
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A				=False




class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):
	pass




@nightly
@require_torch_gpu
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"A painting of a squirrel eating a burger "
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pipe(
					    prompt=lowercase						,  generator=lowercase						,  guidance_scale=7.5						,  num_inference_steps=2						,  output_type="numpy" ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(lowercase )
									lowerCamelCase_						=			VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase )
					pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			generator.manual_seed(0 )
					lowerCamelCase_						=			pipe(
					    prompt=lowercase						,  generator=lowercase						,  guidance_scale=7.5						,  num_inference_steps=2						,  output_type="numpy" ).images

					assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			VersatileDiffusionTextToImagePipeline.from_pretrained(
					    "shi-labs/versatile-diffusion"						,  torch_dtype=torch.floataa )
					pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"A painting of a squirrel eating a burger "
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pipe(
					    prompt=lowercase						,  generator=lowercase						,  guidance_scale=7.5						,  num_inference_steps=50						,  output_type="numpy" ).images

					lowerCamelCase_						=			image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					lowerCamelCase_						=			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2



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__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


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

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

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

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

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

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


if __name__ == "__main__":
      main()



