

from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor







class  lowercase				(       lowercase_	,  lowercase_			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			'''pixel_values'''
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			False
				__SCREAMING_SNAKE_CASE					: Dict       			=			TimmBackboneConfig




				def __init__(					self      ,   snake_case      ,   **snake_case       ):
											requires_backends(self      ,   'timm'       )
											super().__init__(snake_case       )
											snake_case_							=    config

											if config.backbone is None:
																		raise ValueError('backbone is not set in the config. Please set it to a timm model name.'       )

											if config.backbone not in timm.list_models():
																		raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''       )

											if hasattr(snake_case      ,   'out_features'       ) and config.out_features is not None:
																		raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'       )

											snake_case_							=    getattr(snake_case      ,   'use_pretrained_backbone'      ,   snake_case       )
											if pretrained is None:
																		raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'       )

											# We just take the final layer by default. This matches the default for the transformers models.
											snake_case_							=    config.out_indices if getattr(snake_case      ,   'out_indices'      ,   snake_case       ) is not None else (-1,)

											snake_case_							=    timm.create_model(
											    config.backbone      ,   pretrained=snake_case      ,   features_only=config.features_only      ,   in_chans=config.num_channels      ,   out_indices=snake_case      ,   **snake_case      ,   )
											# These are used to control the output of the model when called. If output_hidden_states is True, then
											# return_layers is modified to include all layers.
											snake_case_							=    self._backbone.return_layers
											snake_case_							=    {layer['module']: str(snake_case       ) for i, layer in enumerate(self._backbone.feature_info.info       )}
											super()._init_backbone(snake_case       )




				@classmethod
				def     a							(					cls      ,   snake_case      ,   *snake_case      ,   **snake_case       ):
											requires_backends(cls      ,   ['vision', 'timm']       )
											from ...models.timm_backbone import TimmBackboneConfig

											snake_case_							=    kwargs.pop('config'      ,   TimmBackboneConfig()       )

											snake_case_							=    kwargs.pop('use_timm_backbone'      ,   snake_case       )
											if not use_timm:
																		raise ValueError('use_timm_backbone must be True for timm backbones'       )

											snake_case_							=    kwargs.pop('num_channels'      ,   config.num_channels       )
											snake_case_							=    kwargs.pop('features_only'      ,   config.features_only       )
											snake_case_							=    kwargs.pop('use_pretrained_backbone'      ,   config.use_pretrained_backbone       )
											snake_case_							=    kwargs.pop('out_indices'      ,   config.out_indices       )
											snake_case_							=    TimmBackboneConfig(
											    backbone=snake_case      ,   num_channels=snake_case      ,   features_only=snake_case      ,   use_pretrained_backbone=snake_case      ,   out_indices=snake_case      ,   )
											return super()._from_config(snake_case      ,   **snake_case       )




				def     a							(					self      ,   snake_case       ):
											pass




				def     a							(					self      ,   snake_case      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											snake_case_							=    return_dict if return_dict is not None else self.config.use_return_dict
											snake_case_							=    (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											snake_case_							=    output_attentions if output_attentions is not None else self.config.output_attentions

											if output_attentions:
																		raise ValueError('Cannot output attentions for timm backbones at the moment'       )

											if output_hidden_states:
																		# We modify the return layers to include all the stages of the backbone
																		snake_case_							=    self._all_layers
																		snake_case_							=    self._backbone(snake_case      ,   **snake_case       )
																		snake_case_							=    self._return_layers
																		snake_case_							=    tuple(hidden_states[i] for i in self.out_indices       )
											else:
																		snake_case_							=    self._backbone(snake_case      ,   **snake_case       )
																		snake_case_							=    None

											snake_case_							=    tuple(snake_case       )
											snake_case_							=    tuple(snake_case       ) if hidden_states is not None else None

											if not return_dict:
																		snake_case_							=    (feature_maps,)
																		if output_hidden_states:
																									snake_case_							=    output + (hidden_states,)
																		return output

											return BackboneOutput(feature_maps=snake_case      ,   hidden_states=snake_case      ,   attentions=snake_case       )







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






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


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

_UpperCAmelCase		:   Optional[int]										=       time.time()
_UpperCAmelCase		:   List[str]										=       load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       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''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


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




