



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_			=					"""▁"""

a_			=					get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  BertGenerationTokenizer
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  True




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()

    __lowerCamelCase													=    BertGenerationTokenizer(__UpperCAmelCase	,				keep_accents=__UpperCAmelCase				)
    tokenizer.save_pretrained(self.tmpdirname				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''<s>'''
    __lowerCamelCase													=    1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase				)	,				__UpperCAmelCase				)
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    list(self.get_tokenizer().get_vocab().keys()				)

    self.assertEqual(vocab_keys[0]	,				'''<unk>'''				)
    self.assertEqual(vocab_keys[1]	,				'''<s>'''				)
    self.assertEqual(vocab_keys[-1]	,				'''<pad>'''				)
    self.assertEqual(len(__UpperCAmelCase				)	,				1002				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.assertEqual(self.get_tokenizer().vocab_size	,				1000				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    BertGenerationTokenizer(__UpperCAmelCase	,				keep_accents=__UpperCAmelCase				)

    __lowerCamelCase													=    tokenizer.tokenize('''This is a test'''				)
    self.assertListEqual(__UpperCAmelCase	,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				[285, 46, 10, 170, 382]	,				)

    __lowerCamelCase													=    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
    self.assertListEqual(
        __UpperCAmelCase	,				[
            SPIECE_UNDERLINE + '''I''',
            SPIECE_UNDERLINE + '''was''',
            SPIECE_UNDERLINE + '''b''',
            '''or''',
            '''n''',
            SPIECE_UNDERLINE + '''in''',
            SPIECE_UNDERLINE + '''''',
            '''9''',
            '''2''',
            '''0''',
            '''0''',
            '''0''',
            ''',''',
            SPIECE_UNDERLINE + '''and''',
            SPIECE_UNDERLINE + '''this''',
            SPIECE_UNDERLINE + '''is''',
            SPIECE_UNDERLINE + '''f''',
            '''al''',
            '''s''',
            '''é''',
            '''.''',
        ]	,				)
    __lowerCamelCase													=    tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)
    self.assertListEqual(
        __UpperCAmelCase	,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]	,				)

    __lowerCamelCase													=    tokenizer.convert_ids_to_tokens(__UpperCAmelCase				)
    self.assertListEqual(
        __UpperCAmelCase	,				[
            SPIECE_UNDERLINE + '''I''',
            SPIECE_UNDERLINE + '''was''',
            SPIECE_UNDERLINE + '''b''',
            '''or''',
            '''n''',
            SPIECE_UNDERLINE + '''in''',
            SPIECE_UNDERLINE + '''''',
            '''<unk>''',
            '''2''',
            '''0''',
            '''0''',
            '''0''',
            ''',''',
            SPIECE_UNDERLINE + '''and''',
            SPIECE_UNDERLINE + '''this''',
            SPIECE_UNDERLINE + '''is''',
            SPIECE_UNDERLINE + '''f''',
            '''al''',
            '''s''',
            '''<unk>''',
            '''.''',
        ]	,				)




 @cached_property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''Hello World!'''
    __lowerCamelCase													=    [18536, 2260, 101]

    self.assertListEqual(__UpperCAmelCase	,				self.big_tokenizer.encode(__UpperCAmelCase				)				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    (
        '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
        ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
    )
    __lowerCamelCase													=    [
        871,
        419,
        358,
        946,
        991,
        2521,
        452,
        358,
        1357,
        387,
        7751,
        3536,
        112,
        985,
        456,
        126,
        865,
        938,
        5400,
        5734,
        458,
        1368,
        467,
        786,
        2462,
        5246,
        1159,
        633,
        865,
        4519,
        457,
        582,
        852,
        2557,
        427,
        916,
        508,
        405,
        34324,
        497,
        391,
        408,
        11342,
        1244,
        385,
        100,
        938,
        985,
        456,
        574,
        362,
        12597,
        3200,
        3129,
        1172,
    ]

    self.assertListEqual(__UpperCAmelCase	,				self.big_tokenizer.encode(__UpperCAmelCase				)				)




 @require_torch
 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    import torch

    from transformers import BertGenerationConfig, BertGenerationEncoder

    # Build sequence
    __lowerCamelCase													=    list(self.big_tokenizer.get_vocab().keys()				)[:10]
    __lowerCamelCase													=    ''' '''.join(__UpperCAmelCase				)
    __lowerCamelCase													=    self.big_tokenizer.encode_plus(__UpperCAmelCase	,				return_tensors='''pt'''	,				return_token_type_ids=__UpperCAmelCase				)
    __lowerCamelCase													=    self.big_tokenizer.batch_encode_plus(
        [sequence + ''' ''' + sequence]	,				return_tensors='''pt'''	,				return_token_type_ids=__UpperCAmelCase				)

    __lowerCamelCase													=    BertGenerationConfig()
    __lowerCamelCase													=    BertGenerationEncoder(__UpperCAmelCase				)

    assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

    with torch.no_grad():
       model(**__UpperCAmelCase				)
       model(**__UpperCAmelCase				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # fmt: off
    __lowerCamelCase													=    {'''input_ids''': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=__UpperCAmelCase	,				model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''	,				revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''	,				)











from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFFunnelBaseModel,
      TFFunnelForMaskedLM,
      TFFunnelForMultipleChoice,
      TFFunnelForPreTraining,
      TFFunnelForQuestionAnswering,
      TFFunnelForSequenceClassification,
      TFFunnelForTokenClassification,
      TFFunnelModel,
  )



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=13	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=99	,				__UpperCAmelCase=[1, 1, 2]	,				__UpperCAmelCase=1	,				__UpperCAmelCase=32	,				__UpperCAmelCase=4	,				__UpperCAmelCase=8	,				__UpperCAmelCase=37	,				__UpperCAmelCase="gelu_new"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase=512	,				__UpperCAmelCase=3	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4	,				__UpperCAmelCase=None	,				__UpperCAmelCase=False	,				):



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

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size				)

    __lowerCamelCase													=    None
    if self.use_input_mask:
       __lowerCamelCase													=    random_attention_mask([self.batch_size, self.seq_length]				)

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

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

    __lowerCamelCase													=    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	,				)

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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    [input_ids, input_mask]
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.d_model)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelBaseModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    [input_ids, input_mask]
    __lowerCamelCase													=    model(__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 2, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelBaseModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 3, self.d_model)				)

    __lowerCamelCase													=    False
    __lowerCamelCase													=    TFFunnelBaseModel(config=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, 2, self.d_model)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelForPreTraining(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelForMaskedLM(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFFunnelForSequenceClassification(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.num_choices
    __lowerCamelCase													=    TFFunnelForMultipleChoice(config=__UpperCAmelCase				)
    __lowerCamelCase													=    tf.tile(tf.expand_dims(__UpperCAmelCase	,				1				)	,				(1, self.num_choices, 1)				)
    __lowerCamelCase													=    tf.tile(tf.expand_dims(__UpperCAmelCase	,				1				)	,				(1, self.num_choices, 1)				)
    __lowerCamelCase													=    tf.tile(tf.expand_dims(__UpperCAmelCase	,				1				)	,				(1, self.num_choices, 1)				)
    __lowerCamelCase													=    {
        '''input_ids''': multiple_choice_inputs_ids,
        '''attention_mask''': multiple_choice_input_mask,
        '''token_type_ids''': multiple_choice_token_type_ids,
    }
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_choices)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFFunnelForTokenClassification(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelForQuestionAnswering(config=__UpperCAmelCase				)
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    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				):



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



@require_tf
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (
     (
         TFFunnelModel,
         TFFunnelForMaskedLM,
         TFFunnelForPreTraining,
         TFFunnelForQuestionAnswering,
         TFFunnelForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 lowerCAmelCase__       					=  (
     {
         """feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
         """fill-mask""": TFFunnelForMaskedLM,
         """question-answering""": TFFunnelForQuestionAnswering,
         """text-classification""": TFFunnelForSequenceClassification,
         """token-classification""": TFFunnelForTokenClassification,
         """zero-shot""": TFFunnelForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase				)



@require_tf
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (
     (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
 )
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFFunnelModelTester(self	,				base=__UpperCAmelCase				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_base_model(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase				)






