






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 						A_					(						lowercase_	,  **lowercase_			)				->List[str]:







 """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	,  **lowercase_	,  )
class 	a_(		lowercase__	):



     """simple docstring"""



     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	ArgumentParser)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		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					:	Union[str, Any]  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	bool  ,  lowerCAmelCase__					:	bool  ,  lowerCAmelCase__					:	bool  ,  lowerCAmelCase__					:	bool  ,  **lowerCAmelCase__					:	int  ,  )							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		dataset
      SCREAMING_SNAKE_CASE				    =		name
      SCREAMING_SNAKE_CASE				    =		cache_dir
      SCREAMING_SNAKE_CASE				    =		beam_pipeline_options
      SCREAMING_SNAKE_CASE				    =		data_dir
      SCREAMING_SNAKE_CASE				    =		all_configs
      SCREAMING_SNAKE_CASE				    =		save_infos
      SCREAMING_SNAKE_CASE				    =		ignore_verifications
      SCREAMING_SNAKE_CASE				    =		force_redownload
      SCREAMING_SNAKE_CASE				    =		config_kwargs





     def     __UpperCamelCase						(		self					:	Optional[int])							->	str:

      """simple docstring"""
      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)
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._dataset, self._name
      SCREAMING_SNAKE_CASE				    =		dataset_module_factory(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		import_main_class(dataset_module.module_path)
      SCREAMING_SNAKE_CASE				    =		[]
      if self._beam_pipeline_options:
       SCREAMING_SNAKE_CASE				    =		beam.options.pipeline_options.PipelineOptions(
           flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(',') if opt])
      else:
       SCREAMING_SNAKE_CASE				    =		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:
       SCREAMING_SNAKE_CASE				    =		os.path.join(builder_cls.get_imported_module_dir()  ,  config.DATASETDICT_INFOS_FILENAME)

       SCREAMING_SNAKE_CASE				    =		Path(lowerCAmelCase__).name + '.py'

       SCREAMING_SNAKE_CASE				    =		os.path.join(lowerCAmelCase__  ,  lowerCAmelCase__)
       if os.path.isfile(lowerCAmelCase__):
        SCREAMING_SNAKE_CASE				    =		os.path.dirname(lowerCAmelCase__)
       elif os.path.isfile(lowerCAmelCase__):
        SCREAMING_SNAKE_CASE				    =		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
       SCREAMING_SNAKE_CASE				    =		os.path.join(lowerCAmelCase__  ,  config.DATASETDICT_INFOS_FILENAME)
       copyfile(lowerCAmelCase__  ,  lowerCAmelCase__)
       print(f'''Dataset Infos file saved at {user_dataset_infos_path}''')






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 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=13  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=[1, 1, 2]  ,						SCREAMING_SNAKE_CASE_=1  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=8  ,						SCREAMING_SNAKE_CASE_=37  ,						SCREAMING_SNAKE_CASE_="gelu_new"  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=512  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=False  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      seq_length
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      use_input_mask
													__UpperCamelCase  	=      use_token_type_ids
													__UpperCamelCase  	=      use_labels
													__UpperCamelCase  	=      vocab_size
													__UpperCamelCase  	=      block_sizes
													__UpperCamelCase  	=      num_decoder_layers
													__UpperCamelCase  	=      d_model
													__UpperCamelCase  	=      n_head
													__UpperCamelCase  	=      d_head
													__UpperCamelCase  	=      d_inner
													__UpperCamelCase  	=      hidden_act
													__UpperCamelCase  	=      hidden_dropout
													__UpperCamelCase  	=      attention_dropout
													__UpperCamelCase  	=      activation_dropout
													__UpperCamelCase  	=      max_position_embeddings
													__UpperCamelCase  	=      type_vocab_size
													__UpperCamelCase  	=      2
													__UpperCamelCase  	=      num_labels
													__UpperCamelCase  	=      num_choices
													__UpperCamelCase  	=      scope
													__UpperCamelCase  	=      initializer_std

													# Used in the tests to check the size of the first attention layer
													__UpperCamelCase  	=      n_head
													# Used in the tests to check the size of the first hidden state
													__UpperCamelCase  	=      self.d_model
													# Used in the tests to check the number of output hidden states/attentions
													__UpperCamelCase  	=      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:
																			__UpperCamelCase  	=      self.num_hidden_layers + 2



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size      )

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

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

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

													__UpperCamelCase  	=      self.get_config()

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    input_mask,
													    sequence_labels,
													    token_labels,
													    choice_labels,
													    fake_token_labels,
													)



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelModel(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.d_model)      )

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

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelBaseModel(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, 2, self.d_model)      )

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

													__UpperCamelCase  	=      False
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, 2, self.d_model)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_labels
													__UpperCamelCase  	=      FunnelForPreTraining(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelForMaskedLM(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_labels
													__UpperCamelCase  	=      FunnelForSequenceClassification(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_choices
													__UpperCamelCase  	=      FunnelForMultipleChoice(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      input_ids.unsqueeze(1      ).expand(-1  ,						self.num_choices  ,						-1      ).contiguous()
													__UpperCamelCase  	=      token_type_ids.unsqueeze(1      ).expand(-1  ,						self.num_choices  ,						-1      ).contiguous()
													__UpperCamelCase  	=      input_mask.unsqueeze(1      ).expand(-1  ,						self.num_choices  ,						-1      ).contiguous()
													__UpperCamelCase  	=      model(
													    SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_  ,						)
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.num_labels
													__UpperCamelCase  	=      FunnelForTokenClassification(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelForQuestionAnswering(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(
													    SCREAMING_SNAKE_CASE_  ,						attention_mask=SCREAMING_SNAKE_CASE_  ,						token_type_ids=SCREAMING_SNAKE_CASE_  ,						start_positions=SCREAMING_SNAKE_CASE_  ,						end_positions=SCREAMING_SNAKE_CASE_  ,						)
													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      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				
													)  	=      config_and_inputs
													__UpperCamelCase  	=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
													return config, inputs_dict






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	(
							    (
							        FunnelModel,
							        FunnelForMaskedLM,
							        FunnelForPreTraining,
							        FunnelForQuestionAnswering,
							        FunnelForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_snake_case  		=	(
							    {
							        '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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=False      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      super()._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						return_labels=SCREAMING_SNAKE_CASE_      )

													if return_labels:
																			if model_class in get_values(SCREAMING_SNAKE_CASE_      ):
																									__UpperCamelCase  	=      torch.zeros(
																									    (self.model_tester.batch_size, self.model_tester.seq_length)  ,						dtype=torch.long  ,						device=SCREAMING_SNAKE_CASE_      )
													return inputs_dict



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelModelTester(self      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> str:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''weight'''      ) and module.weight is not None:
																			module.weight.data.fill_(3      )
													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''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(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) and getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) is not None:
																									__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									weight.data.fill_(3      )






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	(
							    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
							)



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelModelTester(self  ,						base=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_base_model(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase  	=      True

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''weight'''      ) and module.weight is not None:
																			module.weight.data.fill_(3      )
													if hasattr(SCREAMING_SNAKE_CASE_  ,						'''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(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) and getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) is not None:
																									__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																									weight.data.fill_(3      )






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

							"""simple docstring"""






							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      13
													__UpperCamelCase  	=      7
													__UpperCamelCase  	=      torch.arange(0  ,						batch_size * sequence_length      ).long().reshape(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
													__UpperCamelCase  	=      torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]      )

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

													__UpperCamelCase  	=      torch.tensor(2_3_4_4.8_3_5_2      )
													__UpperCamelCase  	=      torch.tensor(0.8_0_5_2      )
													self.assertTrue(torch.allclose(output.sum()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )
													self.assertTrue(torch.allclose(output.mean()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )

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

													__UpperCamelCase  	=      torch.tensor(2_3_4_3.8_4_2_5      )
													__UpperCamelCase  	=      torch.tensor(0.8_0_4_9      )
													self.assertTrue(torch.allclose(output.sum()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )
													self.assertTrue(torch.allclose(output.mean()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )



							@slow
							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      FunnelTokenizer.from_pretrained('''huggingface/funnel-small'''      )
													__UpperCamelCase  	=      FunnelModel.from_pretrained('''huggingface/funnel-small'''      )
													__UpperCamelCase  	=      tokenizer('''Hello! I am the Funnel Transformer model.'''  ,						return_tensors='''pt'''      )
													__UpperCamelCase  	=      model(**SCREAMING_SNAKE_CASE_      )[0]

													__UpperCamelCase  	=      torch.tensor(2_3_5.7_2_4_6      )
													__UpperCamelCase  	=      torch.tensor(0.0_2_5_6      )
													self.assertTrue(torch.allclose(output.sum()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )
													self.assertTrue(torch.allclose(output.mean()  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )




