


import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

a			:			Optional[int]												=					'''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
	pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
	pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
	if pkg in deps:
		if pkg == "tokenizers":
			# must be loaded here, or else tqdm check may fail
			from .utils import is_tokenizers_available

			if not is_tokenizers_available():
				continue  # not required, check version only if installed

		require_version_core(deps[pkg])
	else:
		raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			require_version(deps[pkg]							,      _snake_case	)




"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Any           =     KandinskyImgaImgPipeline
      UpperCamelCase							:   int           =     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
      UpperCamelCase							:   Optional[Any]           =     [
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
      ]
      UpperCamelCase							:   Union[str, Any]           =     [
          """generator""",
          """height""",
          """width""",
          """strength""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      UpperCamelCase							:   List[str]           =     False



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return 32



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



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



      @property
      def  __snake_case							(   self      ):
          return 100



      @property
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
          return tokenizer



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[int]     							=					MCLIPConfig(
              numDims=self.cross_attention_dim     ,	transformerDimensions=self.text_embedder_hidden_size     ,	hidden_size=self.text_embedder_hidden_size     ,	intermediate_size=37     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	vocab_size=1_005     ,	)

          UpperCAmelCase__	:      Tuple     							=					MultilingualCLIP(UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					text_encoder.eval()

          return text_encoder



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

          UpperCAmelCase__	:      Optional[Any]     							=					{
              'in_channels': 4,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              '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': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          UpperCAmelCase__	:      Optional[Any]     							=					UNetaDConditionModel(**UpperCamelCase_      )
          return model



      @property
      def  __snake_case							(   self      ):
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["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",
              ],
              "vq_embed_dim": 4,
          }



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					VQModel(**self.dummy_movq_kwargs      )
          return model



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_text_encoder
          UpperCAmelCase__	:      int     							=					self.dummy_tokenizer
          UpperCAmelCase__	:      int     							=					self.dummy_unet
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_movq

          UpperCAmelCase__	:      Any     							=					{
              'num_train_timesteps': 1_000,
              'beta_schedule': 'linear',
              'beta_start': 0.00085,
              'beta_end': 0.012,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(**UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      int     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(seed + 1      )      ).to(UpperCamelCase_      )
          # create init_image
          UpperCAmelCase__	:      Optional[int]     							=					floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      Union[str, Any]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      ).resize((256, 256)      )

          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Dict     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[int]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					{
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 10,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'cpu'

          UpperCAmelCase__	:      Any     							=					self.get_dummy_components()

          UpperCAmelCase__	:      Any     							=					self.pipeline_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					pipe.to(UpperCamelCase_      )

          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      Optional[int]     							=					output.images

          UpperCAmelCase__	:      Dict     							=					pipe(
              **self.get_dummy_inputs(UpperCamelCase_      )     ,	return_dict=UpperCamelCase_     ,	)[0]

          UpperCAmelCase__	:      Dict     							=					image[0, -3:, -3:, -1]
          UpperCAmelCase__	:      Any     							=					image_from_tuple[0, -3:, -3:, -1]

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

          UpperCAmelCase__	:      Any     							=					np.array(
              [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
          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  __snake_case							(   self      ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy'      )

          UpperCAmelCase__	:      List[Any]     							=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
          UpperCAmelCase__	:      Any     							=					'A red cartoon frog, 4k'

          UpperCAmelCase__	:      Union[str, Any]     							=					KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'     ,	torch_dtype=torch.floataa      )
          pipe_prior.to(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipeline.to(UpperCamelCase_      )

          pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					torch.Generator(device='cpu'      ).manual_seed(0      )
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					pipe_prior(
              UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

          UpperCAmelCase__	:      Optional[Any]     							=					pipeline(
              UpperCamelCase_     ,	image=UpperCamelCase_     ,	image_embeds=UpperCamelCase_     ,	negative_image_embeds=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=100     ,	height=768     ,	width=768     ,	strength=0.2     ,	output_type='np'     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(UpperCamelCase_     ,	UpperCamelCase_      )

