
"""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    __lowerCAmelCase    (_UpperCamelCase		):
	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    (_UpperCamelCase	,  _UpperCamelCase		):
	if args.student_type == "roberta":
		__lowerCAmelCase      :						Optional[int]						      =				False
	elif args.student_type == "gpt2":
		__lowerCAmelCase      :						Tuple						      =				False
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	if args.student_type == "roberta":
		__lowerCAmelCase      :						Optional[Any]						      =				False
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						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=_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=4000	,  help='Checkpoint interval.'		)
	__lowerCAmelCase      :						List[str]						      =				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		)

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

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

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

	__lowerCAmelCase      :						str						      =				LmSeqsDataset(params=_UpperCamelCase	,  data=_UpperCamelCase		)
	logger.info('Data loader created.'		)

	# STUDENT #
	logger.info(F"Loading student config from {args.student_config}"		)
	__lowerCAmelCase      :						List[str]						      =				student_config_class.from_pretrained(args.student_config		)
	__lowerCAmelCase      :						Optional[int]						      =				True

	if args.student_pretrained_weights is not None:
		logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"		)
		__lowerCAmelCase      :						Union[str, Any]						      =				student_model_class.from_pretrained(args.student_pretrained_weights	,  config=_UpperCamelCase		)
	else:
		__lowerCAmelCase      :						Any						      =				student_model_class(_UpperCamelCase		)

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

	# TEACHER #
	__lowerCAmelCase      :						Optional[int]						      =				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()
	__lowerCAmelCase      :						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 qiskit
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Union[str, Any]						      =				qiskit.Aer.get_backend('aer_simulator'		)

	# Create a Quantum Circuit acting on the q register
	__lowerCAmelCase      :						str						      =				qiskit.QuantumCircuit(_UpperCamelCase	,  _UpperCamelCase		)

	# Map the quantum measurement to the classical bits
	circuit.measure([0]	,  [0]		)

	# Execute the circuit on the simulator
	__lowerCAmelCase      :						Optional[int]						      =				qiskit.execute(_UpperCamelCase	,  _UpperCamelCase	,  shots=1000		)

	# Return the histogram data of the results of the experiment.
	return job.result().get_counts(_UpperCamelCase		)


if __name__ == "__main__":
							print(f'Total count for various states are: {single_qubit_measure(1, 1)}')