



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Optional[int] =  KandinskyVaaControlnetPipeline
 a__					: Any =  ["image_embeds", "negative_image_embeds", "hint"]
 a__					: str =  ["image_embeds", "negative_image_embeds", "hint"]
 a__					: List[Any] =  [
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 a__					: Union[str, Any] =  False

 @property
 def     snake_case_      (    self					:					str      )   ->	List[Any]:
    return 32

 @property
 def     snake_case_      (    self					:					Optional[int]      )   ->	Optional[int]:
    return 32

 @property
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Tuple:
    return self.time_input_dim

 @property
 def     snake_case_      (    self					:					str      )   ->	Any:
    return self.time_input_dim * 4

 @property
 def     snake_case_      (    self					:					Optional[int]      )   ->	Tuple:
    return 1_00

 @property
 def     snake_case_      (    self					:					Tuple      )   ->	Union[str, Any]:
    torch.manual_seed(0      )

    _A      =       {
        '''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,
    }

    _A      =       UNetaDConditionModel(**__lowerCAmelCase      )
    return model

 @property
 def     snake_case_      (    self					:					Tuple      )   ->	List[Any]:
    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     snake_case_      (    self					:					int      )   ->	List[str]:
    torch.manual_seed(0      )
    _A      =       VQModel(**self.dummy_movq_kwargs      )
    return model

 def     snake_case_      (    self					:					List[str]      )   ->	Dict:
    _A      =       self.dummy_unet
    _A      =       self.dummy_movq

    _A      =       DDIMScheduler(
        num_train_timesteps=10_00			,						beta_schedule='''linear'''			,						beta_start=0.0_0085			,						beta_end=0.012			,						clip_sample=__lowerCAmelCase			,						set_alpha_to_one=__lowerCAmelCase			,						steps_offset=1			,						prediction_type='''epsilon'''			,						thresholding=__lowerCAmelCase			,						)

    _A      =       {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''movq''': movq,
    }
    return components

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Tuple=0      )   ->	Optional[Any]:
    _A      =       floats_tensor((1, self.text_embedder_hidden_size)			,						rng=random.Random(__lowerCAmelCase      )      ).to(__lowerCAmelCase      )
    _A      =       floats_tensor((1, self.text_embedder_hidden_size)			,						rng=random.Random(seed + 1      )      ).to(
        __lowerCAmelCase      )

    # create hint
    _A      =       floats_tensor((1, 3, 64, 64)			,						rng=random.Random(__lowerCAmelCase      )      ).to(__lowerCAmelCase      )

    if str(__lowerCAmelCase      ).startswith('''mps'''      ):
       _A      =       torch.manual_seed(__lowerCAmelCase      )
    else:
       _A      =       torch.Generator(device=__lowerCAmelCase      ).manual_seed(__lowerCAmelCase      )
    _A      =       {
        '''image_embeds''': image_embeds,
        '''negative_image_embeds''': negative_image_embeds,
        '''hint''': hint,
        '''generator''': generator,
        '''height''': 64,
        '''width''': 64,
        '''guidance_scale''': 4.0,
        '''num_inference_steps''': 2,
        '''output_type''': '''np''',
    }
    return inputs

 def     snake_case_      (    self					:					Any      )   ->	Union[str, Any]:
    _A      =       '''cpu'''

    _A      =       self.get_dummy_components()

    _A      =       self.pipeline_class(**__lowerCAmelCase      )
    _A      =       pipe.to(__lowerCAmelCase      )

    pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       pipe(**self.get_dummy_inputs(__lowerCAmelCase      )      )
    _A      =       output.images

    _A      =       pipe(
        **self.get_dummy_inputs(__lowerCAmelCase      )			,						return_dict=__lowerCAmelCase			,						)[0]

    _A      =       image[0, -3:, -3:, -1]
    _A      =       image_from_tuple[0, -3:, -3:, -1]

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

    _A      =       np.array(
        [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

    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 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					Any      )   ->	List[Any]:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 def     snake_case_      (    self					:					Tuple      )   ->	List[Any]:
    _A      =       load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''      )

    _A      =       load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/kandinskyv22/hint_image_cat.png'''      )
    _A      =       torch.from_numpy(np.array(__lowerCAmelCase      )      ).float() / 255.0
    _A      =       hint.permute(2			,						0			,						1      ).unsqueeze(0      )

    _A      =       KandinskyVaaPriorPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-2-prior'''			,						torch_dtype=torch.floataa      )
    pipe_prior.to(__lowerCAmelCase      )

    _A      =       KandinskyVaaControlnetPipeline.from_pretrained(
        '''kandinsky-community/kandinsky-2-2-controlnet-depth'''			,						torch_dtype=torch.floataa      )
    _A      =       pipeline.to(__lowerCAmelCase      )
    pipeline.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       '''A robot, 4k photo'''

    _A      =       torch.Generator(device='''cuda'''      ).manual_seed(0      )
    _A  ,    _A      =       pipe_prior(
        __lowerCAmelCase			,						generator=__lowerCAmelCase			,						num_inference_steps=5			,						negative_prompt=''''''			,						).to_tuple()

    _A      =       torch.Generator(device='''cuda'''      ).manual_seed(0      )
    _A      =       pipeline(
        image_embeds=__lowerCAmelCase			,						negative_image_embeds=__lowerCAmelCase			,						hint=__lowerCAmelCase			,						generator=__lowerCAmelCase			,						num_inference_steps=1_00			,						output_type='''np'''			,						)

    _A      =       output.images[0]

    assert image.shape == (5_12, 5_12, 3)

    assert_mean_pixel_difference(__lowerCAmelCase			,						__lowerCAmelCase      )








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):



 """simple docstring"""

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    _A      =       tempfile.mkdtemp()

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

    _A      =       {
        '''do_resize''': True,
        '''size''': 20,
        '''do_center_crop''': True,
        '''crop_size''': 18,
        '''do_normalize''': True,
        '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
        '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    _A      =       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					:					Dict			,						**__lowerCAmelCase					:					int      )   ->	Optional[int]:
    return BertTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	Tuple:
    return BertTokenizerFast.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple			,						**__lowerCAmelCase					:					str      )   ->	Union[str, Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					int      )   ->	Optional[Any]:
    _A      =       [np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta      )]
    _A      =       [Image.fromarray(np.moveaxis(__lowerCAmelCase			,						0			,						-1      )      ) for x in image_inputs]
    return image_inputs

 def     snake_case_      (    self					:					Dict      )   ->	List[str]:
    _A      =       self.get_tokenizer()
    _A      =       self.get_rust_tokenizer()
    _A      =       self.get_image_processor()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_slow.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.from_pretrained(self.tmpdirname			,						use_fast=__lowerCAmelCase      )

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_fast.save_pretrained(self.tmpdirname      )
    _A      =       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			,						__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					:					List[Any]      )   ->	List[str]:
    _A      =       AlignProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

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

    _A      =       AlignProcessor.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			,						__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					:					str      )   ->	List[Any]:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       self.prepare_image_inputs()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''np'''      )
    _A      =       processor(images=__lowerCAmelCase			,						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					:					Union[str, Any]      )   ->	Dict:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''

    _A      =       processor(text=__lowerCAmelCase      )

    _A      =       tokenizer(__lowerCAmelCase			,						padding='''max_length'''			,						max_length=64      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Any:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

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

    _A      =       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]      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

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

    _A      =       processor.batch_decode(__lowerCAmelCase      )
    _A      =       tokenizer.batch_decode(__lowerCAmelCase      )

    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

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

    _A      =       processor(text=__lowerCAmelCase			,						images=__lowerCAmelCase      )

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



