
"""simple docstring"""







import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCamelCase__   					=		4
lowerCamelCase__   					=		3




class  A__       (	_lowerCamelCase):
		pass



def     __lowerCAmelCase				(_UpperCamelCase		):


	for shard in shards:
		for i in range(_snake_case		):
			yield {"i": i, "shard": shard}
def     __lowerCAmelCase				():
	__lowerCAmelCase			:						Optional[int]									=      int(os.environ['RANK']		)
	__lowerCAmelCase			:						Union[str, Any]									=      int(os.environ['WORLD_SIZE']		)

	__lowerCAmelCase			:						Optional[int]									=      ArgumentParser()
	parser.add_argument('--streaming'       ,  type=_snake_case		)
	parser.add_argument('--local_rank'       ,  type=_snake_case		)
	parser.add_argument('--num_workers'       ,  type=_snake_case       ,  default=0		)
	__lowerCAmelCase			:						Tuple									=      parser.parse_args()
	__lowerCAmelCase			:						Optional[Any]									=      args.streaming
	__lowerCAmelCase			:						Any									=      args.num_workers

	__lowerCAmelCase			:						Optional[int]									=      {"shards": [F"shard_{shard_idx}" for shard_idx in range(_snake_case		)]}
	__lowerCAmelCase			:						List[Any]									=      IterableDataset.from_generator(_snake_case       ,  gen_kwargs=_snake_case		)
	if not streaming:
		__lowerCAmelCase			:						Dict									=      Dataset.from_list(list(_snake_case		)		)

	__lowerCAmelCase			:						List[str]									=      split_dataset_by_node(_snake_case       ,  rank=_snake_case       ,  world_size=_snake_case		)
	__lowerCAmelCase			:						Optional[Any]									=      torch.utils.data.DataLoader(_snake_case       ,  num_workers=_snake_case		)

	__lowerCAmelCase			:						Optional[int]									=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	__lowerCAmelCase			:						Dict									=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)		)

	__lowerCAmelCase			:						Optional[int]									=      sum(1 for _ in dataloader		)
	if local_size != expected_local_size:
		raise FailedTestError(F"local_size {local_size} != expected_local_size {expected_local_size}"		)


if __name__ == "__main__":
							main()

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
