


'''simple docstring'''
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						(UnCLIPScheduler,)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  __A  :		List[Any]       =			{
      'num_train_timesteps': 1_000,
      'variance_type': 'fixed_small_log',
      'clip_sample': True,
      'clip_sample_range': 1.0,
      'prediction_type': 'epsilon',
  }

  config.update(**__lowercase						)
  return config


 def  snake_case__   (      self						):

  """simple docstring"""







  for timesteps in [1, 5, 100, 1_000]:
   self.check_over_configs(num_train_timesteps=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  for variance in ["fixed_small_log", "learned_range"]:
   self.check_over_configs(variance_type=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  for clip_sample in [True, False]:
   self.check_over_configs(clip_sample=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  for clip_sample_range in [1, 5, 10, 20]:
   self.check_over_configs(clip_sample_range=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  for prediction_type in ["epsilon", "sample"]:
   self.check_over_configs(prediction_type=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  for time_step in [0, 500, 999]:
   for prev_timestep in [None, 5, 100, 250, 500, 750]:
    if prev_timestep is not None and prev_timestep >= time_step:
     continue

    self.check_over_forward(time_step=__lowercase      ,      prev_timestep=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.scheduler_classes[0]
  __A  :		Tuple       =			self.get_scheduler_config(variance_type='fixed_small_log'						)
  __A  :		Optional[int]       =			scheduler_class(**__lowercase						)

  assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0_000E-10						)						) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.0_5_4_9_6_2_5						)						) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.9_9_9_4_9_8_7						)						) < 1E-5


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.scheduler_classes[0]
  __A  :		int       =			self.get_scheduler_config(variance_type='learned_range'						)
  __A  :		Tuple       =			scheduler_class(**__lowercase						)

  __A  :		Dict       =			0.5

  assert scheduler._get_variance(1      ,      predicted_variance=__lowercase						) - -1_0.1_7_1_2_7_9_0 < 1E-5
  assert scheduler._get_variance(487      ,      predicted_variance=__lowercase						) - -5.7_9_9_8_0_5_2 < 1E-5
  assert scheduler._get_variance(999      ,      predicted_variance=__lowercase						) - -0.0_0_1_0_0_1_1 < 1E-5


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self.scheduler_classes[0]
  __A  :		Union[str, Any]       =			self.get_scheduler_config()
  __A  :		Optional[int]       =			scheduler_class(**__lowercase						)

  __A  :		Union[str, Any]       =			scheduler.timesteps

  __A  :		List[Any]       =			self.dummy_model()
  __A  :		Tuple       =			self.dummy_sample_deter
  __A  :		Tuple       =			torch.manual_seed(0						)

  for i, t in enumerate(__lowercase						):
   # 1. predict noise residual
   __A  :		List[Any]       =			model(__lowercase      ,      __lowercase						)

   # 2. predict previous mean of sample x_t-1
   __A  :		Any       =			scheduler.step(__lowercase      ,      __lowercase      ,      __lowercase      ,      generator=__lowercase						).prev_sample

   __A  :		List[str]       =			pred_prev_sample

  __A  :		int       =			torch.sum(torch.abs(__lowercase						)						)
  __A  :		List[Any]       =			torch.mean(torch.abs(__lowercase						)						)

  assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5						) < 1E-2
  assert abs(result_mean.item() - 0.3_2_8_4_7_4_3						) < 1E-3


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self.scheduler_classes[0]
  __A  :		str       =			self.get_scheduler_config()
  __A  :		List[str]       =			scheduler_class(**__lowercase						)

  scheduler.set_timesteps(25						)

  __A  :		List[Any]       =			scheduler.timesteps

  __A  :		Union[str, Any]       =			self.dummy_model()
  __A  :		Optional[int]       =			self.dummy_sample_deter
  __A  :		Any       =			torch.manual_seed(0						)

  for i, t in enumerate(__lowercase						):
   # 1. predict noise residual
   __A  :		str       =			model(__lowercase      ,      __lowercase						)

   if i + 1 == timesteps.shape[0]:
    __A  :		int       =			None
   else:
    __A  :		List[Any]       =			timesteps[i + 1]

   # 2. predict previous mean of sample x_t-1
   __A  :		Union[str, Any]       =			scheduler.step(
       __lowercase      ,      __lowercase      ,      __lowercase      ,      prev_timestep=__lowercase      ,      generator=__lowercase						).prev_sample

   __A  :		Optional[Any]       =			pred_prev_sample

  __A  :		List[Any]       =			torch.sum(torch.abs(__lowercase						)						)
  __A  :		Tuple       =			torch.mean(torch.abs(__lowercase						)						)

  assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3						) < 1E-2
  assert abs(result_mean.item() - 0.3_3_6_2_0_3_8						) < 1E-3


 def  snake_case__   (      self						):

  """simple docstring"""







  pass







 def  snake_case__   (      self						):

  """simple docstring"""







  pass










'''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
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


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

 logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""			)
 if args.tokenizer_type == "bert":
  __A  :		Any       =			BertTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  __A  :		List[Any]       =			tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  __A  :		Any       =			RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		List[Any]       =			tokenizer.special_tokens_map['cls_token']  # `<s>`
  __A  :		Dict       =			tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  __A  :		List[str]       =			GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  __A  :		Union[str, Any]       =			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:
  __A  :		Optional[int]       =			fp.readlines()

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

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

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

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


if __name__ == "__main__":
 main()






