






'''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


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







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Dict:
		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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Union[str, Any]:
		if args.student_type == "roberta":
				UpperCamelCase__				:					int					=					False
		elif args.student_type == "gpt2":
				UpperCamelCase__				:					int					=					False







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Optional[Any]:
		if args.student_type == "roberta":
				UpperCamelCase__				:					Tuple					=					False







def       __UpperCAmelCase						(  )							->     Any:
		UpperCamelCase__				:					List[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=lowerCamelCase_     ,      required=lowerCamelCase_     ,      help='The output directory (log, checkpoints, parameters, etc.)')
		parser.add_argument(
		    '--data_file'     ,      type=lowerCamelCase_     ,      required=lowerCamelCase_     ,      help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'     ,      )

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

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

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

		parser.add_argument('--log_interval'     ,      type=lowerCamelCase_     ,      default=500     ,      help='Tensorboard logging interval.')
		parser.add_argument('--checkpoint_interval'     ,      type=lowerCamelCase_     ,      default=4_000     ,      help='Checkpoint interval.')
		UpperCamelCase__				:					Tuple					=					parser.parse_args()
		sanity_checks(lowerCamelCase_)

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

		UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Optional[int]					=					MODEL_CLASSES[args.student_type]
		UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					MODEL_CLASSES[args.teacher_type]

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

		if args.mlm:
				logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)')
				with open(args.token_counts     ,      'rb') as fp:
						UpperCamelCase__				:					List[Any]					=					pickle.load(lowerCamelCase_)

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

		UpperCamelCase__				:					Union[str, Any]					=					LmSeqsDataset(params=lowerCamelCase_     ,      data=lowerCamelCase_)
		logger.info('Data loader created.')

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

		if args.student_pretrained_weights is not None:
				logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}')
				UpperCamelCase__				:					Dict					=					student_model_class.from_pretrained(args.student_pretrained_weights     ,      config=lowerCamelCase_)
		else:
				UpperCamelCase__				:					str					=					student_model_class(lowerCamelCase_)

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

		# TEACHER #
		UpperCamelCase__				:					Union[str, Any]					=					teacher_model_class.from_pretrained(args.teacher_name     ,      output_hidden_states=lowerCamelCase_)
		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(lowerCamelCase_     ,      lowerCamelCase_)
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(lowerCamelCase_     ,      lowerCamelCase_)

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


if __name__ == "__main__":
		main()












'''simple docstring'''




class       __lowercase					:

	def __init__(      self   :						List[str]  ,					UpperCAmelCase_   :						str = ""  ,					UpperCAmelCase_   :						bool = False):
			# Mapping from the first character of the prefix of the node
			UpperCamelCase__				:					dict[str, RadixNode]					=					{}

			# A node will be a leaf if the tree contains its word
			UpperCamelCase__				:					List[Any]					=					is_leaf

			UpperCamelCase__				:					Optional[Any]					=					prefix

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[int]					=					0
			for q, w in zip(self.prefix  ,					UpperCAmelCase_):
					if q != w:
							break

					x += 1

			return self.prefix[:x], self.prefix[x:], word[x:]

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						list[str]):
			for word in words:
					self.insert(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str):
			# Case 1: If the word is the prefix of the node
			# Solution: We set the current node as leaf
			if self.prefix == word:
					UpperCamelCase__				:					Optional[Any]					=					True

			# Case 2: The node has no edges that have a prefix to the word
			# Solution: We create an edge from the current node to a new one
			# containing the word
			elif word[0] not in self.nodes:
					UpperCamelCase__				:					Optional[Any]					=					RadixNode(prefix=UpperCAmelCase_  ,					is_leaf=UpperCAmelCase_)

			else:
					UpperCamelCase__				:					int					=					self.nodes[word[0]]
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[Any]					=					incoming_node.match(
					    UpperCAmelCase_)

					# Case 3: The node prefix is equal to the matching
					# Solution: We insert remaining word on the next node
					if remaining_prefix == "":
							self.nodes[matching_string[0]].insert(UpperCAmelCase_)

					# Case 4: The word is greater equal to the matching
					# Solution: Create a node in between both nodes, change
					# prefixes and add the new node for the remaining word
					else:
							UpperCamelCase__				:					Tuple					=					remaining_prefix

							UpperCamelCase__				:					str					=					self.nodes[matching_string[0]]
							UpperCamelCase__				:					Optional[Any]					=					RadixNode(UpperCAmelCase_  ,					UpperCAmelCase_)
							UpperCamelCase__				:					str					=					aux_node

							if remaining_word == "":
									UpperCamelCase__				:					int					=					True
							else:
									self.nodes[matching_string[0]].insert(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[Any]					=					self.nodes.get(word[0]  ,					UpperCAmelCase_)
			if not incoming_node:
					return False
			else:
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					int					=					incoming_node.match(
					    UpperCAmelCase_)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
							return False
					# This applies when the word and the prefix are equal
					elif remaining_word == "":
							return incoming_node.is_leaf
					# We have word remaining so we check the next node
					else:
							return incoming_node.find(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[int]					=					self.nodes.get(word[0]  ,					UpperCAmelCase_)
			if not incoming_node:
					return False
			else:
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					incoming_node.match(
					    UpperCAmelCase_)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
							return False
					# We have word remaining so we check the next node
					elif remaining_word != "":
							return incoming_node.delete(UpperCAmelCase_)
					else:
							# If it is not a leaf, we don't have to delete
							if not incoming_node.is_leaf:
									return False
							else:
									# We delete the nodes if no edges go from it
									if len(incoming_node.nodes) == 0:
											del self.nodes[word[0]]
											# We merge the current node with its only child
											if len(self.nodes) == 1 and not self.is_leaf:
													UpperCamelCase__				:					List[str]					=					list(self.nodes.values())[0]
													UpperCamelCase__				:					Tuple					=					merging_node.is_leaf
													self.prefix += merging_node.prefix
													UpperCamelCase__				:					Tuple					=					merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
									elif len(incoming_node.nodes) > 1:
											UpperCamelCase__				:					str					=					False
									# If there is 1 edge, we merge it with its child
									else:
											UpperCamelCase__				:					List[Any]					=					list(incoming_node.nodes.values())[0]
											UpperCamelCase__				:					Optional[Any]					=					merging_node.is_leaf
											incoming_node.prefix += merging_node.prefix
											UpperCamelCase__				:					Union[str, Any]					=					merging_node.nodes

									return True





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						int = 0):
			if self.prefix != "":
					print('-' * height  ,					self.prefix  ,					'  (leaf)' if self.is_leaf else '')

			for value in self.nodes.values():
					value.print_tree(height + 1)







def       __UpperCAmelCase						(  )							->     bool:
		UpperCamelCase__				:					Union[str, Any]					=					'banana bananas bandana band apple all beast'.split()
		UpperCamelCase__				:					List[Any]					=					RadixNode()
		root.insert_many(lowerCamelCase_)

		assert all(root.find(lowerCamelCase_) for word in words)
		assert not root.find('bandanas')
		assert not root.find('apps')
		root.delete('all')
		assert not root.find('all')
		root.delete('banana')
		assert not root.find('banana')
		assert root.find('bananas')

		return True







def       __UpperCAmelCase						(  )							->     None:
		assert test_trie()







def       __UpperCAmelCase						(  )							->     None:
		UpperCamelCase__				:					List[Any]					=					RadixNode()
		UpperCamelCase__				:					List[str]					=					'banana bananas bandanas bandana band apple all beast'.split()
		root.insert_many(lowerCamelCase_)

		print('Words:'     ,      lowerCamelCase_)
		print('Tree:')
		root.print_tree()


if __name__ == "__main__":
		main()




