






import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       int       						= torch.exp(snake_case_			)
						__magic_name__      :       str       						= torch.sum(snake_case_,   dim=1			)  # sum of exp(x_i)
						__magic_name__      :       Optional[Any]       						= torch.sum(x * exp_x,   dim=1			)  # sum of x_i * exp(x_i)
						return torch.log(snake_case_			) - B / A





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				str:
							super().__init__()
							__magic_name__      :       Tuple       						= config.output_attentions
							__magic_name__      :       Optional[int]       						= config.output_hidden_states
							__magic_name__      :       List[Any]       						= nn.ModuleList([BertLayer(__a			) for _ in range(config.num_hidden_layers			)]			)
							__magic_name__      :       List[Any]       						= nn.ModuleList([BertHighway(__a			) for _ in range(config.num_hidden_layers			)]			)

							__magic_name__      :       List[Any]       						= [-1 for _ in range(config.num_hidden_layers			)]
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Tuple:
							if (type(__a			) is float) or (type(__a			) is int):
													for i in range(len(self.early_exit_entropy			)			):
																			__magic_name__      :       Union[str, Any]       						= x
							else:
													__magic_name__      :       Optional[int]       						= x
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Optional[Any]       						= pooler.state_dict()
							for highway in self.highway:
													for name, param in highway.pooler.state_dict().items():
																			param.copy_(loaded_model[name]			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       )							->				Tuple:
							__magic_name__      :       Optional[Any]       						= ()
							__magic_name__      :       Optional[Any]       						= ()
							__magic_name__      :       Union[str, Any]       						= ()
							for i, layer_module in enumerate(self.layer			):
													if self.output_hidden_states:
																			__magic_name__      :       Any       						= all_hidden_states + (hidden_states,)

													__magic_name__      :       Dict       						= layer_module(
													    __a      ,       __a      ,       head_mask[i]      ,       __a      ,       __a			)
													__magic_name__      :       int       						= layer_outputs[0]

													if self.output_attentions:
																			__magic_name__      :       Optional[int]       						= all_attentions + (layer_outputs[1],)

													__magic_name__      :       Tuple       						= (hidden_states,)
													if self.output_hidden_states:
																			__magic_name__      :       Any       						= current_outputs + (all_hidden_states,)
													if self.output_attentions:
																			__magic_name__      :       Any       						= current_outputs + (all_attentions,)

													__magic_name__      :       Dict       						= self.highway[i](__a			)
													# logits, pooled_output

													if not self.training:
																			__magic_name__      :       str       						= highway_exit[0]
																			__magic_name__      :       List[str]       						= entropy(__a			)
																			__magic_name__      :       int       						= highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																			__magic_name__      :       int       						= all_highway_exits + (highway_exit,)

																			if highway_entropy < self.early_exit_entropy[i]:
																									__magic_name__      :       List[str]       						= (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																									raise HighwayException(__a      ,       i + 1			)
													else:
																			__magic_name__      :       int       						= all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
													__magic_name__      :       List[Any]       						= all_hidden_states + (hidden_states,)

							__magic_name__      :       Optional[int]       						= (hidden_states,)
							if self.output_hidden_states:
													__magic_name__      :       List[Any]       						= outputs + (all_hidden_states,)
							if self.output_attentions:
													__magic_name__      :       Any       						= outputs + (all_attentions,)

							__magic_name__      :       Optional[int]       						= outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''						,  UpperCamelCase__						,  )
class 	snake_case__ (							UpperCamelCase__				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				Optional[Any]:
							super().__init__(__a			)
							__magic_name__      :       Dict       						= config

							__magic_name__      :       List[str]       						= BertEmbeddings(__a			)
							__magic_name__      :       List[Any]       						= DeeBertEncoder(__a			)
							__magic_name__      :       Optional[Any]       						= BertPooler(__a			)

							self.init_weights()
	def       __magic_name__    (    self			)							->				str:
							self.encoder.init_highway_pooler(self.pooler			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							return self.embeddings.word_embeddings
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[Any]:
							__magic_name__      :       Union[str, Any]       						= value
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Optional[Any]:
							for layer, heads in heads_to_prune.items():
													self.encoder.layer[layer].attention.prune_heads(__a			)







	@add_start_docstrings_to_model_forward(__a			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       )							->				Tuple:
							if input_ids is not None and inputs_embeds is not None:
													raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""			)
							elif input_ids is not None:
													__magic_name__      :       Dict       						= input_ids.size()
							elif inputs_embeds is not None:
													__magic_name__      :       Any       						= inputs_embeds.size()[:-1]
							else:
													raise ValueError("""You have to specify either input_ids or inputs_embeds"""			)

							__magic_name__      :       Tuple       						= input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
													__magic_name__      :       Optional[Any]       						= torch.ones(__a      ,       device=__a			)
							if encoder_attention_mask is None:
													__magic_name__      :       List[str]       						= torch.ones(__a      ,       device=__a			)
							if token_type_ids is None:
													__magic_name__      :       int       						= torch.zeros(__a      ,       dtype=torch.long      ,       device=__a			)

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							__magic_name__      :       int       						= self.get_extended_attention_mask(__a      ,       __a      ,       __a			)

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
													__magic_name__      :       Any       						= encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
													__magic_name__      :       Tuple       						= encoder_attention_mask[:, None, None, :]

							__magic_name__      :       str       						= encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()			).dtype			)  # fp16 compatibility
							__magic_name__      :       Optional[Any]       						= (1.0 - encoder_extended_attention_mask) * -1_00_00.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							__magic_name__      :       Optional[int]       						= self.get_head_mask(__a      ,       self.config.num_hidden_layers			)

							__magic_name__      :       Any       						= self.embeddings(
							    input_ids=__a      ,       position_ids=__a      ,       token_type_ids=__a      ,       inputs_embeds=__a			)
							__magic_name__      :       str       						= self.encoder(
							    __a      ,       attention_mask=__a      ,       head_mask=__a      ,       encoder_hidden_states=__a      ,       encoder_attention_mask=__a      ,       )
							__magic_name__      :       List[str]       						= encoder_outputs[0]
							__magic_name__      :       List[Any]       						= self.pooler(__a			)

							__magic_name__      :       str       						= (
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class 	snake_case__ (							UpperCamelCase__				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				str:
							__magic_name__      :       List[str]       						= message
							__magic_name__      :       Dict       						= exit_layer  # start from 1!





class 	snake_case__ (							nn.Module				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				str:
							super().__init__()
							__magic_name__      :       Any       						= BertPooler(__a			)
							__magic_name__      :       Optional[int]       						= nn.Dropout(config.hidden_dropout_prob			)
							__magic_name__      :       int       						= nn.Linear(config.hidden_size      ,       config.num_labels			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				str:
							# Pooler
							__magic_name__      :       Any       						= encoder_outputs[0]
							__magic_name__      :       Tuple       						= self.pooler(__a			)
							# "return" pooler_output

							# BertModel
							__magic_name__      :       List[str]       						= (pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							__magic_name__      :       Optional[Any]       						= bmodel_output[1]

							__magic_name__      :       Any       						= self.dropout(__a			)
							__magic_name__      :       List[str]       						= self.classifier(__a			)

							return logits, pooled_output





@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''						,  UpperCamelCase__						,  )
class 	snake_case__ (							UpperCamelCase__				):
	def __init__(    self      ,       lowerCAmelCase__			)							->				Optional[Any]:
							super().__init__(__a			)
							__magic_name__      :       Dict       						= config.num_labels
							__magic_name__      :       Optional[int]       						= config.num_hidden_layers

							__magic_name__      :       Union[str, Any]       						= DeeBertModel(__a			)
							__magic_name__      :       Dict       						= nn.Dropout(config.hidden_dropout_prob			)
							__magic_name__      :       str       						= nn.Linear(config.hidden_size      ,       self.config.num_labels			)

							self.init_weights()







	@add_start_docstrings_to_model_forward(__a			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=-1      ,       lowerCAmelCase__=False      ,       )							->				List[Any]:
							__magic_name__      :       List[Any]       						= self.num_layers
							try:
													__magic_name__      :       int       						= self.bert(
													    __a      ,       attention_mask=__a      ,       token_type_ids=__a      ,       position_ids=__a      ,       head_mask=__a      ,       inputs_embeds=__a      ,       )
													# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

													__magic_name__      :       Dict       						= outputs[1]

													__magic_name__      :       Optional[Any]       						= self.dropout(__a			)
													__magic_name__      :       str       						= self.classifier(__a			)
													__magic_name__      :       int       						= (logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
													__magic_name__      :       Union[str, Any]       						= e.message
													__magic_name__      :       List[str]       						= e.exit_layer
													__magic_name__      :       List[Any]       						= outputs[0]

							if not self.training:
													__magic_name__      :       Union[str, Any]       						= entropy(__a			)
													__magic_name__      :       Optional[int]       						= []
													__magic_name__      :       Union[str, Any]       						= []
							if labels is not None:
													if self.num_labels == 1:
																			#  We are doing regression
																			__magic_name__      :       str       						= MSELoss()
																			__magic_name__      :       Dict       						= loss_fct(logits.view(-1			)      ,       labels.view(-1			)			)
													else:
																			__magic_name__      :       List[str]       						= CrossEntropyLoss()
																			__magic_name__      :       Optional[int]       						= loss_fct(logits.view(-1      ,       self.num_labels			)      ,       labels.view(-1			)			)

													# work with highway exits
													__magic_name__      :       str       						= []
													for highway_exit in outputs[-1]:
																			__magic_name__      :       str       						= highway_exit[0]
																			if not self.training:
																									highway_logits_all.append(__a			)
																									highway_entropy.append(highway_exit[2]			)
																			if self.num_labels == 1:
																									#  We are doing regression
																									__magic_name__      :       Dict       						= MSELoss()
																									__magic_name__      :       Any       						= loss_fct(highway_logits.view(-1			)      ,       labels.view(-1			)			)
																			else:
																									__magic_name__      :       Union[str, Any]       						= CrossEntropyLoss()
																									__magic_name__      :       Optional[int]       						= loss_fct(highway_logits.view(-1      ,       self.num_labels			)      ,       labels.view(-1			)			)
																			highway_losses.append(__a			)

													if train_highway:
																			__magic_name__      :       Optional[Any]       						= (sum(highway_losses[:-1]			),) + outputs
																			# exclude the final highway, of course
													else:
																			__magic_name__      :       List[str]       						= (loss,) + outputs
							if not self.training:
													__magic_name__      :       str       						= outputs + ((original_entropy, highway_entropy), exit_layer)
													if output_layer >= 0:
																			__magic_name__      :       Any       						= (
																			    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																			)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)








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 	snake_case__ :
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=13      ,       lowerCAmelCase__=30      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=32      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=37      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=10      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=2      ,       )							->				Optional[Any]:
							__magic_name__      :       Optional[Any]       						= parent
							__magic_name__      :       List[str]       						= batch_size
							__magic_name__      :       Union[str, Any]       						= image_size
							__magic_name__      :       Optional[Any]       						= patch_size
							__magic_name__      :       Union[str, Any]       						= num_channels
							__magic_name__      :       Union[str, Any]       						= is_training
							__magic_name__      :       Union[str, Any]       						= use_labels
							__magic_name__      :       Tuple       						= hidden_size
							__magic_name__      :       List[str]       						= num_hidden_layers
							__magic_name__      :       Optional[Any]       						= num_attention_heads
							__magic_name__      :       Tuple       						= intermediate_size
							__magic_name__      :       Union[str, Any]       						= hidden_act
							__magic_name__      :       str       						= hidden_dropout_prob
							__magic_name__      :       List[str]       						= attention_probs_dropout_prob
							__magic_name__      :       Tuple       						= type_sequence_label_size
							__magic_name__      :       Any       						= initializer_range
							__magic_name__      :       str       						= scope
							__magic_name__      :       str       						= encoder_stride

							# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
							__magic_name__      :       Dict       						= (image_size // patch_size) ** 2
							__magic_name__      :       int       						= num_patches + 2
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Union[str, Any]       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

							__magic_name__      :       Tuple       						= None
							if self.use_labels:
													__magic_name__      :       Any       						= ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)

							__magic_name__      :       Union[str, Any]       						= self.get_config()

							return config, pixel_values, labels
	def       __magic_name__    (    self			)							->				List[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=lowerCAmelCase__      ,       initializer_range=self.initializer_range      ,       encoder_stride=self.encoder_stride      ,       )
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       int       						= TFDeiTModel(config=lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Optional[Any]:
							__magic_name__      :       int       						= TFDeiTForMaskedImageModeling(config=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(
							    result.reconstruction.shape      ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

							# test greyscale images
							__magic_name__      :       Tuple       						= 1
							__magic_name__      :       List[Any]       						= TFDeiTForMaskedImageModeling(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
							__magic_name__      :       Any       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(result.reconstruction.shape      ,       (self.batch_size, 1, self.image_size, self.image_size)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= self.type_sequence_label_size
							__magic_name__      :       str       						= TFDeiTForImageClassification(lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)

							# test greyscale images
							__magic_name__      :       int       						= 1
							__magic_name__      :       List[str]       						= TFDeiTForImageClassification(lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
							__magic_name__      :       Dict       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)







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





@require_tf
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					int											=   (
	    (
	        TFDeiTModel,
	        TFDeiTForImageClassification,
	        TFDeiTForImageClassificationWithTeacher,
	        TFDeiTForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase__						:					Any											=   (
	    {
	        '''feature-extraction''': TFDeiTModel,
	        '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
	    }
	    if is_tf_available()
	    else {}
	)

	lowercase__						:					int											=   False
	lowercase__						:					List[Any]											=   False
	lowercase__						:					Tuple											=   False
	lowercase__						:					int											=   False
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       str       						= TFDeiTModelTester(self			)
							__magic_name__      :       Optional[Any]       						= ConfigTester(self      ,       config_class=lowerCAmelCase__      ,       has_text_modality=lowerCAmelCase__      ,       hidden_size=37			)
	def       __magic_name__    (    self			)							->				Tuple:
							self.config_tester.run_common_tests()
	@unittest.skip(reason="""DeiT does not use inputs_embeds"""			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							pass
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__						,__magic_name__      :       Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       List[Any]       						= model_class(lowerCAmelCase__			)
													self.assertIsInstance(model.get_input_embeddings()      ,       (tf.keras.layers.Layer)			)
													__magic_name__      :       Tuple       						= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__      ,       tf.keras.layers.Dense			)			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__						,__magic_name__      :       str       						= self.model_tester.prepare_config_and_inputs_for_common()

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

													__magic_name__      :       List[str]       						= ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Any       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__=False			)							->				str:
							__magic_name__      :       Any       						= super()._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__      ,       return_labels=lowerCAmelCase__			)

							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       __magic_name__    (    self			)							->				Dict:
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__      :       int       						= TFDeiTModel.from_pretrained(lowerCAmelCase__			)
													self.assertIsNotNone(lowerCAmelCase__			)



def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       List[Any]       						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image





@require_tf
@require_vision
class 	snake_case__ (							unittest.TestCase				):
	@cached_property
	def       __magic_name__    (    self			)							->				Optional[int]:
							return (
							    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""			)
							    if is_vision_available()
							    else None
							)







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

							__magic_name__      :       Any       						= self.default_image_processor
							__magic_name__      :       Tuple       						= prepare_img()
							__magic_name__      :       Tuple       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""tf"""			)

							# forward pass
							__magic_name__      :       Dict       						= model(**lowerCAmelCase__			)

							# verify the logits
							__magic_name__      :       Optional[Any]       						= tf.TensorShape((1, 10_00)			)
							self.assertEqual(outputs.logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			)

							self.assertTrue(np.allclose(outputs.logits[0, :3]      ,       lowerCAmelCase__      ,       atol=1e-4			)			)
