa.input_ids						)

       self.assertEqual(decode_s.split()[0]						,	UpperCamelCase__						)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       pass







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    [self.get_tokenizer(do_lower_case=UpperCamelCase__						,	add_bos_token=UpperCamelCase__						)]
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'						):
               snake_case    :      Optional[int]      					=    "Encode this."
               snake_case    :      Tuple      					=    "This one too please."
               snake_case    :      List[Any]      					=    tokenizer.encode(UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						)
               encoded_sequence += tokenizer.encode(UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						)
               snake_case    :      Any      					=    tokenizer.encode_plus(
                   UpperCamelCase__						,	UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						,	return_special_tokens_mask=UpperCamelCase__						,	)
               snake_case    :      Union[str, Any]      					=    encoded_sequence_dict["input_ids"]
               snake_case    :      int      					=    encoded_sequence_dict["special_tokens_mask"]
               self.assertEqual(len(UpperCamelCase__						)						,	len(UpperCamelCase__						)						)

               snake_case    :      Optional[Any]      					=    [
                   (x if not special_tokens_mask[i] else None) for i, x in enumerate(UpperCamelCase__						)
               ]
               snake_case    :      Optional[Any]      					=    [x for x in filtered_sequence if x is not None]
               self.assertEqual(UpperCamelCase__						,	UpperCamelCase__						)







@require_tokenizers
class _lowerCAmelCase				(      unittest.TestCase	):







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

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    AutoTokenizer.from_pretrained("facebook/opt-350m"						,	from_slow=UpperCamelCase__						)
       snake_case    :      List[Any]      					=    "A photo of a cat"

       snake_case    :      Any      					=    tokenizer.encode(
           UpperCamelCase__						,	)
       self.assertEqual(UpperCamelCase__						,	[2, 250, 1345, 9, 10, 4758]						)
       tokenizer.save_pretrained("test_opt"						)

       snake_case    :      Tuple      					=    AutoTokenizer.from_pretrained("./test_opt"						)
       snake_case    :      str      					=    tokenizer.encode(
           UpperCamelCase__						,	)
       self.assertEqual(UpperCamelCase__						,	[2, 250, 1345, 9, 10, 4758]						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    AutoTokenizer.from_pretrained("facebook/opt-350m"						,	use_slow=UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    "A photo of a cat"

       snake_case    :      List[str]      					=    tokenizer.encode(
           UpperCamelCase__						,	)
       # Same as above
       self.assertEqual(UpperCamelCase__						,	[2, 250, 1345, 9, 10, 4758]						)







   @unittest.skip("This test is failing because of a bug in the fast tokenizer"						)
   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      List[str]      					=    AutoTokenizer.from_pretrained("facebook/opt-350m"						,	from_slow=UpperCamelCase__						)

       snake_case    :      int      					=    "bos"
       snake_case    :      Optional[int]      					=    tokenizer.get_vocab()["bos"]

       snake_case    :      Dict      					=    "A photo of a cat"
       snake_case    :      Union[str, Any]      					=    tokenizer.encode(
           UpperCamelCase__						,	)
       # We changed the bos token
       self.assertEqual(UpperCamelCase__						,	[3_1957, 250, 1345, 9, 10, 4758]						)
       tokenizer.save_pretrained("./tok"						)
       snake_case    :      int      					=    AutoTokenizer.from_pretrained("./tok"						)
       self.assertTrue(tokenizer.is_fast						)
       snake_case    :      List[Any]      					=    tokenizer.encode(
           UpperCamelCase__						,	)
       self.assertEqual(UpperCamelCase__						,	[3_1957, 250, 1345, 9, 10, 4758]						)









"""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()





