



"""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


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



def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       Union[str, Any]:
				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_     :							Union[str, Any]						,   lowercase_     :							Any							)	->       Tuple:






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



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[Any]							)	->       Optional[int]:






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



def        lowerCAmelCase_(   )	->       str:
				_lowerCamelCase        =				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=__SCREAMING_SNAKE_CASE						,   required=__SCREAMING_SNAKE_CASE						,   help='''The output directory (log, checkpoints, parameters, etc.)'''							)
				parser.add_argument(
				    '''--data_file'''						,   type=__SCREAMING_SNAKE_CASE						,   required=__SCREAMING_SNAKE_CASE						,   help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''						,   )

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

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

				parser.add_argument('''--temperature'''						,   default=2.0						,   type=__SCREAMING_SNAKE_CASE						,   help='''Temperature for the softmax temperature.'''							)
				parser.add_argument(
				    '''--alpha_ce'''						,   default=0.5						,   type=__SCREAMING_SNAKE_CASE						,   help='''Linear weight for the distillation loss. Must be >=0.'''							)
				parser.add_argument(
				    '''--alpha_mlm'''						,   default=0.0						,   type=__SCREAMING_SNAKE_CASE						,   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=__SCREAMING_SNAKE_CASE						,   help='''Linear weight for the CLM loss. Must be >=0.'''							)
				parser.add_argument('''--alpha_mse'''						,   default=0.0						,   type=__SCREAMING_SNAKE_CASE						,   help='''Linear weight of the MSE loss. Must be >=0.'''							)
				parser.add_argument(
				    '''--alpha_cos'''						,   default=0.0						,   type=__SCREAMING_SNAKE_CASE						,   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=0.1_5						,   type=__SCREAMING_SNAKE_CASE						,   help='''Proportion of tokens for which we need to make a prediction.'''						,   )
				parser.add_argument('''--word_mask'''						,   default=0.8						,   type=__SCREAMING_SNAKE_CASE						,   help='''Proportion of tokens to mask out.'''							)
				parser.add_argument('''--word_keep'''						,   default=0.1						,   type=__SCREAMING_SNAKE_CASE						,   help='''Proportion of tokens to keep.'''							)
				parser.add_argument('''--word_rand'''						,   default=0.1						,   type=__SCREAMING_SNAKE_CASE						,   help='''Proportion of tokens to randomly replace.'''							)
				parser.add_argument(
				    '''--mlm_smoothing'''						,   default=0.7						,   type=__SCREAMING_SNAKE_CASE						,   help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''						,   )
				parser.add_argument('''--token_counts'''						,   type=__SCREAMING_SNAKE_CASE						,   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=__SCREAMING_SNAKE_CASE						,   default=3						,   help='''Number of pass on the whole dataset.'''							)
				parser.add_argument('''--batch_size'''						,   type=__SCREAMING_SNAKE_CASE						,   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=__SCREAMING_SNAKE_CASE						,   default=50						,   help='''Gradient accumulation for larger training batches.'''						,   )
				parser.add_argument('''--warmup_prop'''						,   default=0.0_5						,   type=__SCREAMING_SNAKE_CASE						,   help='''Linear warmup proportion.'''							)
				parser.add_argument('''--weight_decay'''						,   default=0.0						,   type=__SCREAMING_SNAKE_CASE						,   help='''Weight decay if we apply some.'''							)
				parser.add_argument('''--learning_rate'''						,   default=5e-4						,   type=__SCREAMING_SNAKE_CASE						,   help='''The initial learning rate for Adam.'''							)
				parser.add_argument('''--adam_epsilon'''						,   default=1e-6						,   type=__SCREAMING_SNAKE_CASE						,   help='''Epsilon for Adam optimizer.'''							)
				parser.add_argument('''--max_grad_norm'''						,   default=5.0						,   type=__SCREAMING_SNAKE_CASE						,   help='''Max gradient norm.'''							)
				parser.add_argument('''--initializer_range'''						,   default=0.0_2						,   type=__SCREAMING_SNAKE_CASE						,   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=__SCREAMING_SNAKE_CASE						,   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=__SCREAMING_SNAKE_CASE						,   default=1						,   help='''Number of GPUs in the node.'''							)
				parser.add_argument('''--local_rank'''						,   type=__SCREAMING_SNAKE_CASE						,   default=-1						,   help='''Distributed training - Local rank'''							)
				parser.add_argument('''--seed'''						,   type=__SCREAMING_SNAKE_CASE						,   default=56						,   help='''Random seed'''							)

				parser.add_argument('''--log_interval'''						,   type=__SCREAMING_SNAKE_CASE						,   default=5_00						,   help='''Tensorboard logging interval.'''							)
				parser.add_argument('''--checkpoint_interval'''						,   type=__SCREAMING_SNAKE_CASE						,   default=40_00						,   help='''Checkpoint interval.'''							)
				_lowerCamelCase        =				parser.parse_args()
				sanity_checks(__SCREAMING_SNAKE_CASE							)

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

				_lowerCamelCase        =				MODEL_CLASSES[args.student_type]
				_lowerCamelCase        =				MODEL_CLASSES[args.teacher_type]

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

				if args.mlm:
								logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""							)
								with open(args.token_counts						,   '''rb'''							) as fp:
												_lowerCamelCase        =				pickle.load(__SCREAMING_SNAKE_CASE							)

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

				_lowerCamelCase        =				LmSeqsDataset(params=__SCREAMING_SNAKE_CASE						,   data=__SCREAMING_SNAKE_CASE							)
				logger.info('''Data loader created.'''							)

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

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

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

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

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


if __name__ == "__main__":
			main()





"""simple docstring"""
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM


@require_torch
class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=9_9				,       lowerCamelCase__=1_3				,       lowerCamelCase__=1_6				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=2				,       lowerCamelCase__=3_2				,       lowerCamelCase__=4				,       lowerCamelCase__=4				,       lowerCamelCase__=3_0				,       lowerCamelCase__=0				,       lowerCamelCase__=1				,       lowerCamelCase__=2				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				decoder_seq_length
									# For common tests
									_lowerCamelCase        =				self.decoder_seq_length
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_attention_mask
									_lowerCamelCase        =				use_labels

									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				decoder_layers
									_lowerCamelCase        =				decoder_layers
									_lowerCamelCase        =				decoder_ffn_dim
									_lowerCamelCase        =				decoder_attention_heads
									_lowerCamelCase        =				decoder_attention_heads
									_lowerCamelCase        =				eos_token_id
									_lowerCamelCase        =				bos_token_id
									_lowerCamelCase        =				pad_token_id
									_lowerCamelCase        =				decoder_start_token_id
									_lowerCamelCase        =				use_cache
									_lowerCamelCase        =				max_position_embeddings

									_lowerCamelCase        =				None
									_lowerCamelCase        =				decoder_seq_length
									_lowerCamelCase        =				2
									_lowerCamelCase        =				1

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				None
									if self.use_attention_mask:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       vocab_size=2   )

									_lowerCamelCase        =				None
									if self.use_labels:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				TrOCRConfig(
									    vocab_size=self.vocab_size				,       d_model=self.d_model				,       decoder_layers=self.decoder_layers				,       decoder_ffn_dim=self.decoder_ffn_dim				,       decoder_attention_heads=self.decoder_attention_heads				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.bos_token_id				,       use_cache=self.use_cache				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       max_position_embeddings=self.max_position_embeddings				,       )

									return (config, input_ids, attention_mask, lm_labels)

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       ):
									_lowerCamelCase        =				True
									_lowerCamelCase        =				TrOCRDecoder(config=lowerCamelCase__   ).to(lowerCamelCase__   ).eval()
									_lowerCamelCase        =				input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									_lowerCamelCase        =				model(lowerCamelCase__				,       use_cache=lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       use_cache=lowerCamelCase__   )

									self.parent.assertTrue(len(lowerCamelCase__   ) == len(lowerCamelCase__   )   )
									self.parent.assertTrue(len(lowerCamelCase__   ) == len(lowerCamelCase__   ) + 1   )

									_lowerCamelCase        =				outputs['''past_key_values''']

									# create hypothetical next token and extent to next_input_ids
									_lowerCamelCase        =				ids_tensor((2, 1)				,       config.vocab_size - 1   ) + 1

									# append to next input_ids and
									_lowerCamelCase        =				torch.cat([input_ids, next_tokens]				,       dim=-1   )

									_lowerCamelCase        =				model(lowerCamelCase__   )['''last_hidden_state''']
									_lowerCamelCase        =				model(lowerCamelCase__				,       past_key_values=lowerCamelCase__   )['''last_hidden_state''']

									# select random slice
									_lowerCamelCase        =				ids_tensor((1,)				,       output_from_past.shape[-1]   ).item()
									_lowerCamelCase        =				output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									_lowerCamelCase        =				output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(lowerCamelCase__				,       lowerCamelCase__				,       atol=1e-3   )





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				config_and_inputs

									_lowerCamelCase        =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict


@require_torch
class 							lowerCamelCase_(   A__,				A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       int										= (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
					lowercase__   :       List[str]										= (TrOCRForCausalLM,) if is_torch_available() else ()
					lowercase__   :       Tuple										= {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
					lowercase__   :       Dict										= True
					lowercase__   :       Optional[Any]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TrOCRStandaloneDecoderModelTester(self				,       is_training=lowerCamelCase__   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									self.config_tester.run_common_tests()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									return





					@unittest.skip('''The model doesn\'t support left padding'''   )  # and it's not used enough to be worth fixing :)
					def 			snake_case__      (    self   ):
									pass
