



"""simple docstring"""

import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__       ):
											super().__init__()
											snake_case_					:		List[Any] 					=					nn.ModuleList(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = False   ,		lowercase__ = True   ,		):
											for i, (image, scale, controlnet) in enumerate(zip(lowercase__   ,		lowercase__   ,		self.nets       )       ):
																snake_case_					,			snake_case_					:		str 					=					controlnet(
																    lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		)

																# merge samples
																if i == 0:
																					snake_case_					,			snake_case_					:		Tuple 					=					down_samples, mid_sample
																else:
																					snake_case_					:		int 					=					[
																					    samples_prev + samples_curr
																					    for samples_prev, samples_curr in zip(lowercase__   ,		lowercase__       )
																					]
																					mid_block_res_sample += mid_sample

											return down_block_res_samples, mid_block_res_sample







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = False   ,		lowercase__ = None   ,		):
											snake_case_					:		Optional[int] 					=					0
											snake_case_					:		List[str] 					=					save_directory
											for controlnet in self.nets:
																controlnet.save_pretrained(
																    lowercase__   ,		is_main_process=lowercase__   ,		save_function=lowercase__   ,		safe_serialization=lowercase__   ,		variant=lowercase__   ,		)

																idx += 1
																snake_case_					:		Any 					=					model_path_to_save + f'_{idx}'







						@classmethod
						def 						__UpperCamelCase							(cls   ,		lowercase__   ,		**lowercase__       ):
											snake_case_					:		Any 					=					0
											snake_case_					:		Union[str, Any] 					=					[]

											# load controlnet and append to list until no controlnet directory exists anymore
											# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
											# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
											snake_case_					:		int 					=					pretrained_model_path
											while os.path.isdir(lowercase__       ):
																snake_case_					:		Any 					=					ControlNetModel.from_pretrained(lowercase__   ,		**lowercase__       )
																controlnets.append(lowercase__       )

																idx += 1
																snake_case_					:		Dict 					=					pretrained_model_path + f'_{idx}'

											logger.info(f'{len(lowercase__       )} controlnets loaded from {pretrained_model_path}.'       )

											if len(lowercase__       ) == 0:
																raise ValueError(
																    f'No ControlNets found under {os.path.dirname(lowercase__       )}. Expected at least {pretrained_model_path + "_0"}.'       )

											return cls(lowercase__       )









"""simple docstring"""

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
)
a_			   =					logging.getLogger(__name__)

if __name__ == "__main__":
			a_			   =					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=30522, type=int)
			a_			   =					parser.parse_args()

			logger.info(F'''Loading data from {args.data_file}''')
			with open(args.data_file, '''rb''') as fp:
						a_			   =					pickle.load(fp)

			logger.info('''Counting occurrences for MLM.''')
			a_			   =					Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			a_			   =					[0] * args.vocab_size
			for k, v in counter.items():
						a_			   =					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)




