





'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor




@require_tokenizers
@require_vision
class 					UpperCAmelCase	(     unittest.TestCase  ):




      def 							lowercase__       (     self     :					int    )							-> str:
            _lowerCAmelCase					=				tempfile.mkdtemp()

            # fmt: off
            _lowerCAmelCase					=				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
            # fmt: on
            _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': {'height': 18, 'width': 18},
                'do_normalize': True,
                'image_mean': [0.5, 0.5, 0.5],
                'image_std': [0.5, 0.5, 0.5],
            }
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     lowerCamelCase__    )
            with open(self.image_processor_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  json.dump(lowerCamelCase__     ,     lowerCamelCase__    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     **__snake_case     :					List[Any]    )							-> int:
            return BertTokenizer.from_pretrained(self.tmpdirname     ,     **lowerCamelCase__    )




      def 							lowercase__       (     self     :					Any     ,     **__snake_case     :					str    )							-> Optional[Any]:
            return ViTImageProcessor.from_pretrained(self.tmpdirname     ,     **lowerCamelCase__    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> Any:
            shutil.rmtree(self.tmpdirname    )




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            _lowerCAmelCase					=				[np.random.randint(2_55     ,     size=(3, 30, 4_00)     ,     dtype=np.uinta    )]

            _lowerCAmelCase					=				[Image.fromarray(np.moveaxis(lowerCamelCase__     ,     0     ,     -1    )    ) for x in image_inputs]

            return image_inputs




      def 							lowercase__       (     self     :					Any    )							-> Tuple:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				self.get_image_processor()

            _lowerCAmelCase					=				VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__     ,     image_processor=lowerCamelCase__    )

            processor.save_pretrained(self.tmpdirname    )
            _lowerCAmelCase					=				VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname    )

            self.assertEqual(processor.tokenizer.get_vocab()     ,     tokenizer.get_vocab()    )
            self.assertIsInstance(processor.tokenizer     ,     (BertTokenizer, BertTokenizerFast)    )

            self.assertEqual(processor.image_processor.to_json_string()     ,     image_processor.to_json_string()    )
            self.assertIsInstance(processor.image_processor     ,     lowerCamelCase__    )




      def 							lowercase__       (     self     :					List[str]    )							-> List[str]:
            _lowerCAmelCase					=				VisionTextDualEncoderProcessor(
                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=lowerCamelCase__     ,     padding_value=1.0    )

            _lowerCAmelCase					=				VisionTextDualEncoderProcessor.from_pretrained(
                self.tmpdirname     ,     bos_token="""(BOS)"""     ,     eos_token="""(EOS)"""     ,     do_normalize=lowerCamelCase__     ,     padding_value=1.0    )

            self.assertEqual(processor.tokenizer.get_vocab()     ,     tokenizer_add_kwargs.get_vocab()    )
            self.assertIsInstance(processor.tokenizer     ,     (BertTokenizer, BertTokenizerFast)    )

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




      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__     ,     image_processor=lowerCamelCase__    )

            _lowerCAmelCase					=				self.prepare_image_inputs()

            _lowerCAmelCase					=				image_processor(lowerCamelCase__     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				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 							lowercase__       (     self     :					Tuple    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__     ,     image_processor=lowerCamelCase__    )

            _lowerCAmelCase					=				'lower newer'

            _lowerCAmelCase					=				processor(text=lowerCamelCase__    )

            _lowerCAmelCase					=				tokenizer(lowerCamelCase__    )

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




      def 							lowercase__       (     self     :					List[str]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__     ,     image_processor=lowerCamelCase__    )

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

            _lowerCAmelCase					=				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 self.assertRaises(lowerCamelCase__    ):
                  processor()




      def 							lowercase__       (     self     :					int    )							-> str:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__     ,     image_processor=lowerCamelCase__    )

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

            _lowerCAmelCase					=				processor.batch_decode(lowerCamelCase__    )
            _lowerCAmelCase					=				tokenizer.batch_decode(lowerCamelCase__    )

            self.assertListEqual(lowerCamelCase__     ,     lowerCamelCase__    )




      def 							lowercase__       (     self     :					Optional[Any]    )							-> Dict:
            _lowerCAmelCase					=				self.get_image_processor()
            _lowerCAmelCase					=				self.get_tokenizer()

            _lowerCAmelCase					=				VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__     ,     image_processor=lowerCamelCase__    )

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

            _lowerCAmelCase					=				processor(text=lowerCamelCase__     ,     images=lowerCamelCase__    )

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











'''simple docstring'''
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


snake_case_					:      Tuple    			=    False






@skip_mps
class 				lowercase__      (				lowercase		,   lowercase		,   lowercase		,   unittest.TestCase  ):
	lowercase__    							=     StableDiffusionAttendAndExcitePipeline
	lowercase__    							=     False
	lowercase__    							=     TEXT_TO_IMAGE_PARAMS
	lowercase__    							=     TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}  )
	lowercase__    							=     TEXT_TO_IMAGE_IMAGE_PARAMS
	lowercase__    							=     TEXT_TO_IMAGE_IMAGE_PARAMS





	@classmethod
	def       UpperCamelCase_    (					cls   :							Any					):







							'''simple docstring'''
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	@classmethod
	def       UpperCamelCase_    (					cls   :							List[Any]					):







							'''simple docstring'''
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						Union[str, Any]												=  UNetaDConditionModel(
							    block_out_channels=(32, 64)					,layers_per_block=1					,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__					,)
							_UpperCamelCase   :						List[str]												=  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__					,)
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  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=128					,)
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  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=1000					,hidden_act='gelu'					,projection_dim=512					,)
							_UpperCamelCase   :						List[str]												=  CLIPTextModel(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

							_UpperCamelCase   :						str												=  {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							str=0					):







							'''simple docstring'''
							if str(lowerCamelCase__					).startswith('mps'					):
													_UpperCamelCase   :						Union[str, Any]												=  torch.manual_seed(lowerCamelCase__					)
							else:
													_UpperCamelCase   :						int												=  torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  {
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  'cpu'

							_UpperCamelCase   :						List[Any]												=  self.get_dummy_components()
							_UpperCamelCase   :						Union[str, Any]												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  self.get_dummy_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  pipe(**lowerCamelCase__					).images
							_UpperCamelCase   :						Union[str, Any]												=  image[0, -3:, -3:, -1]

							self.assertEqual(image.shape					,(1, 64, 64, 3)					)
							_UpperCamelCase   :						Union[str, Any]												=  np.array(
							    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]					)
							_UpperCamelCase   :						Tuple												=  np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(lowerCamelCase__					,1E-3					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4					)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							self._test_inference_batch_single_identical(batch_size=2					,expected_max_diff=7E-4					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4					)





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							super().test_save_load_local(expected_max_difference=5E-4					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							super().test_save_load_optional_components(expected_max_difference=4E-4					)






@require_torch_gpu
@slow
class 				lowercase__      (				unittest.TestCase  ):





	@classmethod
	def       UpperCamelCase_    (					cls   :							Dict					):







							'''simple docstring'''
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	@classmethod
	def       UpperCamelCase_    (					cls   :							Optional[Any]					):







							'''simple docstring'''
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.manual_seed(51					)

							_UpperCamelCase   :						Optional[Any]												=  StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'					,safety_checker=lowerCamelCase__					,torch_dtype=torch.floataa					)
							pipe.to('cuda'					)

							_UpperCamelCase   :						Any												=  'a painting of an elephant with glasses'
							_UpperCamelCase   :						str												=  [5, 7]

							_UpperCamelCase   :						int												=  pipe(
							    prompt=lowerCamelCase__					,token_indices=lowerCamelCase__					,guidance_scale=7.5					,generator=lowerCamelCase__					,num_inference_steps=5					,max_iter_to_alter=5					,output_type='numpy'					,).images[0]

							_UpperCamelCase   :						Tuple												=  load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'					)
							assert np.abs((expected_image - image).max()					) < 5E-1


