"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							{}
							A__	     =							tokenizer(example['content']       ,    truncation=UpperCamelCase__				)['input_ids']
							A__	     =							len(example['content']				) / len(output['input_ids']				)
							return output


__lowerCamelCase											=  HfArgumentParser(PretokenizationArguments)
__lowerCamelCase											=  parser.parse_args()
if args.num_workers is None:
				__lowerCamelCase											=  multiprocessing.cpu_count()
__lowerCamelCase											=  AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

__lowerCamelCase											=  time.time()
__lowerCamelCase											=  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											=  time.time()
ds.push_to_hub(args.tokenized_data_repo)


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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase											=  {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  ["GLPNFeatureExtractor"]
				__lowerCamelCase											=  ["GLPNImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  [
				    "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GLPNForDepthEstimation",
				    "GLPNLayer",
				    "GLPNModel",
				    "GLPNPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_glpn import GLPNFeatureExtractor
								from .image_processing_glpn import GLPNImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_glpn import (
								    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GLPNForDepthEstimation,
								    GLPNLayer,
								    GLPNModel,
								    GLPNPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase											=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





