





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 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					List[Any]         =   KandinskyVaaControlnetPipeline
       _UpperCamelCase				:					Union[str, Any]         =   ['''image_embeds''', '''negative_image_embeds''', '''hint''']
       _UpperCamelCase				:					Union[str, Any]         =   ['''image_embeds''', '''negative_image_embeds''', '''hint''']
       _UpperCamelCase				:					Dict         =   [
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       _UpperCamelCase				:					Any         =   False

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




              """simple docstring"""


              return 32

       @property
       def lowercase (		self: int		)       -> Union[str, Any]:




              """simple docstring"""


              return 32

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




              """simple docstring"""


              return self.time_input_dim

       @property
       def lowercase (		self: Union[str, Any]		)       -> Optional[int]:




              """simple docstring"""


              return self.time_input_dim * 4

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




              """simple docstring"""


              return 100

       @property
       def lowercase (		self: Dict		)       -> str:




              """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(**_SCREAMING_SNAKE_CASE		)
              return model

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




              """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 lowercase (		self: str		)       -> List[str]:




              """simple docstring"""


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

       def lowercase (		self: List[str]		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  self.dummy_unet
              UpperCamelCase_          =  self.dummy_movq

              UpperCamelCase_          =  DDIMScheduler(
                  num_train_timesteps=1000					,   beta_schedule="linear"					,   beta_start=0.0_00_85					,   beta_end=0.0_12					,   clip_sample=_SCREAMING_SNAKE_CASE					,   set_alpha_to_one=_SCREAMING_SNAKE_CASE					,   steps_offset=1					,   prediction_type="epsilon"					,   thresholding=_SCREAMING_SNAKE_CASE					,   )

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

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Optional[int]=0		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(seed + 1		)		).to(
                  _SCREAMING_SNAKE_CASE		)

              # create hint
              UpperCamelCase_          =  floats_tensor((1, 3, 64, 64)					,   rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)

              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  {
                  "image_embeds": image_embeds,
                  "negative_image_embeds": negative_image_embeds,
                  "hint": hint,
                  "generator": generator,
                  "height": 64,
                  "width": 64,
                  "guidance_scale": 4.0,
                  "num_inference_steps": 2,
                  "output_type": "np",
              }
              return inputs



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




              """simple docstring"""


              UpperCamelCase_          =  "cpu"

              UpperCamelCase_          =  self.get_dummy_components()

              UpperCamelCase_          =  self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  pipe.to(_SCREAMING_SNAKE_CASE		)

              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  output.images

              UpperCamelCase_          =  pipe(
                  **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)					,   return_dict=_SCREAMING_SNAKE_CASE					,   )[0]

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

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

              UpperCamelCase_          =  np.array(
                  [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]		)

              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 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Tuple		)       -> str:




              """simple docstring"""


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



       def lowercase (		self: Optional[int]		)       -> Tuple:




              """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(_SCREAMING_SNAKE_CASE		)		).float() / 2_55.0
              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(_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_          =  "A robot, 4k photo"

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

              UpperCamelCase_          =  torch.Generator(device="cuda"		).manual_seed(0		)
              UpperCamelCase_          =  pipeline(
                  image_embeds=_SCREAMING_SNAKE_CASE					,   negative_image_embeds=_SCREAMING_SNAKE_CASE					,   hint=_SCREAMING_SNAKE_CASE					,   generator=_SCREAMING_SNAKE_CASE					,   num_inference_steps=100					,   output_type="np"					,   )

              UpperCamelCase_          =  output.images[0]

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

              assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)








import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'T5Config'




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		jnp.ndarray:
       UpperCamelCase_          =  jnp.zeros_like(UpperCamelCase_  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 0].set(UpperCamelCase_  )

       UpperCamelCase_          =  jnp.where(shifted_input_ids == -100					,		UpperCamelCase_					,		UpperCamelCase_  )
       return shifted_input_ids



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Union[str, Any]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Optional[int]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   '''mt5'''
       _UpperCamelCase				:					List[Any]         =   MTaConfig

