


"""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  :     int    					=    logging.getLogger(__name__)

if __name__ == "__main__":
	A  :     str    					=    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_0_5_2_2, type=int)
	A  :     str    					=    parser.parse_args()

	logger.info(f'''Loading data from {args.data_file}''')
	with open(args.data_file, "rb") as fp:
		A  :     int    					=    pickle.load(fp)

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








"""simple docstring"""


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7_68			):
										super().__init__(__a			)
										__lowerCAmelCase       					=    proj_size

										__lowerCAmelCase       					=    CLIPVisionModel(__a			)
										__lowerCAmelCase       					=    PaintByExampleMapper(__a			)
										__lowerCAmelCase       					=    nn.LayerNorm(config.hidden_size			)
										__lowerCAmelCase       					=    nn.Linear(config.hidden_size					,     self.proj_size			)

										# uncondition for scaling
										__lowerCAmelCase       					=    nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)



							def     snake_case       (  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    self.model(pixel_values=__a			)
										__lowerCAmelCase       					=    clip_output.pooler_output
										__lowerCAmelCase       					=    self.mapper(latent_states[:, None]			)
										__lowerCAmelCase       					=    self.final_layer_norm(__a			)
										__lowerCAmelCase       					=    self.proj_out(__a			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a			):
										super().__init__()
										__lowerCAmelCase       					=    (config.num_hidden_layers + 1) // 5
										__lowerCAmelCase       					=    config.hidden_size
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    nn.ModuleList(
										    [
										        BasicTransformerBlock(__a					,     __a					,     __a					,     activation_fn="gelu"					,     attention_bias=__a			)
										        for _ in range(__a			)
										    ]			)



							def     snake_case       (  self					,     __a			):
										for block in self.blocks:
													__lowerCAmelCase       					=    block(__a			)

										return hidden_states




