



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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.models.layoutlm.modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMModel,
  )



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=13	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=99	,				__UpperCAmelCase=32	,				__UpperCAmelCase=2	,				__UpperCAmelCase=4	,				__UpperCAmelCase=37	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=16	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4	,				__UpperCAmelCase=None	,				__UpperCAmelCase=1000	,				):



    '''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													=    hidden_size
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    type_sequence_label_size
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    num_labels
    __lowerCamelCase													=    num_choices
    __lowerCamelCase													=    scope
    __lowerCamelCase													=    range_bbox




 def        lowerCamelCase      (						self				):



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

    # convert bbox to numpy since TF does not support item assignment
    __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length, 4]	,				self.range_bbox				).numpy()
    # Ensure that bbox is legal
    for i in range(bbox.shape[0]				):
       for j in range(bbox.shape[1]				):
          if bbox[i, j, 3] < bbox[i, j, 1]:
             __lowerCamelCase													=    bbox[i, j, 3]
             __lowerCamelCase													=    bbox[i, j, 1]
             __lowerCamelCase													=    t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             __lowerCamelCase													=    bbox[i, j, 2]
             __lowerCamelCase													=    bbox[i, j, 0]
             __lowerCamelCase													=    t
    __lowerCamelCase													=    tf.convert_to_tensor(__UpperCAmelCase				)

    __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													=    LayoutLMConfig(
        vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				initializer_range=self.initializer_range	,				)

    return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




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



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMModel(config=__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)				)
    self.parent.assertEqual(result.pooler_output.shape	,				(self.batch_size, self.hidden_size)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMForMaskedLM(config=__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				labels=__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	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFLayoutLMForSequenceClassification(config=__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)				)




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



    '''simple docstring'''
    __lowerCamelCase													=    self.num_labels
    __lowerCamelCase													=    TFLayoutLMForTokenClassification(config=__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				labels=__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	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMForQuestionAnswering(config=__UpperCAmelCase				)

    __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__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
    )							,(
        __lowerCamelCase
    )							,
    )													=    config_and_inputs
    __lowerCamelCase													=    {
        '''input_ids''': input_ids,
        '''bbox''': bbox,
        '''token_type_ids''': token_type_ids,
        '''attention_mask''': input_mask,
    }
    return config, inputs_dict



@require_tf
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (
     (
         TFLayoutLMModel,
         TFLayoutLMForMaskedLM,
         TFLayoutLMForTokenClassification,
         TFLayoutLMForSequenceClassification,
         TFLayoutLMForQuestionAnswering,
     )
     if is_tf_available()
     else ()
 )
 lowerCAmelCase__       					=  (
     {
         """feature-extraction""": TFLayoutLMModel,
         """fill-mask""": TFLayoutLMForMaskedLM,
         """text-classification""": TFLayoutLMForSequenceClassification,
         """token-classification""": TFLayoutLMForTokenClassification,
         """zero-shot""": TFLayoutLMForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  1_0




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase	,				hidden_size=37				)




 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_masked_lm(*__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_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				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCamelCase													=    TFLayoutLMModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)




 @unittest.skip('''Onnx compliancy broke with TF 2.10'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass





def 		a__			(		):
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   __lowerCamelCase													=    tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]							)  # noqa: E231
   __lowerCamelCase													=    tf.convert_to_tensor([[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],]							)  # noqa: E231
   __lowerCamelCase													=    tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]							)  # noqa: E231
   __lowerCamelCase													=    tf.convert_to_tensor([[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: E231
   # these are sequence labels (i.e. at the token level)
   __lowerCamelCase													=    tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]							)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class  __lowerCAmelCase     (	unittest.TestCase   ):




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''				)

    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    prepare_layoutlm_batch_inputs()

    # forward pass
    __lowerCamelCase													=    model(input_ids=__UpperCAmelCase	,				bbox=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase				)

    # test the sequence output on [0, :3, :3]
    __lowerCamelCase													=    tf.convert_to_tensor(
        [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]	,				)

    self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]	,				__UpperCAmelCase	,				atol=1E-3				)				)

    # test the pooled output on [1, :3]
    __lowerCamelCase													=    tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]				)

    self.assertTrue(np.allclose(outputs.pooler_output[1, :3]	,				__UpperCAmelCase	,				atol=1E-3				)				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # initialize model with randomly initialized sequence classification head
    __lowerCamelCase													=    TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''	,				num_labels=2				)

    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    prepare_layoutlm_batch_inputs()

    # forward pass
    __lowerCamelCase													=    model(
        input_ids=__UpperCAmelCase	,				bbox=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				labels=tf.convert_to_tensor([1, 1]				)	,				)

    # test whether we get a loss as a scalar
    __lowerCamelCase													=    outputs.loss
    __lowerCamelCase													=    (2,)
    self.assertEqual(loss.shape	,				__UpperCAmelCase				)

    # test the shape of the logits
    __lowerCamelCase													=    outputs.logits
    __lowerCamelCase													=    (2, 2)
    self.assertEqual(logits.shape	,				__UpperCAmelCase				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # initialize model with randomly initialized token classification head
    __lowerCamelCase													=    TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''	,				num_labels=13				)

    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    prepare_layoutlm_batch_inputs()

    # forward pass
    __lowerCamelCase													=    model(
        input_ids=__UpperCAmelCase	,				bbox=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase	,				labels=__UpperCAmelCase				)

    # test the shape of the logits
    __lowerCamelCase													=    outputs.logits
    __lowerCamelCase													=    tf.convert_to_tensor((2, 25, 13)				)
    self.assertEqual(logits.shape	,				__UpperCAmelCase				)




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # initialize model with randomly initialized token classification head
    __lowerCamelCase													=    TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''				)

    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    prepare_layoutlm_batch_inputs()

    # forward pass
    __lowerCamelCase													=    model(input_ids=__UpperCAmelCase	,				bbox=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				token_type_ids=__UpperCAmelCase				)

    # test the shape of the logits
    __lowerCamelCase													=    tf.convert_to_tensor((2, 25)				)
    self.assertEqual(outputs.start_logits.shape	,				__UpperCAmelCase				)
    self.assertEqual(outputs.end_logits.shape	,				__UpperCAmelCase				)











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				)






