'''simple docstring'''




class        a__ :






     def __init__(					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    0
         __lowerCamelCase            =    0
         __lowerCamelCase            =    {}






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						str       ):





         """simple docstring"""



         if vertex not in self.adjacency:
             __lowerCamelCase            =    {}
             self.num_vertices += 1






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						Any    ,							a						:						int    ,							a						:						List[str]       ):





         """simple docstring"""



         self.add_vertex(__UpperCAmelCase       )
         self.add_vertex(__UpperCAmelCase       )

         if head == tail:
             return

         __lowerCamelCase            =    weight
         __lowerCamelCase            =    weight






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.get_edges()
         for edge in edges:
             __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
             edges.remove((tail, head, weight)       )
         for i in range(len(__UpperCAmelCase       )       ):
             __lowerCamelCase            =    list(edges[i]       )

         edges.sort(key=lambda a       : e[2]       )
         for i in range(len(__UpperCAmelCase       ) - 1       ):
             if edges[i][2] >= edges[i + 1][2]:
                 __lowerCamelCase            =    edges[i][2] + 1
         for edge in edges:
             __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
             __lowerCamelCase            =    weight
             __lowerCamelCase            =    weight






     def __str__(					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    ''''''
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 __lowerCamelCase            =    self.adjacency[head][tail]
                 string += f"""{head} -> {tail} == {weight}\n"""
         return string.rstrip('''\n'''       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase            =    []
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 output.append((tail, head, self.adjacency[head][tail])       )
         return output






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         return self.adjacency.keys()






     @staticmethod
     def   SCREAMING_SNAKE_CASE__  (					a						:						Union[str, Any]=None    ,							a						:						List[Any]=None       ):





         """simple docstring"""



         __lowerCamelCase            =    Graph()
         if vertices is None:
             __lowerCamelCase            =    []
         if edges is None:
             __lowerCamelCase            =    []
         for vertex in vertices:
             g.add_vertex(__UpperCAmelCase       )
         for edge in edges:
             g.add_edge(*__UpperCAmelCase       )
         return g




     class        a__ :






          def __init__(					self						:						Optional[Any]       ):





              """simple docstring"""



              __lowerCamelCase            =    {}
              __lowerCamelCase            =    {}






          def __len__(					self						:						Any       ):





              """simple docstring"""



              return len(self.parent       )






          def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Tuple       ):





              """simple docstring"""



              if item in self.parent:
                  return self.find(__UpperCAmelCase       )

              __lowerCamelCase            =    item
              __lowerCamelCase            =    0
              return item






          def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Tuple       ):





              """simple docstring"""



              if item not in self.parent:
                  return self.make_set(__UpperCAmelCase       )
              if item != self.parent[item]:
                  __lowerCamelCase            =    self.find(self.parent[item]       )
              return self.parent[item]






          def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						Optional[int]    ,							a						:						Tuple       ):





              """simple docstring"""



              __lowerCamelCase            =    self.find(__UpperCAmelCase       )
              __lowerCamelCase            =    self.find(__UpperCAmelCase       )

              if roota == roota:
                  return roota

              if self.rank[roota] > self.rank[roota]:
                  __lowerCamelCase            =    roota
                  return roota

              if self.rank[roota] < self.rank[roota]:
                  __lowerCamelCase            =    roota
                  return roota

              if self.rank[roota] == self.rank[roota]:
                  self.rank[roota] += 1
                  __lowerCamelCase            =    roota
                  return roota
              return None






     @staticmethod
     def   SCREAMING_SNAKE_CASE__  (					a						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    graph.num_vertices

         __lowerCamelCase            =    Graph.UnionFind()
         __lowerCamelCase            =    []
         while num_components > 1:
             __lowerCamelCase            =    {}
             for vertex in graph.get_vertices():
                 __lowerCamelCase            =    -1

             __lowerCamelCase            =    graph.get_edges()
             for edge in edges:
                 __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
                 edges.remove((tail, head, weight)       )
             for edge in edges:
                 __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
                 __lowerCamelCase            =    union_find.find(__UpperCAmelCase       )
                 __lowerCamelCase            =    union_find.find(__UpperCAmelCase       )
                 if seta != seta:
                     if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                         __lowerCamelCase            =    [head, tail, weight]

                     if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                         __lowerCamelCase            =    [head, tail, weight]
             for vertex in cheap_edge:
                 if cheap_edge[vertex] != -1:
                     __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    cheap_edge[vertex]
                     if union_find.find(__UpperCAmelCase       ) != union_find.find(__UpperCAmelCase       ):
                         union_find.union(__UpperCAmelCase    ,							__UpperCAmelCase       )
                         mst_edges.append(cheap_edge[vertex]       )
                         __lowerCamelCase            =    num_components - 1
         __lowerCamelCase            =    Graph.build(edges=__UpperCAmelCase       )
         return mst



"""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  lowerCamelCase    :

			def __init__(       self					:    str     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Union[str, Any]=1_3     ,					__UpperCAmelCase					:    Union[str, Any]=7     ,					__UpperCAmelCase					:    List[str]=True     ,					__UpperCAmelCase					:    Tuple=True     ,					__UpperCAmelCase					:    Any=True     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    Union[str, Any]=9_9     ,					__UpperCAmelCase					:    str=[1, 1, 2]     ,					__UpperCAmelCase					:    str=1     ,					__UpperCAmelCase					:    Dict=3_2     ,					__UpperCAmelCase					:    Optional[int]=4     ,					__UpperCAmelCase					:    str=8     ,					__UpperCAmelCase					:    List[Any]=3_7     ,					__UpperCAmelCase					:    Union[str, Any]="gelu_new"     ,					__UpperCAmelCase					:    List[Any]=0.1     ,					__UpperCAmelCase					:    str=0.1     ,					__UpperCAmelCase					:    Any=0.0     ,					__UpperCAmelCase					:    str=5_1_2     ,					__UpperCAmelCase					:    Optional[Any]=3     ,					__UpperCAmelCase					:    List[str]=0.02     ,					__UpperCAmelCase					:    Tuple=3     ,					__UpperCAmelCase					:    str=4     ,					__UpperCAmelCase					:    Optional[Any]=None     ,					__UpperCAmelCase					:    int=False     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   seq_length
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_input_mask
						SCREAMING_SNAKE_CASE__										=   use_token_type_ids
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   block_sizes
						SCREAMING_SNAKE_CASE__										=   num_decoder_layers
						SCREAMING_SNAKE_CASE__										=   d_model
						SCREAMING_SNAKE_CASE__										=   n_head
						SCREAMING_SNAKE_CASE__										=   d_head
						SCREAMING_SNAKE_CASE__										=   d_inner
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout
						SCREAMING_SNAKE_CASE__										=   attention_dropout
						SCREAMING_SNAKE_CASE__										=   activation_dropout
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   2
						SCREAMING_SNAKE_CASE__										=   num_labels
						SCREAMING_SNAKE_CASE__										=   num_choices
						SCREAMING_SNAKE_CASE__										=   scope
						SCREAMING_SNAKE_CASE__										=   initializer_std

						# Used in the tests to check the size of the first attention layer
						SCREAMING_SNAKE_CASE__										=   n_head
						# Used in the tests to check the size of the first hidden state
						SCREAMING_SNAKE_CASE__										=   self.d_model
						# Used in the tests to check the number of output hidden states/attentions
						SCREAMING_SNAKE_CASE__										=   sum(self.block_sizes							) + (0 if base else self.num_decoder_layers)
						# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
						# the last hidden state of the first block (which is the first hidden state of the decoder).
						if not base:
									SCREAMING_SNAKE_CASE__										=   self.num_hidden_layers + 2

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size							)

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

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

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

						SCREAMING_SNAKE_CASE__										=   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					:    Optional[Any]     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    int     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)

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

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

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

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

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    int     ,					) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   TFFunnelBaseModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)

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

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

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

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Optional[int]     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelForPreTraining(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    List[Any]     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelForMaskedLM(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Tuple     ,					) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.num_labels
						SCREAMING_SNAKE_CASE__										=   TFFunnelForSequenceClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    int     ,					) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   self.num_choices
						SCREAMING_SNAKE_CASE__										=   TFFunnelForMultipleChoice(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1							)     ,					(1, self.num_choices, 1)							)
						SCREAMING_SNAKE_CASE__										=   tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1							)     ,					(1, self.num_choices, 1)							)
						SCREAMING_SNAKE_CASE__										=   tf.tile(tf.expand_dims(__UpperCAmelCase     ,					1							)     ,					(1, self.num_choices, 1)							)
						SCREAMING_SNAKE_CASE__										=   {
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						    """token_type_ids""": multiple_choice_token_type_ids,
						}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    str     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.num_labels
						SCREAMING_SNAKE_CASE__										=   TFFunnelForTokenClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.num_labels)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    int     ,					) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   TFFunnelForQuestionAnswering(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						SCREAMING_SNAKE_CASE__										=   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        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		
						)										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict





@require_tf
class  lowerCamelCase    (A__      ,A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		int       		=				(
			    (
			        TFFunnelModel,
			        TFFunnelForMaskedLM,
			        TFFunnelForPreTraining,
			        TFFunnelForQuestionAnswering,
			        TFFunnelForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__ :		Tuple       		=				(
			    {
			        '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__ :		str       		=				False
			lowerCamelCase__ :		Any       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     str:
						self.config_tester.run_common_tests()

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase							)





@require_tf
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[Any]       		=				(
			    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
			)
			lowerCamelCase__ :		Dict       		=				False
			lowerCamelCase__ :		Tuple       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   TFFunnelModelTester(self     ,					base=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase							)

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

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_base_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase							)


