



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       UpperCAmelCase__(							__lowerCAmelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  List[str]       				=    KandinskyVaaControlnetImgaImgPipeline
   A    :  Tuple       				=    ["image_embeds", "negative_image_embeds", "image", "hint"]
   A    :  Dict       				=    ["image_embeds", "negative_image_embeds", "image", "hint"]
   A    :  Any       				=    [
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   A    :  Optional[int]       				=    False


   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         return 32


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




         """simple docstring"""

         return 32


   @property
   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         return self.time_input_dim


   @property
   def 			UpperCAmelCase		(						self  :			Dict)	->				Union[str, Any]:




         """simple docstring"""

         return self.time_input_dim * 4


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




         """simple docstring"""

         return 1_00


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         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(**_UpperCamelCase)
         return model


   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				List[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 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.dummy_unet
         lowercase__     =  self.dummy_movq

         lowercase__     =  {
             """num_train_timesteps""": 10_00,
             """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,
         }

         lowercase__     =  DDIMScheduler(**_UpperCamelCase)

         lowercase__     =  {
             """unet""": unet,
             """scheduler""": scheduler,
             """movq""": movq,
         }

         return components


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			Any=0)	->				str:




         """simple docstring"""

         lowercase__     =  floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)
         lowercase__     =  floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(seed + 1)).to(
             _UpperCamelCase)
         # create init_image
         lowercase__     =  floats_tensor((1, 3, 64, 64)			,				rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)
         lowercase__     =  image.cpu().permute(0			,				2			,				3			,				1)[0]
         lowercase__     =  Image.fromarray(np.uinta(_UpperCamelCase)).convert('RGB').resize((2_56, 2_56))
         # create hint
         lowercase__     =  floats_tensor((1, 3, 64, 64)			,				rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)

         if str(_UpperCamelCase).startswith('mps'):
               lowercase__     =  torch.manual_seed(_UpperCamelCase)
         else:
               lowercase__     =  torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)
         lowercase__     =  {
             """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 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         lowercase__     =  """cpu"""

         lowercase__     =  self.get_dummy_components()

         lowercase__     =  self.pipeline_class(**_UpperCamelCase)
         lowercase__     =  pipe.to(_UpperCamelCase)

         pipe.set_progress_bar_config(disable=_UpperCamelCase)

         lowercase__     =  pipe(**self.get_dummy_inputs(_UpperCamelCase))
         lowercase__     =  output.images

         lowercase__     =  pipe(
             **self.get_dummy_inputs(_UpperCamelCase)			,				return_dict=_UpperCamelCase			,				)[0]

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

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

         lowercase__     =  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       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Any)	->				Dict:




         """simple docstring"""

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



   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				int:




         """simple docstring"""

         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_12, 5_12))

         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(_UpperCamelCase)).float() / 2_55.0
         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(_UpperCamelCase)

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

         pipeline.set_progress_bar_config(disable=_UpperCamelCase)

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

         lowercase__     =  pipe_prior(
             _UpperCamelCase			,				image=_UpperCamelCase			,				strength=0.85			,				generator=_UpperCamelCase			,				negative_prompt=''			,				).to_tuple()

         lowercase__     =  pipeline(
             image=_UpperCamelCase			,				image_embeds=_UpperCamelCase			,				negative_image_embeds=_UpperCamelCase			,				hint=_UpperCamelCase			,				generator=_UpperCamelCase			,				num_inference_steps=1_00			,				height=5_12			,				width=5_12			,				strength=0.5			,				output_type='np'			,				)

         lowercase__     =  output.images[0]

         assert image.shape == (5_12, 5_12, 3)

         assert_mean_pixel_difference(_UpperCamelCase			,				_UpperCamelCase)





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a__      :				Union[str, Any]   	=			logging.get_logger(__name__)

a__      :				Optional[Any]   	=			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    "umt5"
   A    :  List[str]       				=    ["past_key_values"]


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Optional[int]=25_01_12			,				lowerCAmelCase  :			str=5_12			,				lowerCAmelCase  :			List[Any]=64			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Union[str, Any]=8			,				lowerCAmelCase  :			Tuple=None			,				lowerCAmelCase  :			Optional[Any]=6			,				lowerCAmelCase  :			int=32			,				lowerCAmelCase  :			int=1_28			,				lowerCAmelCase  :			List[str]=0.1			,				lowerCAmelCase  :			List[str]=1E-6			,				lowerCAmelCase  :			Optional[int]=1.0			,				lowerCAmelCase  :			Optional[Any]="gated-gelu"			,				lowerCAmelCase  :			List[Any]=True			,				lowerCAmelCase  :			List[str]=True			,				lowerCAmelCase  :			List[Any]="T5Tokenizer"			,				lowerCAmelCase  :			str=True			,				lowerCAmelCase  :			Optional[int]=0			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			Any=0			,				**lowerCAmelCase  :			int			,				)	->				str:




         """simple docstring"""

         super().__init__(
             is_encoder_decoder=lowerCAmelCase			,				tokenizer_class=lowerCAmelCase			,				tie_word_embeddings=lowerCAmelCase			,				pad_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  d_kv
         lowercase__     =  d_ff
         lowercase__     =  num_layers
         lowercase__     =  (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__     =  num_heads
         lowercase__     =  relative_attention_num_buckets
         lowercase__     =  relative_attention_max_distance
         lowercase__     =  dropout_rate
         lowercase__     =  layer_norm_epsilon
         lowercase__     =  initializer_factor
         lowercase__     =  feed_forward_proj
         lowercase__     =  use_cache

         lowercase__     =  self.feed_forward_proj.split('-')
         lowercase__     =  act_info[-1]
         lowercase__     =  act_info[0] == 'gated'

         if len(lowerCAmelCase) > 1 and act_info[0] != "gated" or len(lowerCAmelCase) > 2:
               raise ValueError(
                   f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                   'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                   '\'gated-gelu\' or \'relu\'')

         if feed_forward_proj == "gated-gelu":
               lowercase__     =  'gelu_new'


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return self.d_model


   @property
   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         return self.num_heads



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




         """simple docstring"""

         return self.num_layers



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         lowercase__     =  {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
               lowercase__     =  'past_encoder_sequence + sequence'
               lowercase__     =  {0: 'batch'}
               lowercase__     =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
               self.fill_with_past_key_values_(lowerCAmelCase			,				direction='inputs')

         return common_inputs


   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return 13



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




         """simple docstring"""

         return 5E-4
