


"""simple docstring"""







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class 				__A    (	nn.Module      ):






 """simple docstring"""




 def __init__(		self			,   __A = 16			,   __A = 88			,   __A = None			,   __A = 1			,   __A = 0.0			,   __A = 32			,   __A = None			,   __A = False			,   __A = None			,   __A = None			,   __A = "geglu"			,   __A = None			,   ) ->					List[Any]:
     super().__init__()
     a					=nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=__A			,   attention_head_dim=__A			,   in_channels=__A			,   num_layers=__A			,   dropout=__A			,   norm_num_groups=__A			,   cross_attention_dim=__A			,   attention_bias=__A			,   sample_size=__A			,   num_vector_embeds=__A			,   activation_fn=__A			,   num_embeds_ada_norm=__A			,   )
             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
     a					=0.5

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





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A=None			,   __A=None			,   __A=None			,   __A = True			,   ) ->					Optional[Any]:
     a					=hidden_states

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

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

     if not return_dict:
         return (output_states,)

     return TransformeraDModelOutput(sample=__A						)



"""simple docstring"""







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def _A						(						lowercase		):







    """simple docstring"""
    a					={}
    a					=tokenizer(example['''content''']	,   truncation=lowercase		)['''input_ids''']
    a					=len(example['''content''']		) / len(output['''input_ids''']		)
    return output


lowerCamelCase_       :       Optional[int]      						=  HfArgumentParser(PretokenizationArguments)
lowerCamelCase_       :       Optional[Any]      						=  parser.parse_args()
if args.num_workers is None:
       lowerCamelCase_       :       Tuple      						=  multiprocessing.cpu_count()
lowerCamelCase_       :       Any      						=  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCamelCase_       :       Any      						=  time.time()
lowerCamelCase_       :       int      						=  load_dataset(args.dataset_name, split="""train""")
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       List[str]      						=  time.time()
lowerCamelCase_       :       str      						=  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'Dataset tokenized in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       Union[str, Any]      						=  time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')