
import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
_UpperCAmelCase    			=					"stas/tiny-wmt19-en-ru"

class 		__magic_name__				( lowerCAmelCase__						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = FSMTTokenizer
  _UpperCamelCase						 = False


  def     _UpperCAmelCase  (					self		):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    _lowerCamelCase      =						[
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    _lowerCamelCase      =						dict(zip(lowerCAmelCase__	,		range(len(lowerCAmelCase__		)		)		)		)
    _lowerCamelCase      =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    _lowerCamelCase      =						['''en''', '''ru''']
    _lowerCamelCase      =						{
        '''langs''': self.langs,
        '''src_vocab_size''': 10,
        '''tgt_vocab_size''': 20,
    }

    _lowerCamelCase      =						os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES['''src_vocab_file''']		)
    _lowerCamelCase      =						os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES['''tgt_vocab_file''']		)
    _lowerCamelCase      =						os.path.join(self.tmpdirname	,		'''tokenizer_config.json'''		)
    _lowerCamelCase      =						os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES['''merges_file''']		)
    with open(self.src_vocab_file	,		'''w'''		) as fp:
      fp.write(json.dumps(lowerCAmelCase__		)		)
    with open(self.tgt_vocab_file	,		'''w'''		) as fp:
      fp.write(json.dumps(lowerCAmelCase__		)		)
    with open(self.merges_file	,		'''w'''		) as fp:
      fp.write('''\n'''.join(lowerCAmelCase__		)		)
    with open(lowerCAmelCase__	,		'''w'''		) as fp:
      fp.write(json.dumps(lowerCAmelCase__		)		)


  @cached_property
  def     _UpperCAmelCase  (					self		):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''		)


  @cached_property
  def     _UpperCAmelCase  (					self		):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						FSMTTokenizer.from_pretrained(lowerCAmelCase__		)
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]	,		['''en''', '''ru''']		)
    self.assertEqual(tokenizer.src_vocab_size	,		21		)
    self.assertEqual(tokenizer.tgt_vocab_size	,		21		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						FSMTTokenizer(self.langs	,		self.src_vocab_file	,		self.tgt_vocab_file	,		self.merges_file		)

    _lowerCamelCase      =						'''lower'''
    _lowerCamelCase      =						['''low''', '''er</w>''']
    _lowerCamelCase      =						tokenizer.tokenize(lowerCAmelCase__		)
    self.assertListEqual(lowerCAmelCase__	,		lowerCAmelCase__		)

    _lowerCamelCase      =						tokens + ['''<unk>''']
    _lowerCamelCase      =						[14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__		)	,		lowerCAmelCase__		)


  @slow
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.tokenizer_ru_en

    _lowerCamelCase      =						tokenizer.encode('''sequence builders'''	,		add_special_tokens=lowerCAmelCase__		)
    _lowerCamelCase      =						tokenizer.encode('''multi-sequence build'''	,		add_special_tokens=lowerCAmelCase__		)

    _lowerCamelCase      =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__		)
    _lowerCamelCase      =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__	,		lowerCAmelCase__		)

    assert encoded_sentence == text + [2]
    assert encoded_pair == text + [2] + text_a + [2]


  @slow
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.tokenizer_en_ru
    _lowerCamelCase      =						self.tokenizer_ru_en

    _lowerCamelCase      =						[
        [
            '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
            [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
        ],
        ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
    ]

    # if data needs to be recreated or added, run:
    # import torch
    # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
    # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

    for src_text, tgt_input_ids in targets:
      _lowerCamelCase      =						tokenizer_enc.encode(lowerCAmelCase__	,		return_tensors=lowerCAmelCase__		)
      self.assertListEqual(lowerCAmelCase__	,		lowerCAmelCase__		)

      # and decode backward, using the reversed languages model
      _lowerCamelCase      =						tokenizer_dec.decode(lowerCAmelCase__	,		skip_special_tokens=lowerCAmelCase__		)
      self.assertEqual(lowerCAmelCase__	,		lowerCAmelCase__		)


  @slow
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''	,		do_lower_case=lowerCAmelCase__		)
    _lowerCamelCase      =						tokenizer.tokenize('''USA is United States of America'''		)
    _lowerCamelCase      =						['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
    self.assertListEqual(lowerCAmelCase__	,		lowerCAmelCase__		)


  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''		)
  def     _UpperCAmelCase  (					self		):
    pass


  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''		)
  def     _UpperCAmelCase  (					self		):
    pass





"""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 					UpperCamelCase   :







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

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







       def 						a_		(					self) -> Optional[int]:
            snake_case_   			=   ids_tensor([self.batch_size, self.seq_length],				self.vocab_size)

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

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

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

            snake_case_   			=   self.get_config()

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







       def 						a_		(					self) -> Dict:
            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,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Tuple:
            snake_case_   			=   FunnelModel(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.d_model))

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

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







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Any:
            snake_case_   			=   FunnelBaseModel(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__,				token_type_ids=lowerCAmelCase__)
            snake_case_   			=   model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, 2, self.d_model))

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

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







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> List[Any]:
            snake_case_   			=   self.num_labels
            snake_case_   			=   FunnelForPreTraining(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   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,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> str:
            snake_case_   			=   FunnelForMaskedLM(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   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,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Optional[int]:
            snake_case_   			=   self.num_labels
            snake_case_   			=   FunnelForSequenceClassification(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   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,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Optional[int]:
            snake_case_   			=   self.num_choices
            snake_case_   			=   FunnelForMultipleChoice(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   input_ids.unsqueeze(1).expand(-1,				self.num_choices,				-1).contiguous()
            snake_case_   			=   token_type_ids.unsqueeze(1).expand(-1,				self.num_choices,				-1).contiguous()
            snake_case_   			=   input_mask.unsqueeze(1).expand(-1,				self.num_choices,				-1).contiguous()
            snake_case_   			=   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,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> Tuple:
            snake_case_   			=   self.num_labels
            snake_case_   			=   FunnelForTokenClassification(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   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,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				) -> int:
            snake_case_   			=   FunnelForQuestionAnswering(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_   			=   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) -> Optional[int]:
            snake_case_   			=   self.prepare_config_and_inputs()
            (
                (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      (
                snake_case_
            )			,      
            )   			=   config_and_inputs
            snake_case_   			=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict




@require_torch
class 					UpperCamelCase   (							lowerCAmelCase__       ,						lowerCAmelCase__       ,						unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = (
           (
               FunnelModel,
               FunnelForMaskedLM,
               FunnelForPreTraining,
               FunnelForQuestionAnswering,
               FunnelForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_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,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__=False) -> List[Any]:
            snake_case_   			=   super()._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__,				return_labels=lowerCAmelCase__)

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







       def 						a_		(					self) -> List[Any]:
            snake_case_   			=   FunnelModelTester(self)
            snake_case_   			=   ConfigTester(self,				config_class=lowerCAmelCase__)







       def 						a_		(					self) -> Tuple:
            self.config_tester.run_common_tests()







       def 						a_		(					self) -> Dict:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__)







       def 						a_		(					self) -> Optional[Any]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__)







       def 						a_		(					self) -> List[str]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__)







       def 						a_		(					self) -> int:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__)







       def 						a_		(					self) -> Any:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__) -> 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:
                      snake_case_   			=   getattr(lowerCAmelCase__,				lowerCAmelCase__)
                      weight.data.fill_(3)




@require_torch
class 					UpperCamelCase   (							lowerCAmelCase__       ,						unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = False
       SCREAMING_SNAKE_CASE_						      = (
           (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
       )







       def 						a_		(					self) -> List[Any]:
            snake_case_   			=   FunnelModelTester(self,				base=lowerCAmelCase__)
            snake_case_   			=   ConfigTester(self,				config_class=lowerCAmelCase__)







       def 						a_		(					self) -> Tuple:
            self.config_tester.run_common_tests()







       def 						a_		(					self) -> str:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_base_model(*lowerCAmelCase__)







       def 						a_		(					self) -> Optional[Any]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__)







       def 						a_		(					self) -> List[Any]:
            snake_case_   			=   self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__)







       def 						a_		(					self) -> Union[str, Any]:
            snake_case_			,      snake_case_   			=   self.model_tester.prepare_config_and_inputs_for_common()
            snake_case_   			=   True

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







       def 						a_		(					self,				lowerCAmelCase__) -> 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:
                      snake_case_   			=   getattr(lowerCAmelCase__,				lowerCAmelCase__)
                      weight.data.fill_(3)




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







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

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

            snake_case_   			=   torch.tensor(2344.8352)
            snake_case_   			=   torch.tensor(0.8052)
            self.assertTrue(torch.allclose(output.sum(),				lowerCAmelCase__,				atol=1e-4))
            self.assertTrue(torch.allclose(output.mean(),				lowerCAmelCase__,				atol=1e-4))

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

            snake_case_   			=   torch.tensor(2343.8425)
            snake_case_   			=   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) -> str:
            snake_case_   			=   FunnelTokenizer.from_pretrained('huggingface/funnel-small')
            snake_case_   			=   FunnelModel.from_pretrained('huggingface/funnel-small')
            snake_case_   			=   tokenizer('Hello! I am the Funnel Transformer model.',				return_tensors='pt')
            snake_case_   			=   model(**lowerCAmelCase__)[0]

            snake_case_   			=   torch.tensor(235.7246)
            snake_case_   			=   torch.tensor(0.0256)
            self.assertTrue(torch.allclose(output.sum(),				lowerCAmelCase__,				atol=1e-4))
            self.assertTrue(torch.allclose(output.mean(),				lowerCAmelCase__,				atol=1e-4))

