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 					A							(    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			str              =      KandinskyVaaControlnetPipeline
   __UpperCAmelCase			:			Tuple              =      ['image_embeds', 'negative_image_embeds', 'hint']
   __UpperCAmelCase			:			int              =      ['image_embeds', 'negative_image_embeds', 'hint']
   __UpperCAmelCase			:			Optional[int]              =      [
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   __UpperCAmelCase			:			List[str]              =      False


   @property
   def       lowercase_				(self					:						int		)						->    List[Any]:
         """simple docstring"""



         return 3_2


   @property
   def       lowercase_				(self					:						str		)						->    Tuple:
         """simple docstring"""



         return 3_2


   @property
   def       lowercase_				(self					:						str		)						->    Any:
         """simple docstring"""



         return self.time_input_dim


   @property
   def       lowercase_				(self					:						Optional[Any]		)						->    List[str]:
         """simple docstring"""



         return self.time_input_dim * 4


   @property
   def       lowercase_				(self					:						Optional[Any]		)						->    List[str]:
         """simple docstring"""



         return 1_0_0


   @property
   def       lowercase_				(self					:						Dict		)						->    Optional[Any]:
         """simple docstring"""



         torch.manual_seed(0		)

         UpperCAmelCase__								=  {
             "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,
         }

         UpperCAmelCase__								=  UNetaDConditionModel(**__UpperCAmelCase		)
         return model


   @property
   def       lowercase_				(self					:						List[Any]		)						->    Tuple:
         """simple docstring"""



         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       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



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


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.dummy_unet
         UpperCAmelCase__								=  self.dummy_movq

         UpperCAmelCase__								=  DDIMScheduler(
             num_train_timesteps=1_0_0_0	,  beta_schedule="linear"	,  beta_start=0.00085	,  beta_end=0.012	,  clip_sample=__UpperCAmelCase	,  set_alpha_to_one=__UpperCAmelCase	,  steps_offset=1	,  prediction_type="epsilon"	,  thresholding=__UpperCAmelCase	,  )

         UpperCAmelCase__								=  {
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						Optional[Any]=0		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1		)		).to(
             __UpperCAmelCase		)

         # create hint
         UpperCAmelCase__								=  floats_tensor((1, 3, 6_4, 6_4)	,  rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)

         if str(__UpperCAmelCase		).startswith("mps"		):
               UpperCAmelCase__								=  torch.manual_seed(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "hint": hint,
             "generator": generator,
             "height": 6_4,
             "width": 6_4,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs





   def       lowercase_				(self					:						Optional[Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  "cpu"

         UpperCAmelCase__								=  self.get_dummy_components()

         UpperCAmelCase__								=  self.pipeline_class(**__UpperCAmelCase		)
         UpperCAmelCase__								=  pipe.to(__UpperCAmelCase		)

         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(**self.get_dummy_inputs(__UpperCAmelCase		)		)
         UpperCAmelCase__								=  output.images

         UpperCAmelCase__								=  pipe(
             **self.get_dummy_inputs(__UpperCAmelCase		)	,  return_dict=__UpperCAmelCase	,  )[0]

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

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

         UpperCAmelCase__								=  np.array(
             [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]		)

         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       lowercase_				(self					:						int		)						->    Optional[Any]:
         """simple docstring"""



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





   def       lowercase_				(self					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"		)

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

         UpperCAmelCase__								=  KandinskyVaaPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"	,  torch_dtype=torch.floataa		)
         pipe_prior.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  KandinskyVaaControlnetPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"	,  torch_dtype=torch.floataa		)
         UpperCAmelCase__								=  pipeline.to(__UpperCAmelCase		)
         pipeline.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  "A robot, 4k photo"

         UpperCAmelCase__								=  torch.Generator(device="cuda"		).manual_seed(0		)
         UpperCAmelCase__		,				UpperCAmelCase__								=  pipe_prior(
             __UpperCAmelCase	,  generator=__UpperCAmelCase	,  num_inference_steps=5	,  negative_prompt=""	,  ).to_tuple()

         UpperCAmelCase__								=  torch.Generator(device="cuda"		).manual_seed(0		)
         UpperCAmelCase__								=  pipeline(
             image_embeds=__UpperCAmelCase	,  negative_image_embeds=__UpperCAmelCase	,  hint=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  num_inference_steps=1_0_0	,  output_type="np"	,  )

         UpperCAmelCase__								=  output.images[0]

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

         assert_mean_pixel_difference(__UpperCAmelCase	,  __UpperCAmelCase		)




# Lint as: python3
import itertools
import os
import re


UpperCamelCase__    							= re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__    							= re.compile(R'([a-z\d])([A-Z])')

UpperCamelCase__    							= re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__    							= re.compile(R'(_{2,})')

UpperCamelCase__    							= R'^\w+(\.\w+)*$'

UpperCamelCase__    							= R'<>:/\|?*'




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  _uppercase_uppercase_re.sub(r"\1_\2",  __A							)
      UpperCAmelCase__								=  _lowercase_uppercase_re.sub(r"\1_\2",  __A							)
      return name.lower()




def        lowerCAmelCase_			(					__A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  _single_underscore_re.split(__A							)
      UpperCAmelCase__								=  [_multiple_underscores_re.split(__A							) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A							) if n != ""							)




def        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      return camelcase_to_snakecase(__A							)




def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[int]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      if not re.match(_split_re,  __A							):
            raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'."""							)
      return f"""{filename_prefix_for_name(__A							)}-{split}"""




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      if filetype_suffix:
            prefix += f""".{filetype_suffix}"""
      UpperCAmelCase__								=  os.path.join(__A,  __A							)
      return f"""{filepath}*"""







def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      UpperCAmelCase__								=  os.path.join(__A,  __A							)

      if shard_lengths:
            UpperCAmelCase__								=  len(__A							)
            UpperCAmelCase__								=  [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__A							)]
            if filetype_suffix:
                  UpperCAmelCase__								=  [filename + f""".{filetype_suffix}""" for filename in filenames]
            return filenames
      else:
            UpperCAmelCase__								=  prefix
            if filetype_suffix:
                  filename += f""".{filetype_suffix}"""
            return [filename]



