'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class       UpperCAmelCase__  (       _UpperCAmelCase					,     unittest.TestCase			):
   A							:       List[str]     =    KandinskyVaaControlnetImgaImgPipeline
   A							:       List[str]     =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   A							:       Optional[Any]     =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   A							:       Tuple     =    [
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   A							:       Dict     =    False
   @property
   def 		_lowerCamelCase (self				)    ->     int:
        return 32
   @property
   def 		_lowerCamelCase (self				)    ->     int:
        return 32
   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        return self.time_input_dim
   @property
   def 		_lowerCamelCase (self				)    ->     Any:
        return self.time_input_dim * 4
   @property
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        return 100
   @property
   def 		_lowerCamelCase (self				)    ->     int:
        torch.manual_seed(0				)

        lowercase_					:       Optional[int]     				=					{
            'in_channels': 8,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'image_hint',
            'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
            'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
            'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
            'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
            'layers_per_block': 1,
            'encoder_hid_dim': self.text_embedder_hidden_size,
            'encoder_hid_dim_type': 'image_proj',
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': None,
        }

        lowercase_					:       Tuple     				=					UNetaDConditionModel(**A_				)
        return model
   @property
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }
   @property
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        torch.manual_seed(0				)
        lowercase_					:       Optional[Any]     				=					VQModel(**self.dummy_movq_kwargs				)
        return model
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[Any]     				=					self.dummy_unet
        lowercase_					:       Tuple     				=					self.dummy_movq

        lowercase_					:       List[Any]     				=					{
            'num_train_timesteps': 1_000,
            'beta_schedule': 'linear',
            'beta_start': 0.0_00_85,
            'beta_end': 0.0_12,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        lowercase_					:       Union[str, Any]     				=					DDIMScheduler(**A_				)

        lowercase_					:       Dict     				=					{
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components
   def 		_lowerCamelCase (self       ,				_a       ,				_a=0				)    ->     str:
        lowercase_					:       List[str]     				=					floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(A_				)				).to(A_				)
        lowercase_					:       Tuple     				=					floats_tensor((1, self.text_embedder_hidden_size)       ,				rng=random.Random(seed + 1				)				).to(
            A_				)
        # create init_image
        lowercase_					:       str     				=					floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(A_				)				).to(A_				)
        lowercase_					:       int     				=					image.cpu().permute(0       ,				2       ,				3       ,				1				)[0]
        lowercase_					:       Tuple     				=					Image.fromarray(np.uinta(A_				)				).convert('RGB'				).resize((256, 256)				)
        # create hint
        lowercase_					:       int     				=					floats_tensor((1, 3, 64, 64)       ,				rng=random.Random(A_				)				).to(A_				)

        if str(A_				).startswith('mps'				):
             lowercase_					:       Dict     				=					torch.manual_seed(A_				)
        else:
             lowercase_					:       List[str]     				=					torch.Generator(device=A_				).manual_seed(A_				)
        lowercase_					:       Dict     				=					{
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'hint': hint,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 10,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs





   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       List[str]     				=					'cpu'

        lowercase_					:       Union[str, Any]     				=					self.get_dummy_components()

        lowercase_					:       Union[str, Any]     				=					self.pipeline_class(**A_				)
        lowercase_					:       List[Any]     				=					pipe.to(A_				)

        pipe.set_progress_bar_config(disable=A_				)

        lowercase_					:       Tuple     				=					pipe(**self.get_dummy_inputs(A_				)				)
        lowercase_					:       Optional[Any]     				=					output.images

        lowercase_					:       Tuple     				=					pipe(
            **self.get_dummy_inputs(A_				)       ,				return_dict=A_       ,				)[0]

        lowercase_					:       int     				=					image[0, -3:, -3:, -1]
        lowercase_					:       int     				=					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        lowercase_					:       Tuple     				=					np.array(
            [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]				)
        assert (
            np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



@slow
@require_torch_gpu
class       UpperCAmelCase__  (       unittest.TestCase			):
   def 		_lowerCamelCase (self				)    ->     int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       str     				=					load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'				)

        lowercase_					:       int     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
        lowercase_					:       List[Any]     				=					init_image.resize((512, 512)				)

        lowercase_					:       Union[str, Any]     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/hint_image_cat.png'				)
        lowercase_					:       Dict     				=					torch.from_numpy(np.array(A_				)				).float() / 255.0
        lowercase_					:       Optional[Any]     				=					hint.permute(2       ,				0       ,				1				).unsqueeze(0				)

        lowercase_					:       List[str]     				=					'A robot, 4k photo'

        lowercase_					:       Dict     				=					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior'       ,				torch_dtype=torch.floataa				)
        pipe_prior.to(A_				)

        lowercase_					:       Any     				=					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-controlnet-depth'       ,				torch_dtype=torch.floataa				)
        lowercase_					:       List[Any]     				=					pipeline.to(A_				)

        pipeline.set_progress_bar_config(disable=A_				)

        lowercase_					:       Optional[int]     				=					torch.Generator(device='cpu'				).manual_seed(0				)

        lowercase_				,lowercase_					:       int     				=					pipe_prior(
            A_       ,				image=A_       ,				strength=0.85       ,				generator=A_       ,				negative_prompt=''       ,				).to_tuple()

        lowercase_					:       Dict     				=					pipeline(
            image=A_       ,				image_embeds=A_       ,				negative_image_embeds=A_       ,				hint=A_       ,				generator=A_       ,				num_inference_steps=100       ,				height=512       ,				width=512       ,				strength=0.5       ,				output_type='np'       ,				)

        lowercase_					:       List[str]     				=					output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(A_       ,				A_				)





'''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       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					tempfile.mkdtemp()

        lowercase_					:       int     				=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_					:       Union[str, 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]				)				)

        lowercase_					:       Union[str, Any]     				=					{
            '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],
        }
        lowercase_					:       List[str]     				=					os.path.join(self.tmpdirname       ,				_a				)
        with open(self.image_processor_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             json.dump(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     int:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        shutil.rmtree(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					[np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta				)]
        lowercase_					:       Optional[Any]     				=					[Image.fromarray(np.moveaxis(_a       ,				0       ,				-1				)				) for x in image_inputs]
        return image_inputs
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					self.get_tokenizer()
        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       int     				=					self.get_image_processor()

        lowercase_					:       int     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_slow.save_pretrained(self.tmpdirname				)
        lowercase_					:       List[str]     				=					AlignProcessor.from_pretrained(self.tmpdirname       ,				use_fast=_a				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_fast.save_pretrained(self.tmpdirname				)
        lowercase_					:       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       ,				_a				)
        self.assertIsInstance(processor_fast.tokenizer       ,				_a				)

        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       ,				_a				)
        self.assertIsInstance(processor_fast.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       str     				=					AlignProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        lowercase_					:       Tuple     				=					self.get_tokenizer(bos_token='(BOS)'       ,				eos_token='(EOS)'				)
        lowercase_					:       str     				=					self.get_image_processor(do_normalize=_a       ,				padding_value=1.0				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor.from_pretrained(
            self.tmpdirname       ,				bos_token='(BOS)'       ,				eos_token='(EOS)'       ,				do_normalize=_a       ,				padding_value=1.0				)

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

        self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.get_image_processor()
        lowercase_					:       Union[str, Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[int]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

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

        lowercase_					:       Union[str, Any]     				=					image_processor(_a       ,				return_tensors='np'				)
        lowercase_					:       Union[str, Any]     				=					processor(images=_a       ,				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 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[Any]     				=					self.get_image_processor()
        lowercase_					:       int     				=					self.get_tokenizer()

        lowercase_					:       Any     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Tuple     				=					'lower newer'

        lowercase_					:       Optional[Any]     				=					processor(text=_a				)

        lowercase_					:       List[Any]     				=					tokenizer(_a       ,				padding='max_length'       ,				max_length=64				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.get_image_processor()
        lowercase_					:       List[Any]     				=					self.get_tokenizer()

        lowercase_					:       List[str]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[Any]     				=					'lower newer'
        lowercase_					:       List[Any]     				=					self.prepare_image_inputs()

        lowercase_					:       List[str]     				=					processor(text=_a       ,				images=_a				)

        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(_a				):
             processor()
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.get_image_processor()
        lowercase_					:       Optional[Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_					:       str     				=					processor.batch_decode(_a				)
        lowercase_					:       Dict     				=					tokenizer.batch_decode(_a				)

        self.assertListEqual(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.get_image_processor()
        lowercase_					:       str     				=					self.get_tokenizer()

        lowercase_					:       str     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[int]     				=					'lower newer'
        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					processor(text=_a       ,				images=_a				)

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




