


'''simple docstring'''





import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_PRETRAINING_MAPPING,
					    FunnelBaseModel,
					    FunnelForMaskedLM,
					    FunnelForMultipleChoice,
					    FunnelForPreTraining,
					    FunnelForQuestionAnswering,
					    FunnelForSequenceClassification,
					    FunnelForTokenClassification,
					    FunnelModel,
					)
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Tuple ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[str]=13 ,						lowerCAmelCase_       : int=7 ,						lowerCAmelCase_       : Union[str, Any]=True ,						lowerCAmelCase_       : Union[str, Any]=True ,						lowerCAmelCase_       : Dict=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[str]=99 ,						lowerCAmelCase_       : Dict=[1, 1, 2] ,						lowerCAmelCase_       : Optional[int]=1 ,						lowerCAmelCase_       : List[str]=32 ,						lowerCAmelCase_       : Optional[int]=4 ,						lowerCAmelCase_       : Dict=8 ,						lowerCAmelCase_       : Tuple=37 ,						lowerCAmelCase_       : Union[str, Any]="gelu_new" ,						lowerCAmelCase_       : int=0.1 ,						lowerCAmelCase_       : List[str]=0.1 ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : str=512 ,						lowerCAmelCase_       : int=3 ,						lowerCAmelCase_       : Optional[int]=0.02 ,						lowerCAmelCase_       : Dict=3 ,						lowerCAmelCase_       : Union[str, Any]=4 ,						lowerCAmelCase_       : Optional[int]=None ,						lowerCAmelCase_       : List[str]=False ,						)			->		int:
						__A=  parent
						__A=  batch_size
						__A=  seq_length
						__A=  is_training
						__A=  use_input_mask
						__A=  use_token_type_ids
						__A=  use_labels
						__A=  vocab_size
						__A=  block_sizes
						__A=  num_decoder_layers
						__A=  d_model
						__A=  n_head
						__A=  d_head
						__A=  d_inner
						__A=  hidden_act
						__A=  hidden_dropout
						__A=  attention_dropout
						__A=  activation_dropout
						__A=  max_position_embeddings
						__A=  type_vocab_size
						__A=  2
						__A=  num_labels
						__A=  num_choices
						__A=  scope
						__A=  initializer_std

						# Used in the tests to check the size of the first attention layer
						__A=  n_head
						# Used in the tests to check the size of the first hidden state
						__A=  self.d_model
						# Used in the tests to check the number of output hidden states/attentions
						__A=  sum(self.block_sizes  ) + (0 if base else self.num_decoder_layers)
						# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
						# the last hidden state of the first block (which is the first hidden state of the decoder).
						if not base:
									__A=  self.num_hidden_layers + 2
			def 				lowerCAmelCase    (     self       : List[str]  )			->		List[str]:
						__A=  ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size  )

						__A=  None
						if self.use_input_mask:
									__A=  ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2  )

						__A=  None
						if self.use_token_type_ids:
									__A=  ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size  )

						__A=  None
						__A=  None
						__A=  None
						if self.use_labels:
									__A=  ids_tensor([self.batch_size] ,						self.type_sequence_label_size  )
									__A=  ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels  )
									__A=  ids_tensor([self.batch_size] ,						self.num_choices  )
									__A=  ids_tensor([self.batch_size, self.seq_length] ,						1  )

						__A=  self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    fake_token_labels,
						)
			def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[Any]:
						return FunnelConfig(
						    vocab_size=self.vocab_size ,						block_sizes=self.block_sizes ,						num_decoder_layers=self.num_decoder_layers ,						d_model=self.d_model ,						n_head=self.n_head ,						d_head=self.d_head ,						d_inner=self.d_inner ,						hidden_act=self.hidden_act ,						hidden_dropout=self.hidden_dropout ,						attention_dropout=self.attention_dropout ,						activation_dropout=self.activation_dropout ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						initializer_std=self.initializer_std ,						)
			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : str ,						)			->		Union[str, Any]:
						__A=  FunnelModel(config=_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase  )
						__A=  model(_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase  )
						__A=  model(_lowerCAmelCase  )
						self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.d_model)  )

						__A=  False
						__A=  model(_lowerCAmelCase  )
						self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.d_model)  )

						__A=  False
						__A=  model(_lowerCAmelCase  )
						self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.d_model)  )
			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Optional[int] ,						)			->		Dict:
						__A=  FunnelBaseModel(config=_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase  )
						__A=  model(_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase  )
						__A=  model(_lowerCAmelCase  )
						self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, 2, self.d_model)  )

						__A=  False
						__A=  model(_lowerCAmelCase  )
						self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, 3, self.d_model)  )

						__A=  False
						__A=  model(_lowerCAmelCase  )
						self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, 2, self.d_model)  )
			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : int ,						)			->		str:
						__A=  self.num_labels
						__A=  FunnelForPreTraining(config=_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length)  )
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Any ,						)			->		List[Any]:
						__A=  FunnelForMaskedLM(config=_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)  )
			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : List[Any] ,						)			->		List[str]:
						__A=  self.num_labels
						__A=  FunnelForSequenceClassification(_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)  )
			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : List[str] ,						)			->		List[Any]:
						__A=  self.num_choices
						__A=  FunnelForMultipleChoice(config=_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  input_ids.unsqueeze(1  ).expand(-1 ,						self.num_choices ,						-1  ).contiguous()
						__A=  token_type_ids.unsqueeze(1  ).expand(-1 ,						self.num_choices ,						-1  ).contiguous()
						__A=  input_mask.unsqueeze(1  ).expand(-1 ,						self.num_choices ,						-1  ).contiguous()
						__A=  model(
						    _lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						)
						self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices)  )
			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Union[str, Any] ,						)			->		str:
						__A=  self.num_labels
						__A=  FunnelForTokenClassification(config=_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)  )
			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Dict ,						)			->		str:
						__A=  FunnelForQuestionAnswering(config=_lowerCAmelCase  )
						model.to(_lowerCAmelCase  )
						model.eval()
						__A=  model(
						    _lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						start_positions=_lowerCAmelCase ,						end_positions=_lowerCAmelCase ,						)
						self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length)  )
						self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length)  )

			def 				lowerCAmelCase    (     self       : Dict  )			->		str:
						__A=  self.prepare_config_and_inputs()
						(
						    (
						    __A    
						),  (
						    __A    
						),  (
						    __A    
						),  (
						    __A    
						),  (
						    __A    
						),  (
						    __A    
						),  (
						    __A    
						),  (
						    __A    
						),      
						)=  config_and_inputs
						__A=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
						return config, inputs_dict



@require_torch
class 	a__				(				A__			,     A__			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Dict							= False
			A	:							str							= False
			A	:							str							= (
			    (
			        FunnelModel,
			        FunnelForMaskedLM,
			        FunnelForPreTraining,
			        FunnelForQuestionAnswering,
			        FunnelForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			A	:							Dict							= (
			    {
			        '''feature-extraction''': (FunnelBaseModel, FunnelModel),
			        '''fill-mask''': FunnelForMaskedLM,
			        '''question-answering''': FunnelForQuestionAnswering,
			        '''text-classification''': FunnelForSequenceClassification,
			        '''token-classification''': FunnelForTokenClassification,
			        '''zero-shot''': FunnelForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : str=False  )			->		Dict:
						__A=  super()._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase ,						return_labels=_lowerCAmelCase  )

						if return_labels:
									if model_class in get_values(_lowerCAmelCase  ):
												__A=  torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.seq_length) ,						dtype=torch.long ,						device=_lowerCAmelCase  )
						return inputs_dict
			def 				lowerCAmelCase    (     self       : int  )			->		str:
						__A=  FunnelModelTester(self  )
						__A=  ConfigTester(self ,						config_class=_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[Any]:
						self.config_tester.run_common_tests()
			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Dict:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Union[str, Any]:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Dict:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : List[str]  )			->		List[str]:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase  )

			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : List[str]  )			->		Optional[Any]:
						if hasattr(_lowerCAmelCase ,						'weight'  ) and module.weight is not None:
									module.weight.data.fill_(3  )
						if hasattr(_lowerCAmelCase ,						'bias'  ) and module.bias is not None:
									module.bias.data.fill_(3  )

						for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
									if hasattr(_lowerCAmelCase ,						_lowerCAmelCase  ) and getattr(_lowerCAmelCase ,						_lowerCAmelCase  ) is not None:
												__A=  getattr(_lowerCAmelCase ,						_lowerCAmelCase  )
												weight.data.fill_(3  )



@require_torch
class 	a__				(				A__			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Optional[int]							= False
			A	:							List[Any]							= False
			A	:							Optional[Any]							= (
			    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
			)
			def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
						__A=  FunnelModelTester(self ,						base=_lowerCAmelCase  )
						__A=  ConfigTester(self ,						config_class=_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Union[str, Any]:
						self.config_tester.run_common_tests()
			def 				lowerCAmelCase    (     self       : Tuple  )			->		str:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_base_model(*_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Optional[Any]:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
						__A=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase  )
			def 				lowerCAmelCase    (     self       : int  )			->		Union[str, Any]:
						__A,  __A=  self.model_tester.prepare_config_and_inputs_for_common()
						__A=  True

						for model_class in self.all_model_classes:
									if model_class.__name__ == "FunnelBaseModel":
												continue
									__A=  model_class(_lowerCAmelCase  )
									model.to(_lowerCAmelCase  )
									model.train()
									__A=  self._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase ,						return_labels=_lowerCAmelCase  )
									__A=  model(**_lowerCAmelCase  ).loss
									loss.backward()

			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Union[str, Any]  )			->		List[Any]:
						if hasattr(_lowerCAmelCase ,						'weight'  ) and module.weight is not None:
									module.weight.data.fill_(3  )
						if hasattr(_lowerCAmelCase ,						'bias'  ) and module.bias is not None:
									module.bias.data.fill_(3  )

						for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
									if hasattr(_lowerCAmelCase ,						_lowerCAmelCase  ) and getattr(_lowerCAmelCase ,						_lowerCAmelCase  ) is not None:
												__A=  getattr(_lowerCAmelCase ,						_lowerCAmelCase  )
												weight.data.fill_(3  )



@require_torch
@require_sentencepiece
@require_tokenizers
class 	a__				(				unittest.TestCase      ):





			'''simple docstring'''

			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Tuple:
						__A=  13
						__A=  7
						__A=  torch.arange(0 ,						batch_size * sequence_length  ).long().reshape(_lowerCAmelCase ,						_lowerCAmelCase  )
						__A=  [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
						__A=  torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]  )

						__A=  FunnelModel.from_pretrained('sgugger/funnel-random-tiny'  )
						__A=  model(_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase  )[0].abs()

						__A=  torch.tensor(2_344.8_352  )
						__A=  torch.tensor(0.80_52  )
						self.assertTrue(torch.allclose(output.sum() ,						_lowerCAmelCase ,						atol=1E-4  )  )
						self.assertTrue(torch.allclose(output.mean() ,						_lowerCAmelCase ,						atol=1E-4  )  )

						__A=  torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]  )
						__A=  model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase  )[0].abs()

						__A=  torch.tensor(2_343.8_425  )
						__A=  torch.tensor(0.80_49  )
						self.assertTrue(torch.allclose(output.sum() ,						_lowerCAmelCase ,						atol=1E-4  )  )
						self.assertTrue(torch.allclose(output.mean() ,						_lowerCAmelCase ,						atol=1E-4  )  )

			@slow
			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		str:
						__A=  FunnelTokenizer.from_pretrained('huggingface/funnel-small'  )
						__A=  FunnelModel.from_pretrained('huggingface/funnel-small'  )
						__A=  tokenizer('Hello! I am the Funnel Transformer model.' ,						return_tensors='pt'  )
						__A=  model(**_lowerCAmelCase  )[0]

						__A=  torch.tensor(2_35.72_46  )
						__A=  torch.tensor(0.02_56  )
						self.assertTrue(torch.allclose(output.sum() ,						_lowerCAmelCase ,						atol=1E-4  )  )
						self.assertTrue(torch.allclose(output.mean() ,						_lowerCAmelCase ,						atol=1E-4  )  )







'''simple docstring'''





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,**_SCREAMING_SNAKE_CASE				:   Tuple   ):






			"""simple docstring"""




			return RunBeamCommand(
			    args.dataset,args.name,args.cache_dir,args.beam_pipeline_options,args.data_dir,args.all_configs,args.save_info or args.save_infos,args.ignore_verifications,args.force_redownload,**_SCREAMING_SNAKE_CASE,)






class 	a__				(				a_      ):





			'''simple docstring'''

			@staticmethod
			def 				lowerCAmelCase    (     lowerCAmelCase_       : ArgumentParser  )			->		Tuple:
						__A=  parser.add_parser('run_beam' ,						help='Run a Beam dataset processing pipeline'  )
						run_beam_parser.add_argument('dataset' ,						type=lowerCAmelCase_ ,						help='Name of the dataset to download'  )
						run_beam_parser.add_argument('--name' ,						type=lowerCAmelCase_ ,						default=lowerCAmelCase_ ,						help='Dataset config name'  )
						run_beam_parser.add_argument(
						    '--cache_dir' ,						type=lowerCAmelCase_ ,						default=lowerCAmelCase_ ,						help='Cache directory where the datasets are stored' ,						)
						run_beam_parser.add_argument(
						    '--beam_pipeline_options' ,						type=lowerCAmelCase_ ,						default='' ,						help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`' ,						)
						run_beam_parser.add_argument(
						    '--data_dir' ,						type=lowerCAmelCase_ ,						default=lowerCAmelCase_ ,						help='Can be used to specify a manual directory to get the files from' ,						)
						run_beam_parser.add_argument('--all_configs' ,						action='store_true' ,						help='Test all dataset configurations'  )
						run_beam_parser.add_argument('--save_info' ,						action='store_true' ,						help='Save the dataset infos file'  )
						run_beam_parser.add_argument(
						    '--ignore_verifications' ,						action='store_true' ,						help='Run the test without checksums and splits checks'  )
						run_beam_parser.add_argument('--force_redownload' ,						action='store_true' ,						help='Force dataset redownload'  )
						# aliases
						run_beam_parser.add_argument('--save_infos' ,						action='store_true' ,						help='alias for save_info'  )
						run_beam_parser.set_defaults(func=lowerCAmelCase_  )
			def __init__(     self       : Any ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						lowerCAmelCase_       : bool ,						**lowerCAmelCase_       : Tuple ,						)			->		List[Any]:
						__A=  dataset
						__A=  name
						__A=  cache_dir
						__A=  beam_pipeline_options
						__A=  data_dir
						__A=  all_configs
						__A=  save_infos
						__A=  ignore_verifications
						__A=  force_redownload
						__A=  config_kwargs

			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Optional[Any]:
						import apache_beam as beam

						if self._name is not None and self._all_configs:
									print('Both parameters `name` and `all_configs` can\'t be used at once.'  )
									exit(1  )
						__A,  __A=  self._dataset, self._name
						__A=  dataset_module_factory(lowerCAmelCase_  )
						__A=  import_main_class(dataset_module.module_path  )
						__A=  []
						if self._beam_pipeline_options:
									__A=  beam.options.pipeline_options.PipelineOptions(
									    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','  ) if opt]  )
						else:
									__A=  None
						if self._all_configs and len(builder_cls.BUILDER_CONFIGS  ) > 0:
									for builder_config in builder_cls.BUILDER_CONFIGS:
												builders.append(
												    builder_cls(
												        config_name=builder_config.name ,						data_dir=self._data_dir ,						hash=dataset_module.hash ,						beam_options=lowerCAmelCase_ ,						cache_dir=self._cache_dir ,						base_path=dataset_module.builder_kwargs.get('base_path'  ) ,						)  )
						else:
									builders.append(
									    builder_cls(
									        config_name=lowerCAmelCase_ ,						data_dir=self._data_dir ,						beam_options=lowerCAmelCase_ ,						cache_dir=self._cache_dir ,						base_path=dataset_module.builder_kwargs.get('base_path'  ) ,						**self._config_kwargs ,						)  )

						for builder in builders:
									builder.download_and_prepare(
									    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
									    if not self._force_redownload
									    else DownloadMode.FORCE_REDOWNLOAD ,						download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH  ) ,						verification_mode=VerificationMode.NO_CHECKS
									    if self._ignore_verifications
									    else VerificationMode.ALL_CHECKS ,						try_from_hf_gcs=lowerCAmelCase_ ,						)
									if self._save_infos:
												builder._save_infos()

						print('Apache beam run successful.'  )

						# If save_infos=True, the dataset infos file is created next to the loaded module file.
						# Let's move it to the original directory of the dataset script, to allow the user to
						# upload them on S3 at the same time afterwards.
						if self._save_infos:
									__A=  os.path.join(builder_cls.get_imported_module_dir() ,						config.DATASETDICT_INFOS_FILENAME  )

									__A=  Path(lowerCAmelCase_  ).name + '.py'

									__A=  os.path.join(lowerCAmelCase_ ,						lowerCAmelCase_  )
									if os.path.isfile(lowerCAmelCase_  ):
												__A=  os.path.dirname(lowerCAmelCase_  )
									elif os.path.isfile(lowerCAmelCase_  ):
												__A=  path
									else:  # in case of a remote dataset
												print(F"""Dataset Infos file saved at {dataset_infos_path}"""  )
												exit(1  )

									# Move datasetinfo back to the user
									__A=  os.path.join(lowerCAmelCase_ ,						config.DATASETDICT_INFOS_FILENAME  )
									copyfile(lowerCAmelCase_ ,						lowerCAmelCase_  )
									print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""  )



