
"""simple docstring"""




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
						import torch

						from transformers import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						)




class 			_a						:






				def __init__(			self		:   int,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Tuple=1_4,			lowerCAmelCase__		:   int=7,			lowerCAmelCase__		:   Dict=True,			lowerCAmelCase__		:   List[str]=True,			lowerCAmelCase__		:   int=True,			lowerCAmelCase__		:   Union[str, Any]=True,			lowerCAmelCase__		:   Optional[Any]=True,			lowerCAmelCase__		:   str=9_9,			lowerCAmelCase__		:   int=3_2,			lowerCAmelCase__		:   List[str]=5,			lowerCAmelCase__		:   Union[str, Any]=4,			lowerCAmelCase__		:   Any=3_7,			lowerCAmelCase__		:   Union[str, Any]="gelu",			lowerCAmelCase__		:   str=0.1,			lowerCAmelCase__		:   int=0.1,			lowerCAmelCase__		:   Tuple=5_1_2,			lowerCAmelCase__		:   str=1_6,			lowerCAmelCase__		:   Union[str, Any]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Dict=3,			lowerCAmelCase__		:   Optional[Any]=4,			lowerCAmelCase__		:   Any=None,			)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : int         =   parent
										_UpperCamelCase      : Optional[int]         =   batch_size
										_UpperCamelCase      : List[Any]         =   seq_length
										_UpperCamelCase      : Optional[int]         =   is_training
										_UpperCamelCase      : Any         =   use_token_type_ids
										_UpperCamelCase      : List[str]         =   use_input_mask
										_UpperCamelCase      : Optional[int]         =   use_labels
										_UpperCamelCase      : Optional[Any]         =   use_mc_token_ids
										_UpperCamelCase      : Dict         =   vocab_size
										_UpperCamelCase      : Union[str, Any]         =   hidden_size
										_UpperCamelCase      : Optional[Any]         =   num_hidden_layers
										_UpperCamelCase      : Union[str, Any]         =   num_attention_heads
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : Dict         =   hidden_act
										_UpperCamelCase      : List[str]         =   hidden_dropout_prob
										_UpperCamelCase      : Tuple         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : List[str]         =   type_vocab_size
										_UpperCamelCase      : Tuple         =   type_sequence_label_size
										_UpperCamelCase      : Union[str, Any]         =   initializer_range
										_UpperCamelCase      : Any         =   num_labels
										_UpperCamelCase      : Dict         =   num_choices
										_UpperCamelCase      : Tuple         =   scope
										_UpperCamelCase      : Union[str, Any]         =   self.vocab_size - 1






				def     snake_case     (			self		:   List[Any]							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : int         =   ids_tensor([self.batch_size, self.seq_length],			self.vocab_size							)

										_UpperCamelCase      : List[Any]         =   None
										if self.use_input_mask:
																_UpperCamelCase      : str         =   random_attention_mask([self.batch_size, self.seq_length]							)

										_UpperCamelCase      : str         =   None
										if self.use_token_type_ids:
																_UpperCamelCase      : List[Any]         =   ids_tensor([self.batch_size, self.seq_length],			self.type_vocab_size							)

										_UpperCamelCase      : int         =   None
										if self.use_mc_token_ids:
																_UpperCamelCase      : List[Any]         =   ids_tensor([self.batch_size, self.num_choices],			self.seq_length							)

										_UpperCamelCase      : int         =   None
										_UpperCamelCase      : Any         =   None
										_UpperCamelCase      : Tuple         =   None
										if self.use_labels:
																_UpperCamelCase      : str         =   ids_tensor([self.batch_size],			self.type_sequence_label_size							)
																_UpperCamelCase      : List[Any]         =   ids_tensor([self.batch_size, self.seq_length],			self.num_labels							)
																_UpperCamelCase      : Tuple         =   ids_tensor([self.batch_size],			self.num_choices							)

										_UpperCamelCase      : Optional[Any]         =   self.get_config()

										_UpperCamelCase      : Union[str, Any]         =   ids_tensor([self.num_hidden_layers, self.num_attention_heads],			2							)

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										    token_type_ids,
										    mc_token_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)






				def     snake_case     (			self		:   str							)     ->				Union[str, Any]:




										'''simple docstring'''



										return CTRLConfig(
										    vocab_size=self.vocab_size,			n_embd=self.hidden_size,			n_layer=self.num_hidden_layers,			n_head=self.num_attention_heads,			n_positions=self.max_position_embeddings,			pad_token_id=self.pad_token_id,			)






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   List[str],			*lowerCAmelCase__		:   Union[str, Any]							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   CTRLModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()

										model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__,			head_mask=lowerCAmelCase__							)
										model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__							)
										_UpperCamelCase      : Optional[Any]         =   model(lowerCAmelCase__							)
										self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)							)
										self.parent.assertEqual(len(result.past_key_values							),			config.n_layer							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Optional[int],			*lowerCAmelCase__		:   Optional[int]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   CTRLLMHeadModel(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()

										_UpperCamelCase      : List[Any]         =   model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.loss.shape,			()							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)							)






				def     snake_case     (			self		:   int							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   self.prepare_config_and_inputs()

										(
										    _UpperCamelCase							
										)      : Optional[int]         =   config_and_inputs

										_UpperCamelCase      : Union[str, Any]         =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

										return config, inputs_dict






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   List[str],			*lowerCAmelCase__		:   str							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   self.num_labels
										_UpperCamelCase      : int         =   CTRLForSequenceClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : Any         =   ids_tensor([self.batch_size],			self.type_sequence_label_size							)
										_UpperCamelCase      : int         =   model(lowerCAmelCase__,			token_type_ids=lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)							)




@require_torch
class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
				UpperCamelCase								=   (CTRLLMHeadModel,) if is_torch_available() else ()
				UpperCamelCase								=   (
				    {
				        '''feature-extraction''': CTRLModel,
				        '''text-classification''': CTRLForSequenceClassification,
				        '''text-generation''': CTRLLMHeadModel,
				        '''zero-shot''': CTRLForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase								=   True
				UpperCamelCase								=   False
				UpperCamelCase								=   False






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   str							)     ->				List[str]:




										'''simple docstring'''



										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																# config could not be created.
																return True

										return False






				def     snake_case     (			self		:   Optional[int]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   CTRLModelTester(self							)
										_UpperCamelCase      : List[Any]         =   ConfigTester(self,			config_class=lowerCAmelCase__,			n_embd=3_7							)






				def     snake_case     (			self		:   Dict							)     ->				Any:




										'''simple docstring'''



										super().tearDown()
										# clean-up as much as possible GPU memory occupied by PyTorch
										gc.collect()
										torch.cuda.empty_cache()






				def     snake_case     (			self		:   Dict							)     ->				List[str]:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   Dict							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__							)






				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
				def     snake_case     (			self		:   int							)     ->				Optional[int]:




										'''simple docstring'''



										pass






				@slow
				def     snake_case     (			self		:   List[Any]							)     ->				str:




										'''simple docstring'''



										for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase      : Dict         =   CTRLModel.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)






				@unittest.skip('''The model doesn\'t support left padding'''							)  # and it's not used enough to be worth fixing :)
				def     snake_case     (			self		:   int							)     ->				Dict:




										'''simple docstring'''



										pass




@require_torch
class 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Dict							)     ->				str:




										'''simple docstring'''



										super().tearDown()
										# clean-up as much as possible GPU memory occupied by PyTorch
										gc.collect()
										torch.cuda.empty_cache()






				@slow
				def     snake_case     (			self		:   Tuple							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   CTRLLMHeadModel.from_pretrained('''ctrl'''							)
										model.to(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   torch.tensor(
										    [[1_1_8_5_9, 0, 1_6_1_1, 8]],			dtype=torch.long,			device=lowerCAmelCase__							)  # Legal the president is
										_UpperCamelCase      : List[Any]         =   [
										    1_1_8_5_9,
										    0,
										    1_6_1_1,
										    8,
										    5,
										    1_5_0,
										    2_6_4_4_9,
										    2,
										    1_9,
										    3_4_8,
										    4_6_9,
										    3,
										    2_5_9_5,
										    4_8,
										    2_0_7_4_0,
										    2_4_6_5_3_3,
										    2_4_6_5_3_3,
										    1_9,
										    3_0,
										    5,
										]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

										_UpperCamelCase      : Tuple         =   model.generate(lowerCAmelCase__,			do_sample=lowerCAmelCase__							)
										self.assertListEqual(output_ids[0].tolist(),			lowerCAmelCase__							)










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : int         =   tempfile.mkdtemp()

										_UpperCamelCase      : List[str]         =   [
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''的''',
										    '''价''',
										    '''格''',
										    '''是''',
										    '''15''',
										    '''便''',
										    '''alex''',
										    '''##andra''',
										    '''，''',
										    '''。''',
										    '''-''',
										    '''t''',
										    '''shirt''',
										]
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['''vocab_file''']							)
										with open(self.vocab_file,			'''w''',			encoding='''utf-8'''							) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

										_UpperCamelCase      : Dict         =   {
										    '''do_resize''': True,
										    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
										    '''do_center_crop''': True,
										    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
										    '''do_normalize''': True,
										    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										    '''do_convert_rgb''': True,
										}
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			lowerCAmelCase__							)
										with open(self.image_processor_file,			'''w''',			encoding='''utf-8'''							) as fp:
																json.dump(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   str,			**lowerCAmelCase__		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										return BertTokenizer.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any],			**lowerCAmelCase__		:   Tuple							)     ->				str:




										'''simple docstring'''



										return BertTokenizerFast.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Any,			**lowerCAmelCase__		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname							)






				def     snake_case     (			self		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   [np.random.randint(2_5_5,			size=(3, 3_0, 4_0_0),			dtype=np.uinta							)]

										_UpperCamelCase      : List[Any]         =   [Image.fromarray(np.moveaxis(lowerCAmelCase__,			0,			-1							)							) for x in image_inputs]

										return image_inputs






				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_tokenizer()
										_UpperCamelCase      : int         =   self.get_rust_tokenizer()
										_UpperCamelCase      : int         =   self.get_image_processor()

										_UpperCamelCase      : Tuple         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname							)

										self.assertEqual(processor_slow.tokenizer.get_vocab(),			tokenizer_slow.get_vocab()							)
										self.assertEqual(processor_fast.tokenizer.get_vocab(),			tokenizer_fast.get_vocab()							)
										self.assertEqual(tokenizer_slow.get_vocab(),			tokenizer_fast.get_vocab()							)
										self.assertIsInstance(processor_slow.tokenizer,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.tokenizer,			lowerCAmelCase__							)

										self.assertEqual(processor_slow.image_processor.to_json_string(),			image_processor.to_json_string()							)
										self.assertEqual(processor_fast.image_processor.to_json_string(),			image_processor.to_json_string()							)
										self.assertIsInstance(processor_slow.image_processor,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.image_processor,			lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										_UpperCamelCase      : Dict         =   self.get_tokenizer(cls_token='''(CLS)''',			sep_token='''(SEP)'''							)
										_UpperCamelCase      : List[str]         =   self.get_image_processor(do_normalize=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname,			cls_token='''(CLS)''',			sep_token='''(SEP)''',			do_normalize=lowerCAmelCase__							)

										self.assertEqual(processor.tokenizer.get_vocab(),			tokenizer_add_kwargs.get_vocab()							)
										self.assertIsInstance(processor.tokenizer,			lowerCAmelCase__							)

										self.assertEqual(processor.image_processor.to_json_string(),			image_processor_add_kwargs.to_json_string()							)
										self.assertIsInstance(processor.image_processor,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.get_image_processor()
										_UpperCamelCase      : str         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.prepare_image_inputs()

										_UpperCamelCase      : Any         =   image_processor(lowerCAmelCase__,			return_tensors='''np'''							)
										_UpperCamelCase      : Any         =   processor(images=lowerCAmelCase__,			return_tensors='''np'''							)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),			input_processor[key].sum(),			delta=1e-2							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Any         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   '''Alexandra，T-shirt的价格是15便士。'''

										_UpperCamelCase      : List[str]         =   processor(text=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   tokenizer(lowerCAmelCase__							)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],			encoded_processor[key]							)






				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Dict         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : Union[str, Any]         =   self.prepare_image_inputs()

										_UpperCamelCase      : str         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

										self.assertListEqual(list(inputs.keys()							),			['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']							)

										# test if it raises when no input is passed
										with pytest.raises(lowerCAmelCase__							):
																processor()






				def     snake_case     (			self		:   Optional[Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_image_processor()
										_UpperCamelCase      : int         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase      : List[Any]         =   processor.batch_decode(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   tokenizer.batch_decode(lowerCAmelCase__							)

										self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_image_processor()
										_UpperCamelCase      : Optional[int]         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : int         =   self.prepare_image_inputs()

										_UpperCamelCase      : Dict         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

										self.assertListEqual(list(inputs.keys()							),			processor.model_input_names							)



