
"""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__      :	List[Any]          =    logging.getLogger(__name__)



def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						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."		)
     UpperCAmelCase__							: Tuple 							=						parser.parse_args()

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

     logger.info("Start encoding"		)
     logger.info(F"{len(lowerCAmelCase		)} examples to process."		)

     UpperCAmelCase__							: List[Any] 							=						[]
     UpperCAmelCase__							: Dict 							=						0
     UpperCAmelCase__							: Union[str, Any] 							=						1_0000
     UpperCAmelCase__							: List[Any] 							=						time.time()
     for text in data:
          UpperCAmelCase__							: List[str] 							=						F"{bos} {text.strip()} {sep}"
          UpperCAmelCase__							: Dict 							=						tokenizer.encode(lowerCAmelCase       ,    add_special_tokens=lowerCAmelCase		)
          rslt.append(lowerCAmelCase		)

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

     UpperCAmelCase__							: Optional[int] 							=						F"{args.dump_file}.{args.tokenizer_name}.pickle"
     UpperCAmelCase__							: Union[str, Any] 							=						tokenizer.vocab_size
     if vocab_size < (1 << 16):
          UpperCAmelCase__							: int 							=						[np.uintaa(lowerCAmelCase		) for d in rslt]
     else:
          UpperCAmelCase__							: Union[str, Any] 							=						[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 unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort







class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )



     @property
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Tuple 							=						ort.SessionOptions()
          UpperCAmelCase__							: List[str] 							=						False
          return options



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: int 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: str 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: Tuple 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: Any 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=10 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: Tuple 							=						output.images
          UpperCAmelCase__							: Dict 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: Union[str, Any] 							=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: Optional[Any] 							=						LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			subfolder="scheduler" ,			revision="onnx"  )
          UpperCAmelCase__							: Optional[Any] 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			scheduler=__UpperCamelCase ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: List[str] 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: str 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=20 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: List[str] 							=						output.images
          UpperCAmelCase__							: List[Any] 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: int 							=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



