




'''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       A   (		_a					,unittest.TestCase   ):
							lowercase_          =    KandinskyVaaControlnetImgaImgPipeline
							lowercase_          =    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
							lowercase_          =    ['image_embeds', 'negative_image_embeds', 'image', 'hint']
							lowercase_          =    [
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowercase_          =    False







							@property
							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							str:


														"""simple docstring"""


														return 32







							@property
							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Tuple:


														"""simple docstring"""


														return 32







							@property
							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Optional[int]:


														"""simple docstring"""


														return self.time_input_dim







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def        __lowerCAmelCase      (  self	:  Tuple )      ->							int:


														"""simple docstring"""


														return 1_00







							@property
							def        __lowerCAmelCase      (  self	:  Dict )      ->							Optional[Any]:


														"""simple docstring"""


														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        __lowerCAmelCase      (  self	:  Dict )      ->							Optional[int]:


														"""simple docstring"""


														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	:  Tuple )      ->							Dict:


														"""simple docstring"""


														torch.manual_seed(0 )
														_a     					=    VQModel(**self.dummy_movq_kwargs )
														return model







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							str:


														"""simple docstring"""


														_a     					=    self.dummy_unet
														_a     					=    self.dummy_movq

														_a     					=    {
														    '''num_train_timesteps''': 10_00,
														    '''beta_schedule''': '''linear''',
														    '''beta_start''': 0.0_0_0_8_5,
														    '''beta_end''': 0.0_1_2,
														    '''clip_sample''': False,
														    '''set_alpha_to_one''': False,
														    '''steps_offset''': 0,
														    '''prediction_type''': '''epsilon''',
														    '''thresholding''': False,
														}

														_a     					=    DDIMScheduler(**lowerCAmelCase_ )

														_a     					=    {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}

														return components







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Optional[Any]=0 )      ->							str:


														"""simple docstring"""


														_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 init_image
														_a     					=    floats_tensor((1, 3, 64, 64)							,      rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
														_a     					=    image.cpu().permute(0							,      2							,      3							,      1 )[0]
														_a     					=    Image.fromarray(np.uinta(lowerCAmelCase_ ) ).convert('''RGB''' ).resize((2_56, 2_56) )
														# 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''': 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	:  Optional[int] )      ->							List[Any]:


														"""simple docstring"""


														_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(
														    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6] )
														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       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  Any )      ->							Optional[Any]:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Any:


														"""simple docstring"""


														_a     					=    load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )

														_a     					=    load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
														_a     					=    init_image.resize((5_12, 5_12) )

														_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() / 2_5_5.0
														_a     					=    hint.permute(2							,      0							,      1 ).unsqueeze(0 )

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

														_a     					=    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-prior'''							,      torch_dtype=torch.floataa )
														pipe_prior.to(lowerCAmelCase_ )

														_a     					=    KandinskyVaaControlnetImgaImgPipeline.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     					=    torch.Generator(device='''cpu''' ).manual_seed(0 )

														_a					,							_a     					=    pipe_prior(
														    lowerCAmelCase_							,      image=lowerCAmelCase_							,      strength=0.8_5							,      generator=lowerCAmelCase_							,      negative_prompt=''''''							,      ).to_tuple()

														_a     					=    pipeline(
														    image=lowerCAmelCase_							,      image_embeds=lowerCAmelCase_							,      negative_image_embeds=lowerCAmelCase_							,      hint=lowerCAmelCase_							,      generator=lowerCAmelCase_							,      num_inference_steps=1_00							,      height=5_12							,      width=5_12							,      strength=0.5							,      output_type='''np'''							,      )

														_a     					=    output.images[0]

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

														assert_mean_pixel_difference(lowerCAmelCase_							,      lowerCAmelCase_ )







'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


_snake_case				:				Any       							=		logging.get_logger(__name__)

_snake_case				:				Any       							=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case				:				List[str]       							=		{
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case				:				int       							=		{
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

_snake_case				:				int       							=		{
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}

class       A   (		_a   ):
							lowercase_          =    VOCAB_FILES_NAMES
							lowercase_          =    PRETRAINED_VOCAB_FILES_MAP
							lowercase_          =    PRETRAINED_INIT_CONFIGURATION
							lowercase_          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_          =    BertTokenizer







							def __init__(  self	:  str							,      lowerCAmelCase_	:  Union[str, Any]=None							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  List[Any]=True							,      lowerCAmelCase_	:  Any="[UNK]"							,      lowerCAmelCase_	:  Union[str, Any]="[SEP]"							,      lowerCAmelCase_	:  Tuple="[PAD]"							,      lowerCAmelCase_	:  Tuple="[CLS]"							,      lowerCAmelCase_	:  Optional[int]="[MASK]"							,      lowerCAmelCase_	:  Dict=True							,      lowerCAmelCase_	:  Union[str, Any]=None							,      **lowerCAmelCase_	:  Union[str, Any]							,      )      ->							Union[str, Any]:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_							,      tokenizer_file=lowerCAmelCase_							,      do_lower_case=lowerCAmelCase_							,      unk_token=lowerCAmelCase_							,      sep_token=lowerCAmelCase_							,      pad_token=lowerCAmelCase_							,      cls_token=lowerCAmelCase_							,      mask_token=lowerCAmelCase_							,      tokenize_chinese_chars=lowerCAmelCase_							,      strip_accents=lowerCAmelCase_							,      **lowerCAmelCase_							,      )

														_a     					=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
														if (
														    normalizer_state.get('''lowercase'''							,      lowerCAmelCase_ ) != do_lower_case
														    or normalizer_state.get('''strip_accents'''							,      lowerCAmelCase_ ) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''							,      lowerCAmelCase_ ) != tokenize_chinese_chars
														):
																					_a     					=    getattr(lowerCAmelCase_							,      normalizer_state.pop('''type''' ) )
																					_a     					=    do_lower_case
																					_a     					=    strip_accents
																					_a     					=    tokenize_chinese_chars
																					_a     					=    normalizer_class(**lowerCAmelCase_ )

														_a     					=    do_lower_case







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  int=None )      ->							List[str]:


														"""simple docstring"""


														_a     					=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														_a     					=    [self.sep_token_id]
														_a     					=    [self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep ) * [0]
														return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[str] = None )      ->							Tuple[str]:


														"""simple docstring"""


														_a     					=    self._tokenizer.model.save(lowerCAmelCase_							,      name=lowerCAmelCase_ )
														return tuple(lowerCAmelCase_ )

