

import argparse
import logging
import pickle
from collections import Counter


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__)

if __name__ == "__main__":
					_UpperCAmelCase	:     Optional[Any]		       = argparse.ArgumentParser(
					    description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
					)
					parser.add_argument(
					    """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
					)
					parser.add_argument(
					    """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
					)
					parser.add_argument("""--vocab_size""", default=3_0522, type=int)
					_UpperCAmelCase	:     int		       = parser.parse_args()

					logger.info(F'''Loading data from {args.data_file}''')
					with open(args.data_file, """rb""") as fp:
										_UpperCAmelCase	:     Union[str, Any]		       = pickle.load(fp)

					logger.info("""Counting occurrences for MLM.""")
					_UpperCAmelCase	:     Dict		       = Counter()
					for tk_ids in data:
										counter.update(tk_ids)
					_UpperCAmelCase	:     str		       = [0] * args.vocab_size
					for k, v in counter.items():
										_UpperCAmelCase	:     List[Any]		       = v

					logger.info(F'''Dump to {args.token_counts_dump}''')
					with open(args.token_counts_dump, """wb""") as handle:
										pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)







@dataclass
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_if import IFPipeline
					from .pipeline_if_imgaimg import IFImgaImgPipeline
					from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
					from .pipeline_if_inpainting import IFInpaintingPipeline
					from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
					from .pipeline_if_superresolution import IFSuperResolutionPipeline
					from .safety_checker import IFSafetyChecker
					from .watermark import IFWatermarker




