

"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDeiTForImageClassification,
						    TFDeiTForImageClassificationWithTeacher,
						    TFDeiTForMaskedImageModeling,
						    TFDeiTModel,
						)
						from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor





class  lowerCamelCase    :

			def __init__(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Tuple=1_3     ,					__UpperCAmelCase					:    int=3_0     ,					__UpperCAmelCase					:    Optional[Any]=2     ,					__UpperCAmelCase					:    Union[str, Any]=3     ,					__UpperCAmelCase					:    Tuple=True     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    Any=3_2     ,					__UpperCAmelCase					:    Any=2     ,					__UpperCAmelCase					:    Dict=4     ,					__UpperCAmelCase					:    Optional[Any]=3_7     ,					__UpperCAmelCase					:    Dict="gelu"     ,					__UpperCAmelCase					:    List[str]=0.1     ,					__UpperCAmelCase					:    List[str]=0.1     ,					__UpperCAmelCase					:    Dict=1_0     ,					__UpperCAmelCase					:    Optional[int]=0.02     ,					__UpperCAmelCase					:    Any=3     ,					__UpperCAmelCase					:    List[str]=None     ,					__UpperCAmelCase					:    Optional[Any]=2     ,					) ->     Any:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   patch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   scope
						SCREAMING_SNAKE_CASE__										=   encoder_stride

						# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
						SCREAMING_SNAKE_CASE__										=   (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE__										=   num_patches + 2

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

						SCREAMING_SNAKE_CASE__										=   self.get_config()

						return config, pixel_values, labels

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     str:
						return DeiTConfig(
						    image_size=self.image_size     ,					patch_size=self.patch_size     ,					num_channels=self.num_channels     ,					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     ,					is_decoder=__UpperCAmelCase     ,					initializer_range=self.initializer_range     ,					encoder_stride=self.encoder_stride     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    List[Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   TFDeiTModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   TFDeiTForMaskedImageModeling(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(
						    result.reconstruction.shape     ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

						# test greyscale images
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__										=   TFDeiTForMaskedImageModeling(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.reconstruction.shape     ,					(self.batch_size, 1, self.image_size, self.image_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    List[Any]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   TFDeiTForImageClassification(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					labels=__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)							)

						# test greyscale images
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__										=   TFDeiTForImageClassification(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					labels=__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_tf
class  lowerCamelCase    (A__      ,A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[Any]       		=				(
			    (
			        TFDeiTModel,
			        TFDeiTForImageClassification,
			        TFDeiTForImageClassificationWithTeacher,
			        TFDeiTForMaskedImageModeling,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__ :		List[str]       		=				(
			    {
			        'feature-extraction': TFDeiTModel,
			        'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__ :		List[Any]       		=				False
			lowerCamelCase__ :		str       		=				False
			lowerCamelCase__ :		Any       		=				False
			lowerCamelCase__ :		Dict       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   TFDeiTModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=3_7							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""DeiT does not use inputs_embeds"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[int]:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
									self.assertIsInstance(model.get_input_embeddings()     ,					(tf.keras.layers.Layer)							)
									SCREAMING_SNAKE_CASE__										=   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(__UpperCAmelCase     ,					tf.keras.layers.Dense							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   inspect.signature(model.call							)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE__										=   [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE__										=   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase							)

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

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

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Optional[int]=False							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   super()._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase							)

						if return_labels:
									if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call							).parameters:
												del inputs_dict["labels"]

						return inputs_dict





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Union[str, Any]:
						for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__										=   TFDeiTModel.from_pretrained(__UpperCAmelCase							)
									self.assertIsNotNone(__UpperCAmelCase							)

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
			return image





@require_tf
@require_vision
class  lowerCamelCase    (unittest.TestCase      ):

			@cached_property
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						return (
						    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)
						    if is_vision_available()
						    else None
						)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)

						SCREAMING_SNAKE_CASE__										=   self.default_image_processor
						SCREAMING_SNAKE_CASE__										=   prepare_img()
						SCREAMING_SNAKE_CASE__										=   image_processor(images=__UpperCAmelCase     ,					return_tensors="""tf"""							)

						# forward pass
						SCREAMING_SNAKE_CASE__										=   model(**__UpperCAmelCase							)

						# verify the logits
						SCREAMING_SNAKE_CASE__										=   tf.TensorShape((1, 1_0_0_0)							)
						self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tf.constant([-1.0_266, 0.1_912, -1.2_861]							)

						self.assertTrue(np.allclose(outputs.logits[0, :3]     ,					__UpperCAmelCase     ,					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  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							)


