


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Optional[int]       					=     StableDiffusionDiffEditPipeline
       lowercase_					:      Any       					=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
       lowercase_					:      Optional[int]       					=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
       lowercase_					:      str       					=     frozenset(
           []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       lowercase_					:      Dict       					=     frozenset([]  )
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase			:				Optional[Any] 						=						UNetaDConditionModel(
               block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), cross_attention_dim=32, attention_head_dim=(2, 4), use_linear_projection=lowerCamelCase, )
           _lowercase			:				Union[str, Any] 						=						DDIMScheduler(
               beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='scaled_linear', clip_sample=lowerCamelCase, set_alpha_to_one=lowerCamelCase, )
           _lowercase			:				Optional[int] 						=						DDIMInverseScheduler(
               beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='scaled_linear', clip_sample=lowerCamelCase, set_alpha_to_zero=lowerCamelCase, )
           torch.manual_seed(0)
           _lowercase			:				Optional[Any] 						=						AutoencoderKL(
               block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, sample_size=1_28, )
           torch.manual_seed(0)
           _lowercase			:				Dict 						=						CLIPTextConfig(
               bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=10_00, hidden_act='gelu', projection_dim=5_12, )
           _lowercase			:				Union[str, Any] 						=						CLIPTextModel(lowerCamelCase)
           _lowercase			:				Any 						=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _lowercase			:				List[Any] 						=						{
               'unet': unet,
               'scheduler': scheduler,
               'inverse_scheduler': inverse_scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }

           return components
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=0)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						floats_tensor((1, 16, 16), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase			:				Optional[int] 						=						floats_tensor((1, 2, 4, 16, 16), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           if str(lowerCamelCase).startswith('mps'):
               _lowercase			:				List[str] 						=						torch.manual_seed(lowerCamelCase)
           else:
               _lowercase			:				Optional[int] 						=						torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						{
               'prompt': 'a dog and a newt',
               'mask_image': mask,
               'image_latents': latents,
               'generator': generator,
               'num_inference_steps': 2,
               'inpaint_strength': 1.0,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }

           return inputs
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=0)			->  Any:




           """simple docstring"""

           _lowercase			:				Dict 						=						floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase			:				Any 						=						image.cpu().permute(0, 2, 3, 1)[0]
           _lowercase			:				str 						=						Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB')
           if str(lowerCamelCase).startswith('mps'):
               _lowercase			:				List[str] 						=						torch.manual_seed(lowerCamelCase)
           else:
               _lowercase			:				Union[str, Any] 						=						torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase			:				Any 						=						{
               'image': image,
               'source_prompt': 'a cat and a frog',
               'target_prompt': 'a dog and a newt',
               'generator': generator,
               'num_inference_steps': 2,
               'num_maps_per_mask': 2,
               'mask_encode_strength': 1.0,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }

           return inputs
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=0)			->  int:




           """simple docstring"""

           _lowercase			:				int 						=						floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase			:				Optional[int] 						=						image.cpu().permute(0, 2, 3, 1)[0]
           _lowercase			:				Optional[int] 						=						Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB')
           if str(lowerCamelCase).startswith('mps'):
               _lowercase			:				Tuple 						=						torch.manual_seed(lowerCamelCase)
           else:
               _lowercase			:				int 						=						torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase			:				List[Any] 						=						{
               'image': image,
               'prompt': 'a cat and a frog',
               'generator': generator,
               'num_inference_steps': 2,
               'inpaint_strength': 1.0,
               'guidance_scale': 6.0,
               'decode_latents': True,
               'output_type': 'numpy',
           }
           return inputs
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           if not hasattr(self.pipeline_class, '_optional_components'):
               return

           _lowercase			:				Optional[Any] 						=						self.get_dummy_components()
           _lowercase			:				str 						=						self.pipeline_class(**lowerCamelCase)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           # set all optional components to None and update pipeline config accordingly
           for optional_component in pipe._optional_components:
               setattr(lowerCamelCase, lowerCamelCase, lowerCamelCase)
           pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})

           _lowercase			:				Dict 						=						self.get_dummy_inputs(lowerCamelCase)
           _lowercase			:				str 						=						pipe(**lowerCamelCase)[0]

           with tempfile.TemporaryDirectory() as tmpdir:
               pipe.save_pretrained(lowerCamelCase)
               _lowercase			:				Dict 						=						self.pipeline_class.from_pretrained(lowerCamelCase)
               pipe_loaded.to(lowerCamelCase)
               pipe_loaded.set_progress_bar_config(disable=lowerCamelCase)

           for optional_component in pipe._optional_components:
               self.assertTrue(
                   getattr(lowerCamelCase, lowerCamelCase) is None, F'''`{optional_component}` did not stay set to None after loading.''', )

           _lowercase			:				Any 						=						self.get_dummy_inputs(lowerCamelCase)
           _lowercase			:				List[str] 						=						pipe_loaded(**lowerCamelCase)[0]

           _lowercase			:				Tuple 						=						np.abs(output - output_loaded).max()
           self.assertLess(lowerCamelCase, 1E-4)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						'cpu'

           _lowercase			:				Optional[int] 						=						self.get_dummy_components()
           _lowercase			:				List[str] 						=						self.pipeline_class(**lowerCamelCase)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Tuple 						=						self.get_dummy_mask_inputs(lowerCamelCase)
           _lowercase			:				List[Any] 						=						pipe.generate_mask(**lowerCamelCase)
           _lowercase			:				int 						=						mask[0, -3:, -3:]

           self.assertEqual(mask.shape, (1, 16, 16))
           _lowercase			:				Tuple 						=						np.array([0] * 9)
           _lowercase			:				Optional[Any] 						=						np.abs(mask_slice.flatten() - expected_slice).max()
           self.assertLessEqual(lowerCamelCase, 1E-3)
           self.assertEqual(mask[0, -3, -4], 0)
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Tuple 						=						'cpu'

           _lowercase			:				List[Any] 						=						self.get_dummy_components()
           _lowercase			:				int 						=						self.pipeline_class(**lowerCamelCase)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				List[str] 						=						self.get_dummy_inversion_inputs(lowerCamelCase)
           _lowercase			:				List[Any] 						=						pipe.invert(**lowerCamelCase).images
           _lowercase			:				List[str] 						=						image[0, -1, -3:, -3:]

           self.assertEqual(image.shape, (2, 32, 32, 3))
           _lowercase			:				Union[str, Any] 						=						np.array(
               [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9], )
           _lowercase			:				List[Any] 						=						np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(lowerCamelCase, 1E-3)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           super().test_inference_batch_single_identical(expected_max_diff=5E-3)




       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[str] 						=						'cpu'

           _lowercase			:				Dict 						=						self.get_dummy_components()

           _lowercase			:				List[str] 						=						{'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
           _lowercase			:				Tuple 						=						DPMSolverMultistepScheduler(**lowerCamelCase)
           _lowercase			:				List[Any] 						=						DPMSolverMultistepInverseScheduler(**lowerCamelCase)

           _lowercase			:				Any 						=						self.pipeline_class(**lowerCamelCase)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				List[Any] 						=						self.get_dummy_inversion_inputs(lowerCamelCase)
           _lowercase			:				Any 						=						pipe.invert(**lowerCamelCase).images
           _lowercase			:				List[str] 						=						image[0, -1, -3:, -3:]

           self.assertEqual(image.shape, (2, 32, 32, 3))
           _lowercase			:				str 						=						np.array(
               [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9], )
           _lowercase			:				str 						=						np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(lowerCamelCase, 1E-3)






@require_torch_gpu
@slow
class _lowerCamelCase(		unittest.TestCase  ):
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       @classmethod
       def      UpperCamelCase	(   cls)			->  Dict:




           """simple docstring"""

           _lowercase			:				Tuple 						=						load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png')

           _lowercase			:				str 						=						raw_image.convert('RGB').resize((7_68, 7_68))

           _lowercase			:				Union[str, Any] 						=						raw_image
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						torch.manual_seed(0)

           _lowercase			:				Any 						=						StableDiffusionDiffEditPipeline.from_pretrained(
               'stabilityai/stable-diffusion-2-1', safety_checker=lowerCamelCase, torch_dtype=torch.floataa)
           _lowercase			:				Union[str, Any] 						=						DDIMScheduler.from_config(pipe.scheduler.config)
           _lowercase			:				Optional[Any] 						=						DDIMInverseScheduler.from_config(pipe.scheduler.config)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Any 						=						'a bowl of fruit'
           _lowercase			:				Any 						=						'a bowl of pears'

           _lowercase			:				Optional[int] 						=						pipe.generate_mask(
               image=self.raw_image, source_prompt=lowerCamelCase, target_prompt=lowerCamelCase, generator=lowerCamelCase, )

           _lowercase			:				Dict 						=						pipe.invert(
               prompt=lowerCamelCase, image=self.raw_image, inpaint_strength=0.7, generator=lowerCamelCase).latents

           _lowercase			:				Dict 						=						pipe(
               prompt=lowerCamelCase, mask_image=lowerCamelCase, image_latents=lowerCamelCase, generator=lowerCamelCase, negative_prompt=lowerCamelCase, inpaint_strength=0.7, output_type='numpy', ).images[0]

           _lowercase			:				Optional[int] 						=						(
               np.array(
                   load_image(
                       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                       '/diffedit/pears.png').resize((7_68, 7_68)))
               / 2_55
           )
           assert np.abs((expected_image - image).max()) < 5E-1




       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Dict 						=						torch.manual_seed(0)

           _lowercase			:				Union[str, Any] 						=						StableDiffusionDiffEditPipeline.from_pretrained(
               'stabilityai/stable-diffusion-2-1', safety_checker=lowerCamelCase, torch_dtype=torch.floataa)
           _lowercase			:				str 						=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
           _lowercase			:				Optional[int] 						=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Optional[int] 						=						'a bowl of fruit'
           _lowercase			:				str 						=						'a bowl of pears'

           _lowercase			:				int 						=						pipe.generate_mask(
               image=self.raw_image, source_prompt=lowerCamelCase, target_prompt=lowerCamelCase, generator=lowerCamelCase, )

           _lowercase			:				Optional[int] 						=						pipe.invert(
               prompt=lowerCamelCase, image=self.raw_image, inpaint_strength=0.7, generator=lowerCamelCase, num_inference_steps=25, ).latents

           _lowercase			:				Optional[Any] 						=						pipe(
               prompt=lowerCamelCase, mask_image=lowerCamelCase, image_latents=lowerCamelCase, generator=lowerCamelCase, negative_prompt=lowerCamelCase, inpaint_strength=0.7, num_inference_steps=25, output_type='numpy', ).images[0]

           _lowercase			:				int 						=						(
               np.array(
                   load_image(
                       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                       '/diffedit/pears.png').resize((7_68, 7_68)))
               / 2_55
           )
           assert np.abs((expected_image - image).max()) < 5E-1




"""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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  tempfile.mkdtemp()

							lowerCamelCase_					=  [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							lowerCamelCase_					=  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]   )   )

							lowerCamelCase_					=  {
							    '''do_resize''': True,
							    '''size''': 20,
							    '''do_center_crop''': True,
							    '''crop_size''': 18,
							    '''do_normalize''': True,
							    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							}
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						UpperCAmelCase   )
							with open(self.image_processor_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										json.dump(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizer.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta   )]
							lowerCamelCase_					=  [Image.fromarray(np.moveaxis(UpperCAmelCase							,						0							,						-1   )   ) for x in image_inputs]
							return image_inputs




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()
							lowerCamelCase_					=  self.get_rust_tokenizer()
							lowerCamelCase_					=  self.get_image_processor()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_slow.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname							,						use_fast=UpperCAmelCase   )

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_fast.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AlignProcessor(tokenizer=self.get_tokenizer()							,						image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

							lowerCamelCase_					=  self.get_tokenizer(bos_token='''(BOS)'''							,						eos_token='''(EOS)'''   )
							lowerCamelCase_					=  self.get_image_processor(do_normalize=UpperCAmelCase							,						padding_value=1.0   )

							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  image_processor(UpperCAmelCase							,						return_tensors='''np'''   )
							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''

							lowerCamelCase_					=  processor(text=UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding='''max_length'''							,						max_length=64   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase_					=  processor.batch_decode(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.batch_decode(UpperCAmelCase   )

							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

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



