from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_tf_available():
	import tensorflow as tf

	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
	from ..tf_utils import stable_softmax

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE		:     int         =	logging.get_logger(__name__)

@add_end_docstrings(__a				)
class        UpperCamelCase						(				__a				):





						def __init__(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				int:
												super().__init__(*__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE						)
												requires_backends(self			,     """vision"""						)
												self.check_model_type(
												    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
												    if self.framework == """tf"""
												    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING						)





						def        A_							(self			,     __UpperCamelCase=None						)				->				Union[str, Any]:
												UpperCamelCase_		:							Optional[Any]  =		{}
												if top_k is not None:
																		UpperCamelCase_		:							str  =		top_k
												return {}, {}, postprocess_params





						def __call__(self			,     __UpperCamelCase			,     **__UpperCamelCase						)				->				Dict:
												return super().__call__(__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE						)





						def        A_							(self			,     __UpperCamelCase						)				->				Optional[int]:
												UpperCamelCase_		:							Dict  =		load_image(__SCREAMING_SNAKE_CASE						)
												UpperCamelCase_		:							List[str]  =		self.image_processor(images=__SCREAMING_SNAKE_CASE			,     return_tensors=self.framework						)
												return model_inputs





						def        A_							(self			,     __UpperCamelCase						)				->				str:
												UpperCamelCase_		:							List[Any]  =		self.model(**__SCREAMING_SNAKE_CASE						)
												return model_outputs





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=5						)				->				List[Any]:




												if top_k > self.model.config.num_labels:
																		UpperCamelCase_		:							int  =		self.model.config.num_labels

												if self.framework == "pt":
																		UpperCamelCase_		:							List[str]  =		model_outputs.logits.softmax(-1						)[0]
																		UpperCamelCase_,UpperCamelCase_		:							str  =		probs.topk(__SCREAMING_SNAKE_CASE						)
												elif self.framework == "tf":
																		UpperCamelCase_		:							int  =		stable_softmax(model_outputs.logits			,     axis=-1						)[0]
																		UpperCamelCase_		:							List[Any]  =		tf.math.top_k(__SCREAMING_SNAKE_CASE			,     k=__SCREAMING_SNAKE_CASE						)
																		UpperCamelCase_,UpperCamelCase_		:							Dict  =		topk.values.numpy(), topk.indices.numpy()
												else:
																		raise ValueError(f'''Unsupported framework: {self.framework}'''						)

												UpperCamelCase_		:							Dict  =		scores.tolist()
												UpperCamelCase_		:							Any  =		ids.tolist()
												return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE						)]









'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


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

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

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[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""")

UpperCAmelCase					:			List[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""")






