

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class     a__  (    a_	):
			"""simple docstring"""







			__lowerCamelCase       				=				None
			__lowerCamelCase       				=				None
			__lowerCamelCase       				=				None
			__lowerCamelCase       				=				None

class     a__  (    a_	):
			"""simple docstring"""







			def __init__(			self					,					lowercase=1					,					lowercase=0					,					lowercase=2					,					lowercase=512					,					lowercase="cls"					,					lowercase=False					,					lowercase=True					,					**lowercase					,					)     ->  Optional[Any]:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCamelCase					,					bos_token_id=_lowerCamelCase					,					eos_token_id=_lowerCamelCase					,					**_lowerCamelCase				)
							A__						=	project_dim
							A__						=	pooler_fn
							A__						=	learn_encoder
							A__						=	use_attention_mask

class     a__  (    a_	):
			"""simple docstring"""







			__lowerCamelCase       				=				[R"pooler", R"logit_scale"]
			__lowerCamelCase       				=				[R"position_ids", R"predictions.decoder.bias"]
			__lowerCamelCase       				=				"roberta"
			__lowerCamelCase       				=				RobertaSeriesConfig


			def __init__(			self					,					lowercase				)     ->  Tuple:
							'''simple docstring'''






							super().__init__(_lowerCamelCase				)
							A__						=	XLMRobertaModel(_lowerCamelCase				)
							A__						=	nn.Linear(config.hidden_size					,					config.project_dim				)
							A__						=	getattr(_lowerCamelCase					,					"has_pre_transformation"					,					_lowerCamelCase				)
							if self.has_pre_transformation:
											A__						=	nn.Linear(config.hidden_size					,					config.project_dim				)
											A__						=	nn.LayerNorm(config.hidden_size					,					eps=config.layer_norm_eps				)
							self.post_init()


			def    UpperCamelCase						(			self					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					)     ->  List[Any]:
							'''simple docstring'''






							A__						=	return_dict if return_dict is not None else self.config.use_return_dict

							A__						=	self.base_model(
							    input_ids=_lowerCamelCase					,					attention_mask=_lowerCamelCase					,					token_type_ids=_lowerCamelCase					,					position_ids=_lowerCamelCase					,					head_mask=_lowerCamelCase					,					inputs_embeds=_lowerCamelCase					,					encoder_hidden_states=_lowerCamelCase					,					encoder_attention_mask=_lowerCamelCase					,					output_attentions=_lowerCamelCase					,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states					,					return_dict=_lowerCamelCase					,					)

							if self.has_pre_transformation:
											A__						=	outputs["hidden_states"][-2]
											A__						=	self.pre_LN(_lowerCamelCase				)
											A__						=	self.transformation_pre(_lowerCamelCase				)

											return TransformationModelOutput(
											    projection_state=_lowerCamelCase					,					last_hidden_state=outputs.last_hidden_state					,					hidden_states=outputs.hidden_states					,					attentions=outputs.attentions					,					)
							else:
											A__						=	self.transformation(outputs.last_hidden_state				)
											return TransformationModelOutput(
											    projection_state=_lowerCamelCase					,					last_hidden_state=outputs.last_hidden_state					,					hidden_states=outputs.hidden_states					,					attentions=outputs.attentions					,					)









"""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


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       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    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       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()


