
"""simple docstring"""


import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
A_    							=					logging.getLogger(__name__)






def  lowercase						(	):
			lowerCamelCase_					=  argparse.ArgumentParser(
			    description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''						)
			parser.add_argument('''--file_path'''	,type=lowerCAmelCase__	,default='''data/dump.txt'''	,help='''The path to the data.'''						)
			parser.add_argument('''--tokenizer_type'''	,type=lowerCAmelCase__	,default='''bert'''	,choices=['''bert''', '''roberta''', '''gpt2''']						)
			parser.add_argument('''--tokenizer_name'''	,type=lowerCAmelCase__	,default='''bert-base-uncased'''	,help='''The tokenizer to use.'''						)
			parser.add_argument('''--dump_file'''	,type=lowerCAmelCase__	,default='''data/dump'''	,help='''The dump file prefix.'''						)
			lowerCamelCase_					=  parser.parse_args()

			logger.info(f"Loading Tokenizer ({args.tokenizer_name})"						)
			if args.tokenizer_type == "bert":
						lowerCamelCase_					=  BertTokenizer.from_pretrained(args.tokenizer_name						)
						lowerCamelCase_					=  tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
						lowerCamelCase_					=  tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						lowerCamelCase_					=  RobertaTokenizer.from_pretrained(args.tokenizer_name						)
						lowerCamelCase_					=  tokenizer.special_tokens_map['''cls_token''']  # `<s>`
						lowerCamelCase_					=  tokenizer.special_tokens_map['''sep_token''']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						lowerCamelCase_					=  GPTaTokenizer.from_pretrained(args.tokenizer_name						)
						lowerCamelCase_					=  tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
						lowerCamelCase_					=  tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

			logger.info(f"Loading text from {args.file_path}"						)
			with open(args.file_path	,'''r'''	,encoding='''utf8'''						) as fp:
						lowerCamelCase_					=  fp.readlines()

			logger.info('''Start encoding'''						)
			logger.info(f"{len(lowerCAmelCase__						)} examples to process."						)

			lowerCamelCase_					=  []
			lowerCamelCase_					=  0
			lowerCamelCase_					=  10_000
			lowerCamelCase_					=  time.time()
			for text in data:
						lowerCamelCase_					=  f"{bos} {text.strip()} {sep}"
						lowerCamelCase_					=  tokenizer.encode(lowerCAmelCase__	,add_special_tokens=lowerCAmelCase__						)
						rslt.append(lowerCAmelCase__						)

						iter += 1
						if iter % interval == 0:
									lowerCamelCase_					=  time.time()
									logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"						)
									lowerCamelCase_					=  time.time()
			logger.info('''Finished binarization'''						)
			logger.info(f"{len(lowerCAmelCase__						)} examples processed."						)

			lowerCamelCase_					=  f"{args.dump_file}.{args.tokenizer_name}.pickle"
			lowerCamelCase_					=  tokenizer.vocab_size
			if vocab_size < (1 << 16):
						lowerCamelCase_					=  [np.uintaa(lowerCAmelCase__						) for d in rslt]
			else:
						lowerCamelCase_					=  [np.intaa(lowerCAmelCase__						) for d in rslt]
			random.shuffle(rslt_						)
			logger.info(f"Dump to {dp_file}"						)
			with open(lowerCAmelCase__	,'''wb'''						) as handle:
						pickle.dump(rslt_	,lowerCAmelCase__	,protocol=pickle.HIGHEST_PROTOCOL						)


if __name__ == "__main__":
						main()





"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    (DDPMScheduler,)




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCAmelCase   )
							return config




				def        UpperCAmelCase__						(				self   ):
							for timesteps in [1, 5, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,						[0.0_0_2, 0.0_2, 0.2, 2]   ):
										self.check_over_configs(beta_start=UpperCAmelCase							,						beta_end=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							self.check_over_configs(thresholding=UpperCAmelCase   )
							for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=UpperCAmelCase							,						prediction_type=UpperCAmelCase							,						sample_max_value=UpperCAmelCase							,						)




				def        UpperCAmelCase__						(				self   ):
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for t in [0, 500, 999]:
										self.check_over_forward(time_step=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0_9_7_9   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.0_2   )   ) < 1e-5




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter
							lowerCamelCase_					=  torch.manual_seed(0   )

							for t in reversed(range(UpperCAmelCase   )   ):
										# 1. predict noise residual
										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										# 2. predict previous mean of sample x_t-1
										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						generator=UpperCAmelCase   ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
							assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config(prediction_type='''v_prediction'''   )
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter
							lowerCamelCase_					=  torch.manual_seed(0   )

							for t in reversed(range(UpperCAmelCase   )   ):
										# 1. predict noise residual
										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										# 2. predict previous mean of sample x_t-1
										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						generator=UpperCAmelCase   ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
							assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=UpperCAmelCase   )

							lowerCamelCase_					=  scheduler.timesteps

							for i, timestep in enumerate(UpperCAmelCase   ):
										if i == len(UpperCAmelCase   ) - 1:
													lowerCamelCase_					=  -1
										else:
													lowerCamelCase_					=  timesteps[i + 1]

										lowerCamelCase_					=  scheduler.previous_timestep(UpperCAmelCase   )
										lowerCamelCase_					=  prev_t.item()

										self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 51, 0]

							with self.assertRaises(UpperCAmelCase							,						msg='''`custom_timesteps` must be in descending order.'''   ):
										scheduler.set_timesteps(timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]
							lowerCamelCase_					=  len(UpperCAmelCase   )

							with self.assertRaises(UpperCAmelCase							,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
										scheduler.set_timesteps(num_inference_steps=UpperCAmelCase							,						timesteps=UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    UpperCAmelCase							,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,						):
										scheduler.set_timesteps(timesteps=UpperCAmelCase   )



