




'''simple docstring'''



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class      lowerCAmelCase (			nn.Module):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE = 16     ,	__SCREAMING_SNAKE_CASE = 88     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 1     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 32     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "geglu"     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	Optional[int]:



            '''simple docstring'''




            super().__init__()
            __snake_case            =	nn.ModuleList(
                [
                    TransformeraDModel(
                        num_attention_heads=__SCREAMING_SNAKE_CASE     ,	attention_head_dim=__SCREAMING_SNAKE_CASE     ,	in_channels=__SCREAMING_SNAKE_CASE     ,	num_layers=__SCREAMING_SNAKE_CASE     ,	dropout=__SCREAMING_SNAKE_CASE     ,	norm_num_groups=__SCREAMING_SNAKE_CASE     ,	cross_attention_dim=__SCREAMING_SNAKE_CASE     ,	attention_bias=__SCREAMING_SNAKE_CASE     ,	sample_size=__SCREAMING_SNAKE_CASE     ,	num_vector_embeds=__SCREAMING_SNAKE_CASE     ,	activation_fn=__SCREAMING_SNAKE_CASE     ,	num_embeds_ada_norm=__SCREAMING_SNAKE_CASE     ,	)
                    for _ in range(2	)
                ]	)

            # Variables that can be set by a pipeline:

            # The ratio of transformer1 to transformer2's output states to be combined during inference
            __snake_case            =	0.5

            # The shape of `encoder_hidden_states` is expected to be
            # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
            __snake_case            =	[77, 257]

            # Which transformer to use to encode which condition.
            # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
            __snake_case            =	[1, 0]




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	hidden_states

            __snake_case            =	[]
            __snake_case            =	0
            # attention_mask is not used yet
            for i in range(2	):
                 # for each of the two transformers, pass the corresponding condition tokens
                 __snake_case            =	encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
                 __snake_case            =	self.transformer_index_for_condition[i]
                 __snake_case            =	self.transformers[transformer_index](
                     __SCREAMING_SNAKE_CASE     ,	encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,	timestep=__SCREAMING_SNAKE_CASE     ,	cross_attention_kwargs=__SCREAMING_SNAKE_CASE     ,	return_dict=__SCREAMING_SNAKE_CASE     ,	)[0]
                 encoded_states.append(encoded_state - input_states	)
                 tokens_start += self.condition_lengths[i]

            __snake_case            =	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
            __snake_case            =	output_states + input_states

            if not return_dict:
                 return (output_states,)

            return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE	)













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
)
_lowerCamelCase 					=      logging.getLogger(__name__)

if __name__ == "__main__":
	_lowerCamelCase 					=      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)
	_lowerCamelCase 					=      parser.parse_args()

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

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