



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	(     lowerCamelCase_				,unittest.TestCase   ):
       UpperCamelCase__  :   Dict						 =						KandinskyVaaControlnetImgaImgPipeline
       UpperCamelCase__  :   Any						 =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase__  :   Dict						 =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase__  :   Union[str, Any]						 =						[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase__  :   str						 =						False



       @property
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return self.time_input_dim



       @property
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           return self.time_input_dim * 4



       @property
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           return 100



       @property
       def 		_lowerCamelCase      (		self							:					Dict):


           '''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(**__snake_case)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''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							:					int):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    self.dummy_unet
           __a							=    self.dummy_movq

           __a							=    {
               '''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,
           }

           __a							=    DDIMScheduler(**__snake_case)

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

           return components



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str=0):


           '''simple docstring'''

           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(__snake_case)).to(__snake_case)
           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(seed + 1)).to(
               __snake_case)
           # create init_image
           __a							=    floats_tensor((1, 3, 64, 64)					,		rng=random.Random(__snake_case)).to(__snake_case)
           __a							=    image.cpu().permute(0					,		2					,		3					,		1)[0]
           __a							=    Image.fromarray(np.uinta(__snake_case)).convert('''RGB''').resize((256, 256))
           # create hint
           __a							=    floats_tensor((1, 3, 64, 64)					,		rng=random.Random(__snake_case)).to(__snake_case)

           if str(__snake_case).startswith('''mps'''):
               __a							=    torch.manual_seed(__snake_case)
           else:
               __a							=    torch.Generator(device=__snake_case).manual_seed(__snake_case)
           __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							:					Tuple):


           '''simple docstring'''

           __a							=    '''cpu'''

           __a							=    self.get_dummy_components()

           __a							=    self.pipeline_class(**__snake_case)
           __a							=    pipe.to(__snake_case)

           pipe.set_progress_bar_config(disable=__snake_case)

           __a							=    pipe(**self.get_dummy_inputs(__snake_case))
           __a							=    output.images

           __a							=    pipe(
               **self.get_dummy_inputs(__snake_case)					,		return_dict=__snake_case					,		)[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(
               [0.54_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    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					int):


           '''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((512, 512))

           __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(__snake_case)).float() / 255.0
           __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(__snake_case)

           __a							=    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-controlnet-depth'''					,		torch_dtype=torch.floataa)
           __a							=    pipeline.to(__snake_case)

           pipeline.set_progress_bar_config(disable=__snake_case)

           __a							=    torch.Generator(device='''cpu''').manual_seed(0)

           __a	,       __a							=    pipe_prior(
               __snake_case					,		image=__snake_case					,		strength=0.85					,		generator=__snake_case					,		negative_prompt=''''''					,		).to_tuple()

           __a							=    pipeline(
               image=__snake_case					,		image_embeds=__snake_case					,		negative_image_embeds=__snake_case					,		hint=__snake_case					,		generator=__snake_case					,		num_inference_steps=100					,		height=512					,		width=512					,		strength=0.5					,		output_type='''np'''					,		)

           __a							=    output.images[0]

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

           assert_mean_pixel_difference(__snake_case					,		__snake_case)










import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case						:int						=					logging.get_logger(__name__)

class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __a							=    question_encoder
           __a							=    generator
           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if os.path.isfile(__SCREAMING_SNAKE_CASE):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(__SCREAMING_SNAKE_CASE					,		exist_ok=__SCREAMING_SNAKE_CASE)
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''question_encoder_tokenizer''')
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''generator_tokenizer''')
           self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE)
           self.generator.save_pretrained(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase      (		cls							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           from ..auto.tokenization_auto import AutoTokenizer

           __a							=    kwargs.pop('''config'''					,		__SCREAMING_SNAKE_CASE)

           if config is None:
               __a							=    RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.question_encoder					,		subfolder='''question_encoder_tokenizer''')
           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.generator					,		subfolder='''generator_tokenizer''')
           return cls(question_encoder=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE)



       def __call__(		self							:					Dict					,		*__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.current_tokenizer(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		*__SCREAMING_SNAKE_CASE							:					Dict					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return self.generator.batch_decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           return self.generator.decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.generator





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[str]] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					str = "longest"					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           warnings.warn(
               '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
               '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
               '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
               '''details'''					,		__SCREAMING_SNAKE_CASE					,		)
           if max_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               __SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           if tgt_texts is None:
               return model_inputs
           # Process tgt_texts
           if max_target_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               text_target=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    labels['''input_ids''']
           return model_inputs





