
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


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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):






    if args.student_type == "roberta":
        snake_case__						=				False
    elif args.student_type == "gpt2":
        snake_case__						=				False





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):






    if args.student_type == "roberta":
        snake_case__						=				False





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__						=				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=_UpperCamelCase    ,		required=_UpperCamelCase    ,		help="The output directory (log, checkpoints, parameters, etc.)"							)
    parser.add_argument(
        "--data_file"    ,		type=_UpperCamelCase    ,		required=_UpperCamelCase    ,		help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."    ,		)

    parser.add_argument(
        "--student_type"    ,		type=_UpperCamelCase    ,		choices=["distilbert", "roberta", "gpt2"]    ,		required=_UpperCamelCase    ,		help="The student type (DistilBERT, RoBERTa)."    ,		)
    parser.add_argument("--student_config"    ,		type=_UpperCamelCase    ,		required=_UpperCamelCase    ,		help="Path to the student configuration."							)
    parser.add_argument(
        "--student_pretrained_weights"    ,		default=_UpperCamelCase    ,		type=_UpperCamelCase    ,		help="Load student initialization checkpoint."							)

    parser.add_argument(
        "--teacher_type"    ,		choices=["bert", "roberta", "gpt2"]    ,		required=_UpperCamelCase    ,		help="Teacher type (BERT, RoBERTa)."							)
    parser.add_argument("--teacher_name"    ,		type=_UpperCamelCase    ,		required=_UpperCamelCase    ,		help="The teacher model."							)

    parser.add_argument("--temperature"    ,		default=2.0    ,		type=_UpperCamelCase    ,		help="Temperature for the softmax temperature."							)
    parser.add_argument(
        "--alpha_ce"    ,		default=0.5    ,		type=_UpperCamelCase    ,		help="Linear weight for the distillation loss. Must be >=0."							)
    parser.add_argument(
        "--alpha_mlm"    ,		default=0.0    ,		type=_UpperCamelCase    ,		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=_UpperCamelCase    ,		help="Linear weight for the CLM loss. Must be >=0."							)
    parser.add_argument("--alpha_mse"    ,		default=0.0    ,		type=_UpperCamelCase    ,		help="Linear weight of the MSE loss. Must be >=0."							)
    parser.add_argument(
        "--alpha_cos"    ,		default=0.0    ,		type=_UpperCamelCase    ,		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=_UpperCamelCase    ,		help="Proportion of tokens for which we need to make a prediction."    ,		)
    parser.add_argument("--word_mask"    ,		default=0.8    ,		type=_UpperCamelCase    ,		help="Proportion of tokens to mask out."							)
    parser.add_argument("--word_keep"    ,		default=0.1    ,		type=_UpperCamelCase    ,		help="Proportion of tokens to keep."							)
    parser.add_argument("--word_rand"    ,		default=0.1    ,		type=_UpperCamelCase    ,		help="Proportion of tokens to randomly replace."							)
    parser.add_argument(
        "--mlm_smoothing"    ,		default=0.7    ,		type=_UpperCamelCase    ,		help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."    ,		)
    parser.add_argument("--token_counts"    ,		type=_UpperCamelCase    ,		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=_UpperCamelCase    ,		default=3    ,		help="Number of pass on the whole dataset."							)
    parser.add_argument("--batch_size"    ,		type=_UpperCamelCase    ,		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=_UpperCamelCase    ,		default=50    ,		help="Gradient accumulation for larger training batches."    ,		)
    parser.add_argument("--warmup_prop"    ,		default=0.05    ,		type=_UpperCamelCase    ,		help="Linear warmup proportion."							)
    parser.add_argument("--weight_decay"    ,		default=0.0    ,		type=_UpperCamelCase    ,		help="Weight decay if we apply some."							)
    parser.add_argument("--learning_rate"    ,		default=5E-4    ,		type=_UpperCamelCase    ,		help="The initial learning rate for Adam."							)
    parser.add_argument("--adam_epsilon"    ,		default=1E-6    ,		type=_UpperCamelCase    ,		help="Epsilon for Adam optimizer."							)
    parser.add_argument("--max_grad_norm"    ,		default=5.0    ,		type=_UpperCamelCase    ,		help="Max gradient norm."							)
    parser.add_argument("--initializer_range"    ,		default=0.02    ,		type=_UpperCamelCase    ,		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=_UpperCamelCase    ,		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=_UpperCamelCase    ,		default=1    ,		help="Number of GPUs in the node."							)
    parser.add_argument("--local_rank"    ,		type=_UpperCamelCase    ,		default=-1    ,		help="Distributed training - Local rank"							)
    parser.add_argument("--seed"    ,		type=_UpperCamelCase    ,		default=56    ,		help="Random seed"							)

    parser.add_argument("--log_interval"    ,		type=_UpperCamelCase    ,		default=500    ,		help="Tensorboard logging interval."							)
    parser.add_argument("--checkpoint_interval"    ,		type=_UpperCamelCase    ,		default=4_000    ,		help="Checkpoint interval."							)
    snake_case__						=				parser.parse_args()
    sanity_checks(_UpperCamelCase							)

    # ARGS #
    init_gpu_params(_UpperCamelCase							)
    set_seed(_UpperCamelCase							)
    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(_UpperCamelCase							)    ,		_UpperCamelCase    ,		indent=4							)
        git_log(args.dump_path							)

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

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

    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__						=				pickle.load(_UpperCamelCase							)

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

    snake_case__						=				LmSeqsDataset(params=_UpperCamelCase    ,		data=_UpperCamelCase							)
    logger.info("Data loader created."							)

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

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

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

    # TEACHER #
    snake_case__						=				teacher_model_class.from_pretrained(args.teacher_name    ,		output_hidden_states=_UpperCamelCase							)
    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(_UpperCamelCase    ,		_UpperCamelCase							)
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(_UpperCamelCase    ,		_UpperCamelCase							)

    # 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__						=				Distiller(
        params=_UpperCamelCase    ,		dataset=_UpperCamelCase    ,		token_probs=_UpperCamelCase    ,		student=_UpperCamelCase    ,		teacher=_UpperCamelCase							)
    distiller.train()
    logger.info("Let\'s go get some drinks."							)


if __name__ == "__main__":
     main()





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[False] * len(__lowerCAmelCase							)
    snake_case__						=				[-1] * len(__lowerCAmelCase							)

    def dfs(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				True
        snake_case__						=				c
        for u in graph[v]:
            if not visited[u]:
                dfs(__lowerCAmelCase    ,		1 - c							)

    for i in range(len(__lowerCAmelCase							)							):
        if not visited[i]:
            dfs(__lowerCAmelCase    ,		0							)

    for i in range(len(__lowerCAmelCase							)							):
        for j in graph[i]:
            if color[i] == color[j]:
                return False

    return True


# Adjacency list of graph
__magic_name__            =  {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))



