






import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    OpenAIGPTConfig,
							    OpenAIGPTDoubleHeadsModel,
							    OpenAIGPTForSequenceClassification,
							    OpenAIGPTLMHeadModel,
							    OpenAIGPTModel,
							)


class 							__a							:





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__=13 ,    UpperCamelCase__=7 ,    UpperCamelCase__=True ,    UpperCamelCase__=True ,    UpperCamelCase__=True ,    UpperCamelCase__=99 ,    UpperCamelCase__=32 ,    UpperCamelCase__=5 ,    UpperCamelCase__=4 ,    UpperCamelCase__=37 ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=512 ,    UpperCamelCase__=16 ,    UpperCamelCase__=2 ,    UpperCamelCase__=0.02 ,    UpperCamelCase__=3 ,    UpperCamelCase__=4 ,    UpperCamelCase__=None ,    ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   parent
						SCREAMING_SNAKE_CASE_   :	Tuple													=   batch_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   seq_length
						SCREAMING_SNAKE_CASE_   :	Tuple													=   is_training
						SCREAMING_SNAKE_CASE_   :	Tuple													=   use_token_type_ids
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   use_labels
						SCREAMING_SNAKE_CASE_   :	int													=   vocab_size
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	int													=   num_hidden_layers
						SCREAMING_SNAKE_CASE_   :	int													=   num_attention_heads
						SCREAMING_SNAKE_CASE_   :	Dict													=   intermediate_size
						SCREAMING_SNAKE_CASE_   :	Dict													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE_   :	Any													=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_   :	Dict													=   max_position_embeddings
						SCREAMING_SNAKE_CASE_   :	Dict													=   type_vocab_size
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   type_sequence_label_size
						SCREAMING_SNAKE_CASE_   :	Tuple													=   initializer_range
						SCREAMING_SNAKE_CASE_   :	Tuple													=   num_labels
						SCREAMING_SNAKE_CASE_   :	Dict													=   num_choices
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   scope
						SCREAMING_SNAKE_CASE_   :	Any													=   self.vocab_size - 1


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size       )

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

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   OpenAIGPTConfig(
						    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 ,    )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,    2       )

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


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    *UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   OpenAIGPTModel(config=UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()

						SCREAMING_SNAKE_CASE_   :	str													=   model(UpperCamelCase__ ,    token_type_ids=UpperCamelCase__ ,    head_mask=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   model(UpperCamelCase__ ,    token_type_ids=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   model(UpperCamelCase__       )

						self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.seq_length, self.hidden_size)       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    *UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   OpenAIGPTLMHeadModel(UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   model(UpperCamelCase__ ,    token_type_ids=UpperCamelCase__ ,    labels=UpperCamelCase__       )
						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 ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    *UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   OpenAIGPTDoubleHeadsModel(UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()

						SCREAMING_SNAKE_CASE_   :	str													=   model(UpperCamelCase__ ,    token_type_ids=UpperCamelCase__ ,    labels=UpperCamelCase__       )
						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 ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    *UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.num_labels
						SCREAMING_SNAKE_CASE_   :	int													=   OpenAIGPTForSequenceClassification(UpperCamelCase__       )
						model.to(UpperCamelCase__       )
						model.eval()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   ids_tensor([self.batch_size] ,    self.type_sequence_label_size       )
						SCREAMING_SNAKE_CASE_   :	str													=   model(UpperCamelCase__ ,    token_type_ids=UpperCamelCase__ ,    labels=UpperCamelCase__       )
						self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.prepare_config_and_inputs()
						(
						    SCREAMING_SNAKE_CASE_       
						)   :	Optional[Any]													=   config_and_inputs
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   {
						    'input_ids': input_ids,
						    'token_type_ids': token_type_ids,
						    'head_mask': head_mask,
						}

						return config, inputs_dict






@require_torch
class 							__a							(  __A ,	__A ,	__A ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						(
		    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase__      :				Union[str, Any]				    =						(
		    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
		)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
		UpperCAmelCase__      :				Dict				    =						(
		    {
		        """feature-extraction""": OpenAIGPTModel,
		        """text-classification""": OpenAIGPTForSequenceClassification,
		        """text-generation""": OpenAIGPTLMHeadModel,
		        """zero-shot""": OpenAIGPTForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
										# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
										# `OpenAIGPTConfig` 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 ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__=False       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   super()._prepare_for_class(UpperCamelCase__ ,    UpperCamelCase__ ,    return_labels=UpperCamelCase__       )

						if return_labels:
										if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
														SCREAMING_SNAKE_CASE_   :	str													=   torch.zeros(
														    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,    dtype=torch.long ,    device=UpperCamelCase__ ,    )
														SCREAMING_SNAKE_CASE_   :	List[str]													=   inputs_dict['labels']
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   inputs_dict['labels']
														SCREAMING_SNAKE_CASE_   :	str													=   torch.zeros(
														    (self.model_tester.batch_size, self.model_tester.num_choices) ,    dtype=torch.long ,    device=UpperCamelCase__ ,    )
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.zeros(
														    self.model_tester.batch_size ,    dtype=torch.long ,    device=UpperCamelCase__       )
						return inputs_dict


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   OpenAIGPTModelTester(self       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ConfigTester(self ,    config_class=UpperCamelCase__ ,    n_embd=37       )


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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_model(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head_model(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_double_lm_head_model(*UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCamelCase__       )






		@slow
		def   __snake_case			(						self       ):
						for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   OpenAIGPTModel.from_pretrained(UpperCamelCase__       )
										self.assertIsNotNone(UpperCamelCase__       )






@require_torch
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		@slow
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'       )
						model.to(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.tensor([[481, 4735, 544]] ,    dtype=torch.long ,    device=UpperCamelCase__       )  # the president is
						SCREAMING_SNAKE_CASE_   :	Any													=   [
						    481,
						    4735,
						    544,
						    246,
						    963,
						    870,
						    762,
						    239,
						    244,
						    40477,
						    244,
						    249,
						    719,
						    881,
						    487,
						    544,
						    240,
						    244,
						    603,
						    481,
						]  # the president is a very good man. " \n " i\'m sure he is, " said the

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model.generate(UpperCamelCase__ ,    do_sample=UpperCamelCase__       )
						self.assertListEqual(output_ids[0].tolist() ,    UpperCamelCase__       )







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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE_   :	Dict													=   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]       )       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    'do_resize': True,
						    'size': 20,
						    'do_center_crop': True,
						    'crop_size': 18,
						    'do_normalize': True,
						    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
						    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
						}
						SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(self.tmpdirname ,    UpperCamelCase__       )
						with open(self.image_processor_file ,    'w' ,    encoding='utf-8'       ) as fp:
										json.dump(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizerFast.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self       ):
						shutil.rmtree(self.tmpdirname       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [np.random.randint(255 ,    size=(3, 30, 400) ,    dtype=np.uinta       )]
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [Image.fromarray(np.moveaxis(UpperCamelCase__ ,    0 ,    -1       )       ) for x in image_inputs]
						return image_inputs


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname ,    use_fast=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.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 ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.tokenizer ,    UpperCamelCase__       )

						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 ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.image_processor ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer(bos_token='(BOS)' ,    eos_token='(EOS)'       )
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor(do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor.from_pretrained(
						    self.tmpdirname ,    bos_token='(BOS)' ,    eos_token='(EOS)' ,    do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

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

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	int													=   image_processor(UpperCamelCase__ ,    return_tensors='np'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   processor(images=UpperCamelCase__ ,    return_tensors='np'       )

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   'lower newer'

						SCREAMING_SNAKE_CASE_   :	Tuple													=   processor(text=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	int													=   tokenizer(UpperCamelCase__ ,    padding='max_length' ,    max_length=64       )

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

						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(UpperCamelCase__       ):
										processor()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						SCREAMING_SNAKE_CASE_   :	List[str]													=   processor.batch_decode(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.batch_decode(UpperCamelCase__       )

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

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