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


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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     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     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):






     if args.student_type == "roberta":
          lowercase_			:       Tuple									=			False
     elif args.student_type == "gpt2":
          lowercase_			:       Tuple									=			False



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Dict   ):






     if args.student_type == "roberta":
          lowercase_			:       List[Any]									=			False



def     lowercase__( ):
     lowercase_			:       int									=			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=5_00  ,    help='Tensorboard logging interval.'   )
     parser.add_argument('--checkpoint_interval'  ,    type=_UpperCamelCase  ,    default=40_00  ,    help='Checkpoint interval.'   )
     lowercase_			:       Dict									=			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   )

     lowercase_							,						lowercase_							,						lowercase_			:       Dict									=			MODEL_CLASSES[args.student_type]
     lowercase_							,						lowercase_							,						lowercase_			:       List[str]									=			MODEL_CLASSES[args.teacher_type]

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

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

     lowercase_			:       Optional[int]									=			LmSeqsDataset(params=_UpperCamelCase  ,    data=_UpperCamelCase   )
     logger.info('Data loader created.'   )

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

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

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

     # TEACHER #
     lowercase_			:       str									=			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()
     lowercase_			:       Optional[Any]									=			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()


"""simple docstring"""







import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.distilbert.modeling_flax_distilbert import (
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertModel,
       )







class  UpperCamelCase	(    unittest.TestCase						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=13  ,__UpperCamelCase=7  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=True  ,__UpperCamelCase=99  ,__UpperCamelCase=32  ,__UpperCamelCase=5  ,__UpperCamelCase=4  ,__UpperCamelCase=37  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=512  ,__UpperCamelCase=16  ,__UpperCamelCase=2  ,__UpperCamelCase=0.02  ,__UpperCamelCase=4  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       str									=			parent
        lowercase_			:       List[str]									=			batch_size
        lowercase_			:       Optional[Any]									=			seq_length
        lowercase_			:       str									=			is_training
        lowercase_			:       Any									=			use_attention_mask
        lowercase_			:       int									=			use_token_type_ids
        lowercase_			:       Union[str, Any]									=			use_labels
        lowercase_			:       int									=			vocab_size
        lowercase_			:       Union[str, Any]									=			hidden_size
        lowercase_			:       Optional[int]									=			num_hidden_layers
        lowercase_			:       Dict									=			num_attention_heads
        lowercase_			:       Dict									=			intermediate_size
        lowercase_			:       str									=			hidden_act
        lowercase_			:       Union[str, Any]									=			hidden_dropout_prob
        lowercase_			:       Dict									=			attention_probs_dropout_prob
        lowercase_			:       Union[str, Any]									=			max_position_embeddings
        lowercase_			:       List[Any]									=			type_vocab_size
        lowercase_			:       str									=			type_sequence_label_size
        lowercase_			:       Optional[Any]									=			initializer_range
        lowercase_			:       Any									=			num_choices
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

        lowercase_			:       List[Any]									=			None
        if self.use_attention_mask:
             lowercase_			:       Dict									=			random_attention_mask([self.batch_size, self.seq_length]    )

        lowercase_			:       Optional[Any]									=			DistilBertConfig(
            vocab_size=self.vocab_size  ,dim=self.hidden_size  ,n_layers=self.num_hidden_layers  ,n_heads=self.num_attention_heads  ,hidden_dim=self.intermediate_size  ,hidden_act=self.hidden_act  ,dropout=self.hidden_dropout_prob  ,attention_dropout=self.attention_probs_dropout_prob  ,max_position_embeddings=self.max_position_embeddings  ,initializer_range=self.initializer_range  ,tie_weights_=__UpperCamelCase  ,)

        return config, input_ids, attention_mask







   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Any									=			self.prepare_config_and_inputs()
        lowercase_							,						lowercase_							,						lowercase_			:       str									=			config_and_inputs
        lowercase_			:       List[str]									=			{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict







@require_flax
class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						(
       (
           FlaxDistilBertModel,
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			FlaxDistilBertModelTester(self    )







   @slow
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        for model_class_name in self.all_model_classes:
             lowercase_			:       Optional[int]									=			model_class_name.from_pretrained('distilbert-base-uncased'    )
             lowercase_			:       List[str]									=			model(np.ones((1, 1)    )    )
             self.assertIsNotNone(__UpperCamelCase    )







@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   @slow
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'    )
        lowercase_			:       Tuple									=			np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
        lowercase_			:       Optional[int]									=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
        lowercase_			:       Any									=			model(__UpperCamelCase  ,attention_mask=__UpperCamelCase    )[0]
        lowercase_			:       Tuple									=			(1, 11, 768)
        self.assertEqual(output.shape  ,__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]    )

        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]  ,__UpperCamelCase  ,atol=1e-4    )    )

