




"""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


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







def 	_snake_case     (							lowercase__						):
				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 	_snake_case     (							lowercase__				,       lowercase__						):
				if args.student_type == "roberta":
								_lowerCamelCase	:     Tuple       =       False
				elif args.student_type == "gpt2":
								_lowerCamelCase	:     Union[str, Any]       =       False







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if args.student_type == "roberta":
								_lowerCamelCase	:     Union[str, Any]       =       False







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

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

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

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

				parser.add_argument('--log_interval'				,       type=lowercase__				,       default=500				,       help='Tensorboard logging interval.'						)
				parser.add_argument('--checkpoint_interval'				,       type=lowercase__				,       default=4000				,       help='Checkpoint interval.'						)
				_lowerCamelCase	:     str       =       parser.parse_args()
				sanity_checks(lowercase__						)

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

				_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       MODEL_CLASSES[args.student_type]
				_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       MODEL_CLASSES[args.teacher_type]

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

				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	:     Dict       =       pickle.load(lowercase__						)

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

				_lowerCamelCase	:     List[str]       =       LmSeqsDataset(params=lowercase__				,       data=lowercase__						)
				logger.info('Data loader created.'						)

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

				if args.student_pretrained_weights is not None:
								logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''						)
								_lowerCamelCase	:     Dict       =       student_model_class.from_pretrained(args.student_pretrained_weights				,       config=lowercase__						)
				else:
								_lowerCamelCase	:     Optional[Any]       =       student_model_class(lowercase__						)

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

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

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


if __name__ == "__main__":
			main()





"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__			=		argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
lowercase__			=		parser.parse_args()


lowercase__			=		"""cpu"""
lowercase__			=		"""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

lowercase__			=		"""path-to-your-trained-model"""
lowercase__			=		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
			lowercase__			=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__			=		pipe.to(device)

# to channels last
lowercase__			=		pipe.unet.to(memory_format=torch.channels_last)
lowercase__			=		pipe.vae.to(memory_format=torch.channels_last)
lowercase__			=		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
			lowercase__			=		pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase__			=		torch.randn(2, 4, 64, 64)
lowercase__			=		torch.rand(1) * 999
lowercase__			=		torch.randn(2, 77, 768)
lowercase__			=		(sample, timestep, encoder_hidden_status)
try:
			lowercase__			=		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
			lowercase__			=		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__			=		ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__			=		ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
			lowercase__			=		ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__			=		666
lowercase__			=		torch.Generator(device).manual_seed(seed)
lowercase__			=		{"""generator""": generator}
if args.steps is not None:
			lowercase__			=		args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
			lowercase__			=		pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")