






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


_UpperCAmelCase			:		List[str]   				=    logging.get_logger(__name__)

_UpperCAmelCase			:		List[str]   				=    {
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Any		      =    'bloom'
							UpperCamelCase_		:Optional[int]		      =    ['past_key_values']
							UpperCamelCase_		:Dict		      =    {
							    'num_hidden_layers': 'n_layer',
							    'num_attention_heads': 'n_head',
							}




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   str=250_880 ,							SCREAMING_SNAKE_CASE_     :   List[str]=64 ,							SCREAMING_SNAKE_CASE_     :   List[str]=2 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=8 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=1e-5 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.02 ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=1 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=2 ,							SCREAMING_SNAKE_CASE_     :   List[str]=False ,							SCREAMING_SNAKE_CASE_     :   int=0.0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=0.0 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=1 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=False ,							**SCREAMING_SNAKE_CASE_     :   Dict ,							):
											lowerCAmelCase__      			=						vocab_size
											# Backward compatibility with n_embed kwarg
											lowerCAmelCase__      			=						kwargs.pop('''n_embed''' ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						hidden_size if n_embed is None else n_embed
											lowerCAmelCase__      			=						n_layer
											lowerCAmelCase__      			=						n_head
											lowerCAmelCase__      			=						layer_norm_epsilon
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						use_cache
											lowerCAmelCase__      			=						pretraining_tp
											lowerCAmelCase__      			=						apply_residual_connection_post_layernorm
											lowerCAmelCase__      			=						hidden_dropout
											lowerCAmelCase__      			=						attention_dropout

											lowerCAmelCase__      			=						bos_token_id
											lowerCAmelCase__      			=						eos_token_id
											lowerCAmelCase__      			=						slow_but_exact

											super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)



class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    version.parse('1.12'					)




							def __init__(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   PretrainedConfig ,							SCREAMING_SNAKE_CASE_     :   str = "default" ,							SCREAMING_SNAKE_CASE_     :   List[PatchingSpec] = None ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							):
											super().__init__(SCREAMING_SNAKE_CASE_ ,							task=SCREAMING_SNAKE_CASE_ ,							patching_specs=SCREAMING_SNAKE_CASE_ ,							use_past=SCREAMING_SNAKE_CASE_	)
											if not getattr(self._config ,							'''pad_token_id''' ,							SCREAMING_SNAKE_CASE_	):
															# TODO: how to do that better?
															lowerCAmelCase__      			=						0




							@property
							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
											if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ ,							direction='''inputs''' ,							inverted_values_shape=SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
															lowerCAmelCase__      			=						{0: '''batch''', 1: '''sequence'''}

											return common_inputs




							@property
							def 						__snake_case					(   self     :   Any	):
											return self._config.n_layer




							@property
							def 						__snake_case					(   self     :   Optional[int]	):
											return self._config.n_head




							@property
							def 						__snake_case					(   self     :   int	):
											return 1e-3




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   "PreTrainedTokenizer" ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   int = -1 ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							SCREAMING_SNAKE_CASE_     :   Optional["TensorType"] = None ,							):
											lowerCAmelCase__      			=						super(SCREAMING_SNAKE_CASE_ ,							self	).generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE_ ,							batch_size=SCREAMING_SNAKE_CASE_ ,							seq_length=SCREAMING_SNAKE_CASE_ ,							is_pair=SCREAMING_SNAKE_CASE_ ,							framework=SCREAMING_SNAKE_CASE_	)

											# We need to order the input in the way they appears in the forward()
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

											# Need to add the past_keys
											if self.use_past:
															if not is_torch_available():
																			raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
															else:
																			import torch

																			lowerCAmelCase__			,		lowerCAmelCase__      			=						common_inputs['''input_ids'''].shape
																			# Not using the same length for past_key_values
																			lowerCAmelCase__      			=						seqlen + 2
																			lowerCAmelCase__      			=						self._config.hidden_size // self.num_attention_heads
																			lowerCAmelCase__      			=						(
																			    batch * self.num_attention_heads,
																			    head_dim,
																			    past_key_values_length,
																			)
																			lowerCAmelCase__      			=						(
																			    batch * self.num_attention_heads,
																			    past_key_values_length,
																			    head_dim,
																			)
																			lowerCAmelCase__      			=						[
																			    (torch.zeros(SCREAMING_SNAKE_CASE_	), torch.zeros(SCREAMING_SNAKE_CASE_	)) for _ in range(self.num_layers	)
																			]

											lowerCAmelCase__      			=						common_inputs['''attention_mask''']
											if self.use_past:
															lowerCAmelCase__      			=						ordered_inputs['''attention_mask'''].dtype
															lowerCAmelCase__      			=						torch.cat(
															    [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							dtype=SCREAMING_SNAKE_CASE_	)] ,							dim=1	)

											return ordered_inputs




							@property
							def 						__snake_case					(   self     :   Union[str, Any]	):
											return 13













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     :   Dict ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=13 ,							SCREAMING_SNAKE_CASE_     :   str=7 ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							SCREAMING_SNAKE_CASE_     :   List[Any]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=99 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=[1, 1, 2] ,							SCREAMING_SNAKE_CASE_     :   List[str]=1 ,							SCREAMING_SNAKE_CASE_     :   str=32 ,							SCREAMING_SNAKE_CASE_     :   Dict=4 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=8 ,							SCREAMING_SNAKE_CASE_     :   Dict=37 ,							SCREAMING_SNAKE_CASE_     :   Tuple="gelu_new" ,							SCREAMING_SNAKE_CASE_     :   Dict=0.1 ,							SCREAMING_SNAKE_CASE_     :   Any=0.1 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Tuple=512 ,							SCREAMING_SNAKE_CASE_     :   str=3 ,							SCREAMING_SNAKE_CASE_     :   int=0.02 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=3 ,							SCREAMING_SNAKE_CASE_     :   Any=4 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=False ,							):
											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 						__snake_case					(   self     :   Optional[int]	):
											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 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Dict ,							):
											lowerCAmelCase__      			=						TFFunnelModel(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						[input_ids, input_mask]
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)

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

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

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




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							):
											lowerCAmelCase__      			=						TFFunnelBaseModel(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						[input_ids, input_mask]
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)

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

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

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




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Any ,							):
											lowerCAmelCase__      			=						TFFunnelForPreTraining(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length)	)




							def 						__snake_case					(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int ,							):
											lowerCAmelCase__      			=						TFFunnelForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)	)




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




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							):
											lowerCAmelCase__      			=						self.num_choices
											lowerCAmelCase__      			=						TFFunnelForMultipleChoice(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ ,							1	) ,							(1, self.num_choices, 1)	)
											lowerCAmelCase__      			=						tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ ,							1	) ,							(1, self.num_choices, 1)	)
											lowerCAmelCase__      			=						tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ ,							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(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_choices)	)




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							):
											lowerCAmelCase__      			=						self.num_labels
											lowerCAmelCase__      			=						TFFunnelForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)	)




							def 						__snake_case					(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							):
											lowerCAmelCase__      			=						TFFunnelForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											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 						__snake_case					(   self     :   List[str]	):
											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_       (				snake_case__     ,    snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Dict		      =    (
							    (
							        TFFunnelModel,
							        TFFunnelForMaskedLM,
							        TFFunnelForPreTraining,
							        TFFunnelForQuestionAnswering,
							        TFFunnelForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							UpperCamelCase_		:Dict		      =    (
							    {
							        'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
							        'fill-mask': TFFunnelForMaskedLM,
							        'question-answering': TFFunnelForQuestionAnswering,
							        'text-classification': TFFunnelForSequenceClassification,
							        'token-classification': TFFunnelForTokenClassification,
							        'zero-shot': TFFunnelForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase_		:Tuple		      =    False
							UpperCamelCase_		:Tuple		      =    False




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						TFFunnelModelTester(self	)
											lowerCAmelCase__      			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_	)




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




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)



@require_tf
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Optional[Any]		      =    (
							    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
							)
							UpperCamelCase_		:int		      =    False
							UpperCamelCase_		:Any		      =    False




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						TFFunnelModelTester(self ,							base=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											self.config_tester.run_common_tests()




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_base_model(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_	)





