






"""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	):
    UpperCAmelCase_ :     Dict	     =KandinskyVaaControlnetImgaImgPipeline
    UpperCAmelCase_ :     int	     =["image_embeds", "negative_image_embeds", "image", "hint"]
    UpperCAmelCase_ :     str	     =["image_embeds", "negative_image_embeds", "image", "hint"]
    UpperCAmelCase_ :     Dict	     =[
        "generator",
        "height",
        "width",
        "strength",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    UpperCAmelCase_ :     Union[str, Any]	     =False







    @property
    def 			UpperCamelCase_      (			self       ):
      return 3_2







    @property
    def 			UpperCamelCase_      (			self       ):
      return 3_2







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.time_input_dim







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.time_input_dim * 4







    @property
    def 			UpperCamelCase_      (			self       ):
      return 1_0_0







    @property
    def 			UpperCamelCase_      (			self       ):
      torch.manual_seed(0       )

      lowercase							    =       {
          '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							    =       UNetaDConditionModel(**_lowerCamelCase       )
      return model







    @property
    def 			UpperCamelCase_      (			self       ):
      return {
          "block_out_channels": [3_2, 3_2, 6_4, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }







    @property
    def 			UpperCamelCase_      (			self       ):
      torch.manual_seed(0       )
      lowercase							    =       VQModel(**self.dummy_movq_kwargs       )
      return model







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.dummy_unet
      lowercase							    =       self.dummy_movq

      lowercase							    =       {
          'num_train_timesteps': 1_0_0_0,
          '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,
      }

      lowercase							    =       DDIMScheduler(**_lowerCamelCase       )

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

      return components







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=0       ):
      lowercase							    =       floats_tensor((1, self.text_embedder_hidden_size)							,		rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
      lowercase							    =       floats_tensor((1, self.text_embedder_hidden_size)							,		rng=random.Random(seed + 1       )       ).to(
          _lowerCamelCase       )
      # create init_image
      lowercase							    =       floats_tensor((1, 3, 6_4, 6_4)							,		rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       )[0]
      lowercase							    =       Image.fromarray(np.uinta(_lowerCamelCase       )       ).convert('RGB'       ).resize((2_5_6, 2_5_6)       )
      # create hint
      lowercase							    =       floats_tensor((1, 3, 6_4, 6_4)							,		rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )

      if str(_lowerCamelCase       ).startswith('mps'       ):
        lowercase							    =       torch.manual_seed(_lowerCamelCase       )
      else:
        lowercase							    =       torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
      lowercase							    =       {
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'hint': hint,
          'generator': generator,
          'height': 6_4,
          'width': 6_4,
          'num_inference_steps': 1_0,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'cpu'

      lowercase							    =       self.get_dummy_components()

      lowercase							    =       self.pipeline_class(**_lowerCamelCase       )
      lowercase							    =       pipe.to(_lowerCamelCase       )

      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

      lowercase							    =       pipe(**self.get_dummy_inputs(_lowerCamelCase       )       )
      lowercase							    =       output.images

      lowercase							    =       pipe(
          **self.get_dummy_inputs(_lowerCamelCase       )							,		return_dict=_lowerCamelCase							,		)[0]

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

      assert image.shape == (1, 6_4, 6_4, 3)

      lowercase							    =       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 			UpperCamelCase_      (			self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







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

      lowercase							    =       load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'       )
      lowercase							    =       init_image.resize((5_1_2, 5_1_2)       )

      lowercase							    =       load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/hint_image_cat.png'       )
      lowercase							    =       torch.from_numpy(np.array(_lowerCamelCase       )       ).float() / 2_5_5.0
      lowercase							    =       hint.permute(2							,		0							,		1       ).unsqueeze(0       )

      lowercase							    =       'A robot, 4k photo'

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

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

      pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

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

      lowercase    ,     lowercase							    =       pipe_prior(
          _lowerCamelCase							,		image=_lowerCamelCase							,		strength=0.8_5							,		generator=_lowerCamelCase							,		negative_prompt=''							,		).to_tuple()

      lowercase							    =       pipeline(
          image=_lowerCamelCase							,		image_embeds=_lowerCamelCase							,		negative_image_embeds=_lowerCamelCase							,		hint=_lowerCamelCase							,		generator=_lowerCamelCase							,		num_inference_steps=1_0_0							,		height=5_1_2							,		width=5_1_2							,		strength=0.5							,		output_type='np'							,		)

      lowercase							    =       output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)

      assert_mean_pixel_difference(_lowerCamelCase							,		_lowerCamelCase       )










"""simple docstring"""



import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       parent
      lowercase							    =       config_class
      lowercase							    =       has_text_modality
      lowercase							    =       kwargs
      lowercase							    =       common_properties







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )
      lowercase							    =       (
          ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
        common_properties.extend(['vocab_size']       )

      # Test that config has the common properties as getters
      for prop in common_properties:
        self.parent.assertTrue(hasattr(_lowerCamelCase							,		_lowerCamelCase       )							,		msg=F'`{prop}` does not exist'       )

      # Test that config has the common properties as setter
      for idx, name in enumerate(_lowerCamelCase       ):
        try:
          setattr(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       )
          self.parent.assertEqual(
              getattr(_lowerCamelCase							,		_lowerCamelCase       )							,		_lowerCamelCase							,		msg=F'`{name} value {idx} expected, but was {getattr(_lowerCamelCase							,		_lowerCamelCase       )}'       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(_lowerCamelCase       ):
        try:
          lowercase							    =       self.config_class(**{name: idx}       )
          self.parent.assertEqual(
              getattr(_lowerCamelCase							,		_lowerCamelCase       )							,		_lowerCamelCase							,		msg=F'`{name} value {idx} expected, but was {getattr(_lowerCamelCase							,		_lowerCamelCase       )}'       )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )
      lowercase							    =       json.loads(config.to_json_string()       )
      for key, value in self.inputs_dict.items():
        self.parent.assertEqual(obj[key]							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase							    =       os.path.join(_lowerCamelCase							,		'config.json'       )
        config_first.to_json_file(_lowerCamelCase       )
        lowercase							    =       self.config_class.from_json_file(_lowerCamelCase       )

      self.parent.assertEqual(config_second.to_dict()							,		config_first.to_dict()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
        config_first.save_pretrained(_lowerCamelCase       )
        lowercase							    =       self.config_class.from_pretrained(_lowerCamelCase       )

      self.parent.assertEqual(config_second.to_dict()							,		config_first.to_dict()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict       )

      lowercase							    =       'test'
      with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase							    =       os.path.join(_lowerCamelCase							,		_lowerCamelCase       )
        config_first.save_pretrained(_lowerCamelCase       )
        lowercase							    =       self.config_class.from_pretrained(_lowerCamelCase							,		subfolder=_lowerCamelCase       )

      self.parent.assertEqual(config_second.to_dict()							,		config_first.to_dict()       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.config_class(**self.inputs_dict							,		num_labels=5       )
      self.parent.assertEqual(len(config.idalabel       )							,		5       )
      self.parent.assertEqual(len(config.labelaid       )							,		5       )

      lowercase							    =       3
      self.parent.assertEqual(len(config.idalabel       )							,		3       )
      self.parent.assertEqual(len(config.labelaid       )							,		3       )







    def 			UpperCamelCase_      (			self       ):
      if self.config_class.is_composition:
        return
      lowercase							    =       self.config_class()
      self.parent.assertIsNotNone(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       copy.deepcopy(_lowerCamelCase       )
      lowercase							    =       self.config_class(**_lowerCamelCase       )
      lowercase							    =       []
      for key, value in config_common_kwargs.items():
        if key == "torch_dtype":
          if not is_torch_available():
            continue
          else:
            import torch

            if config.torch_dtype != torch.floataa:
              wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)       )
        elif getattr(_lowerCamelCase							,		_lowerCamelCase       ) != value:
          wrong_values.append((key, getattr(_lowerCamelCase							,		_lowerCamelCase       ), value)       )

      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       '\n'.join([F'- {v[0]}: got {v[1]} instead of {v[2]}' for v in wrong_values]       )
        raise ValueError(F'The following keys were not properly set in the config:\n{errors}'       )







    def 			UpperCamelCase_      (			self       ):
      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()


