


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        UpperCAmelCase__			:



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=7   ,  A_=True   ,  A_=True   ,  A_=True   ,  A_=True   ,  A_=99   ,  A_=[1, 1, 2]   ,  A_=1   ,  A_=32   ,  A_=4   ,  A_=8   ,  A_=37   ,  A_="gelu_new"   ,  A_=0.1   ,  A_=0.1   ,  A_=0.0   ,  A_=512   ,  A_=3   ,  A_=0.02   ,  A_=3   ,  A_=4   ,  A_=None   ,  A_=False   ,  )    ->			List[str]:
         __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       )    ->			Optional[Any]:
         __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       )    ->			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 _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			List[str]:
         __UpperCamelCase       				=FunnelModel(config=lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         model.eval()
         __UpperCamelCase       				=model(lowerCAmelCase_   ,  attention_mask=lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_       )
         __UpperCamelCase       				=model(lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_       )
         __UpperCamelCase       				=model(lowerCAmelCase_       )
         self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.d_model)       )

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

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






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Optional[Any]:
         __UpperCamelCase       				=FunnelBaseModel(config=lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         model.eval()
         __UpperCamelCase       				=model(lowerCAmelCase_   ,  attention_mask=lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_       )
         __UpperCamelCase       				=model(lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_       )
         __UpperCamelCase       				=model(lowerCAmelCase_       )
         self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, 2, self.d_model)       )

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

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






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Dict:
         __UpperCamelCase       				=self.num_labels
         __UpperCamelCase       				=FunnelForPreTraining(config=lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         model.eval()
         __UpperCamelCase       				=model(lowerCAmelCase_   ,  attention_mask=lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_   ,  labels=lowerCAmelCase_       )
         self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length)       )






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Any:
         __UpperCamelCase       				=FunnelForMaskedLM(config=lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         model.eval()
         __UpperCamelCase       				=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 _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Tuple:
         __UpperCamelCase       				=self.num_labels
         __UpperCamelCase       				=FunnelForSequenceClassification(lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         model.eval()
         __UpperCamelCase       				=model(lowerCAmelCase_   ,  attention_mask=lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_   ,  labels=lowerCAmelCase_       )
         self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_labels)       )






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			List[str]:
         __UpperCamelCase       				=self.num_choices
         __UpperCamelCase       				=FunnelForMultipleChoice(config=lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         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(
             lowerCAmelCase_   ,  attention_mask=lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_   ,  labels=lowerCAmelCase_   ,  )
         self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_choices)       )






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Optional[Any]:
         __UpperCamelCase       				=self.num_labels
         __UpperCamelCase       				=FunnelForTokenClassification(config=lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         model.eval()
         __UpperCamelCase       				=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 _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			str:
         __UpperCamelCase       				=FunnelForQuestionAnswering(config=lowerCAmelCase_       )
         model.to(lowerCAmelCase_       )
         model.eval()
         __UpperCamelCase       				=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 _a   (     self       )    ->			List[str]:
         __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        UpperCAmelCase__			(   __a  ,    __a  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    False
    UpperCAmelCase__		:						Optional[int]    =    False
    UpperCAmelCase__		:						Tuple    =    (
        (
            FunnelModel,
            FunnelForMaskedLM,
            FunnelForPreTraining,
            FunnelForQuestionAnswering,
            FunnelForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__		:						Optional[int]    =    (
        {
            "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   ,  A_   ,  A_   ,  A_=False       )    ->			Union[str, Any]:
         __UpperCamelCase       				=super()._prepare_for_class(lowerCAmelCase_   ,  lowerCAmelCase_   ,  return_labels=lowerCAmelCase_       )

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






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=FunnelModelTester(self       )
         __UpperCamelCase       				=ConfigTester(self   ,  config_class=lowerCAmelCase_       )






    def _a   (     self       )    ->			Union[str, Any]:
         self.config_tester.run_common_tests()






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase_       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_       )






    def _a   (     self       )    ->			Union[str, Any]:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_       )






    def _a   (     self       )    ->			int:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_       )






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_       )






    def _a   (     self   ,  A_       )    ->			int:







         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:
                   __UpperCamelCase       				=getattr(lowerCAmelCase_   ,  lowerCAmelCase_       )
                   weight.data.fill_(3       )






@require_torch
class        UpperCAmelCase__			(   __a  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						Tuple    =    False
    UpperCAmelCase__		:						int    =    False
    UpperCAmelCase__		:						str    =    (
        (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
    )






    def _a   (     self       )    ->			Any:
         __UpperCamelCase       				=FunnelModelTester(self   ,  base=lowerCAmelCase_       )
         __UpperCamelCase       				=ConfigTester(self   ,  config_class=lowerCAmelCase_       )






    def _a   (     self       )    ->			Optional[int]:
         self.config_tester.run_common_tests()






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_base_model(*lowerCAmelCase_       )






    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_       )






    def _a   (     self       )    ->			str:
         __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(lowerCAmelCase_       )
              model.to(lowerCAmelCase_       )
              model.train()
              __UpperCamelCase       				=self._prepare_for_class(lowerCAmelCase_   ,  lowerCAmelCase_   ,  return_labels=lowerCAmelCase_       )
              __UpperCamelCase       				=model(**lowerCAmelCase_       ).loss
              loss.backward()






    def _a   (     self   ,  A_       )    ->			List[str]:







         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:
                   __UpperCamelCase       				=getattr(lowerCAmelCase_   ,  lowerCAmelCase_       )
                   weight.data.fill_(3       )






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



    """simple docstring"""






    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				=13
         __UpperCamelCase       				=7
         __UpperCamelCase       				=torch.arange(0   ,  batch_size * sequence_length       ).long().reshape(lowerCAmelCase_   ,  lowerCAmelCase_       )
         __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(lowerCAmelCase_   ,  token_type_ids=lowerCAmelCase_       )[0].abs()

         __UpperCamelCase       				=torch.tensor(2344.8352       )
         __UpperCamelCase       				=torch.tensor(0.8052       )
         self.assertTrue(torch.allclose(output.sum()   ,  lowerCAmelCase_   ,  atol=1E-4       )       )
         self.assertTrue(torch.allclose(output.mean()   ,  lowerCAmelCase_   ,  atol=1E-4       )       )

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

         __UpperCamelCase       				=torch.tensor(2343.8425       )
         __UpperCamelCase       				=torch.tensor(0.8049       )
         self.assertTrue(torch.allclose(output.sum()   ,  lowerCAmelCase_   ,  atol=1E-4       )       )
         self.assertTrue(torch.allclose(output.mean()   ,  lowerCAmelCase_   ,  atol=1E-4       )       )






    @slow
    def _a   (     self       )    ->			Union[str, Any]:
         __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(**lowerCAmelCase_       )[0]

         __UpperCamelCase       				=torch.tensor(235.7246       )
         __UpperCamelCase       				=torch.tensor(0.0256       )
         self.assertTrue(torch.allclose(output.sum()   ,  lowerCAmelCase_   ,  atol=1E-4       )       )
         self.assertTrue(torch.allclose(output.mean()   ,  lowerCAmelCase_   ,  atol=1E-4       )       )










'''simple docstring'''


from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 						UpperCAmelCase						(		datasets.BuilderConfig):


 '''simple docstring'''




 __magic_name__       :			Optional[Callable]	=   None
 __magic_name__       :			Optional[dict]	=   None
 __magic_name__       :			Optional[datasets.Features]	=   None




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



      """simple docstring"""







      assert self.generator is not None, "generator must be specified"

      if self.gen_kwargs is None:
           a_									={}







class 						UpperCAmelCase						(		datasets.GeneratorBasedBuilder):


 '''simple docstring'''




 __magic_name__       :			Tuple	=   GeneratorConfig




 def    lowercase_	( self)			->   Tuple:



      """simple docstring"""







      return datasets.DatasetInfo(features=self.config.features)




 def    lowercase_	( self				,	lowerCAmelCase_)			->   int:



      """simple docstring"""







      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,	gen_kwargs=self.config.gen_kwargs)]




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   List[Any]:



      """simple docstring"""







      for idx, ex in enumerate(self.config.generator(**lowerCAmelCase_)):
           yield idx, ex


