

'''simple docstring'''






import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
  from transformers.models.esm.modeling_esm import (
      ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
      EsmEmbeddings,
      create_position_ids_from_input_ids,
  )






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Tuple      ,lowercase__     :  List[str]=1_3      ,lowercase__     :  str=7      ,lowercase__     :  Optional[Any]=False      ,lowercase__     :  List[str]=True      ,lowercase__     :  Any=False      ,lowercase__     :  List[str]=True      ,lowercase__     :  Union[str, Any]=3_3      ,lowercase__     :  str=3_2      ,lowercase__     :  Any=5      ,lowercase__     :  str=4      ,lowercase__     :  List[Any]=3_7      ,lowercase__     :  Tuple="gelu"      ,lowercase__     :  str=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  List[str]=5_1_2      ,lowercase__     :  Tuple=1_6      ,lowercase__     :  Dict=2      ,lowercase__     :  Dict=0.0_2      ,lowercase__     :  Optional[Any]=3      ,lowercase__     :  List[str]=4      ,lowercase__     :  Tuple=None      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_input_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				intermediate_size
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout_prob
   __lowercase													=				attention_probs_dropout_prob
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				type_sequence_label_size
   __lowercase													=				initializer_range
   __lowercase													=				num_labels
   __lowercase													=				num_choices
   __lowercase													=				scope






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

   __lowercase													=				self.get_config()

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   return EsmConfig(
       vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,pad_token_id=1      ,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      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Any      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  str    ):
   __lowercase													=				EsmModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__    )
   __lowercase													=				model(lowercase__    )
   __lowercase													=				model(lowercase__    )

   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       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Any      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  Any      ,lowercase__     :  Tuple    ):
   __lowercase													=				EsmForMaskedLM(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Dict      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Any      ,lowercase__     :  Tuple      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]    ):
   __lowercase													=				self.num_labels
   __lowercase													=				EsmForTokenClassification(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase			
   )						,			(
       __lowercase			
   )						,			(
       __lowercase			
   )						,			(
       __lowercase			
   )						,			(
       __lowercase			
   )						,			(
       __lowercase			
   )						,						
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    False

  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (
      (
          EsmForMaskedLM,
          EsmModel,
          EsmForSequenceClassification,
          EsmForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Any           =    ()
  SCREAMING_SNAKE_CASE					:						List[Any]           =    (
      {
          'feature-extraction': EsmModel,
          'fill-mask': EsmForMaskedLM,
          'text-classification': EsmForSequenceClassification,
          'token-classification': EsmForTokenClassification,
          'zero-shot': EsmForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						int           =    True






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				EsmModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    __lowercase													=				type
    self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase													=				EsmModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()[0]
   __lowercase													=				EsmEmbeddings(config=lowercase__    )

   __lowercase													=				torch.as_tensor([[1_2, 3_1, 1_3, model.padding_idx]]    )
   __lowercase													=				torch.as_tensor(
       [
           [
               0 + model.padding_idx + 1,
               1 + model.padding_idx + 1,
               2 + model.padding_idx + 1,
               model.padding_idx,
           ]
       ]    )
   __lowercase													=				create_position_ids_from_input_ids(lowercase__      ,model.padding_idx    )
   self.assertEqual(position_ids.shape      ,expected_positions.shape    )
   self.assertTrue(torch.all(torch.eq(lowercase__      ,lowercase__    )    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()[0]
   __lowercase													=				EsmEmbeddings(config=lowercase__    )

   __lowercase													=				torch.empty(2      ,4      ,3_0    )
   __lowercase													=				[
       0 + embeddings.padding_idx + 1,
       1 + embeddings.padding_idx + 1,
       2 + embeddings.padding_idx + 1,
       3 + embeddings.padding_idx + 1,
   ]
   __lowercase													=				torch.as_tensor([expected_single_positions, expected_single_positions]    )
   __lowercase													=				embeddings.create_position_ids_from_inputs_embeds(lowercase__    )
   self.assertEqual(position_ids.shape      ,expected_positions.shape    )
   self.assertTrue(torch.all(torch.eq(lowercase__      ,lowercase__    )    )    )






  @unittest.skip('''Esm does not support embedding resizing'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   pass






  @unittest.skip('''Esm does not support embedding resizing'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






@require_torch
class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   with torch.no_grad():
    __lowercase													=				EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''    )
    model.eval()
    __lowercase													=				torch.tensor([[0, 1, 2, 3, 4, 5]]    )
    __lowercase													=				model(lowercase__    )[0]

    __lowercase													=				3_3

    __lowercase													=				torch.Size((1, 6, vocab_size)    )
    self.assertEqual(output.shape      ,lowercase__    )

    __lowercase													=				torch.tensor(
        [[[8.9_2_1_5, -1_0.5_8_9_8, -6.4_6_7_1], [-6.3_9_6_7, -1_3.9_1_1_4, -1.1_2_1_2], [-7.7_8_1_2, -1_3.9_5_1_6, -3.7_4_0_6]]]    )
    self.assertTrue(torch.allclose(output[:, :3, :3]      ,lowercase__      ,atol=1e-4    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   with torch.no_grad():
    __lowercase													=				EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''    )
    model.eval()

    __lowercase													=				torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]    )
    __lowercase													=				model(lowercase__    )[0]
    # compare the actual values for a slice.
    __lowercase													=				torch.tensor(
        [[[0.1_4_4_4, 0.5_4_1_3, 0.3_2_4_8], [0.3_0_3_4, 0.0_0_5_3, 0.3_1_0_8], [0.3_2_2_8, -0.2_4_9_9, 0.3_4_1_5]]]    )
    self.assertTrue(torch.allclose(output[:, :3, :3]      ,lowercase__      ,atol=1e-4    )    )




'''simple docstring'''






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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[Any]=1_3      ,lowercase__     :  Union[str, Any]=7      ,lowercase__     :  List[Any]=True      ,lowercase__     :  str=True      ,lowercase__     :  Any=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Tuple=9_9      ,lowercase__     :  List[str]=[1, 1, 2]      ,lowercase__     :  List[Any]=1      ,lowercase__     :  List[Any]=3_2      ,lowercase__     :  int=4      ,lowercase__     :  Tuple=8      ,lowercase__     :  Tuple=3_7      ,lowercase__     :  str="gelu_new"      ,lowercase__     :  Dict=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  Optional[Any]=0.0      ,lowercase__     :  Tuple=5_1_2      ,lowercase__     :  Any=3      ,lowercase__     :  List[str]=0.0_2      ,lowercase__     :  List[Any]=3      ,lowercase__     :  List[Any]=4      ,lowercase__     :  Optional[Any]=None      ,lowercase__     :  Tuple=False      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_input_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				block_sizes
   __lowercase													=				num_decoder_layers
   __lowercase													=				d_model
   __lowercase													=				n_head
   __lowercase													=				d_head
   __lowercase													=				d_inner
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout
   __lowercase													=				attention_dropout
   __lowercase													=				activation_dropout
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				2
   __lowercase													=				num_labels
   __lowercase													=				num_choices
   __lowercase													=				scope
   __lowercase													=				initializer_std

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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

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

   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )

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

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






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict      ,):
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 2, self.d_model)    )

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

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Any      ,lowercase__     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelForPreTraining(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				TFFunnelForMaskedLM(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  str      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForSequenceClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_choices
   __lowercase													=				TFFunnelForMultipleChoice(config=lowercase__    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				{
       '''input_ids''': multiple_choice_inputs_ids,
       '''attention_mask''': multiple_choice_input_mask,
       '''token_type_ids''': multiple_choice_token_type_ids,
   }
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForTokenClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  Any      ,):
   __lowercase													=				TFFunnelForQuestionAnswering(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   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       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_tf
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						int           =    (
      (
          TFFunnelModel,
          TFFunnelForMaskedLM,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (
      {
          'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
          'fill-mask': TFFunnelForMaskedLM,
          'question-answering': TFFunnelForQuestionAnswering,
          'text-classification': TFFunnelForSequenceClassification,
          'token-classification': TFFunnelForTokenClassification,
          'zero-shot': TFFunnelForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						Tuple           =    False
  SCREAMING_SNAKE_CASE					:						Any           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase__    )






@require_tf
class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[Any]           =    (
      (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
  )
  SCREAMING_SNAKE_CASE					:						Dict           =    False
  SCREAMING_SNAKE_CASE					:						List[str]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self      ,base=lowercase__    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_base_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*lowercase__    )

