


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase					:       Dict			    =     logging.get_logger(__name__)



def UpperCamelCase_				(				__a      )	->   Union[str, Any]:
      a__	:  Tuple     =   R"\w+[.]\d+"
      a__	:  List[Any]     =   re.findall(__a			,				__a      )
      for pat in pats:
            a__	:  Union[str, Any]     =   key.replace(__a			,				"_".join(pat.split("."      )      )      )
      return key



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   List[str]:
      a__	:  List[str]     =   pt_tuple_key[:-1] + ("scale",)
      if (
          any("norm" in str_ for str_ in pt_tuple_key      )
          and (pt_tuple_key[-1] == "bias")
          and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
          and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
      ):
            a__	:  Any     =   pt_tuple_key[:-1] + ("scale",)
            return renamed_pt_tuple_key, pt_tensor
      elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
            a__	:  Optional[Any]     =   pt_tuple_key[:-1] + ("scale",)
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
            a__	:  Union[str, Any]     =   pt_tuple_key[:-1] + ("embedding",)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      a__	:  List[str]     =   pt_tuple_key[:-1] + ("kernel",)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
            a__	:  str     =   pt_tensor.transpose(2			,				3			,				1			,				0      )
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      a__	:  Tuple     =   pt_tuple_key[:-1] + ("kernel",)
      if pt_tuple_key[-1] == "weight":
            a__	:  Tuple     =   pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      a__	:  Optional[Any]     =   pt_tuple_key[:-1] + ("weight",)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      a__	:  Union[str, Any]     =   pt_tuple_key[:-1] + ("bias",)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor



def UpperCamelCase_				(				__a			,				__a			,				__a=42      )	->   str:
      # Step 1: Convert pytorch tensor to numpy
      a__	:  Optional[int]     =   {k: v.numpy() for k, v in pt_state_dict.items()}

      # Step 2: Since the model is stateless, get random Flax params
      a__	:  Tuple     =   flax_model.init_weights(PRNGKey(__a      )      )

      a__	:  Optional[Any]     =   flatten_dict(__a      )
      a__	:  Union[str, Any]     =   {}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            a__	:  Optional[int]     =   rename_key(__a      )
            a__	:  Optional[int]     =   tuple(renamed_pt_key.split("."      )      )

            # Correctly rename weight parameters
            a__,	a__	:  Union[str, Any]     =   rename_key_and_reshape_tensor(__a			,				__a			,				__a      )

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                            f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''      )

        # also add unexpected weight so that warning is thrown
            a__	:  str     =   jnp.asarray(__a      )

      return unflatten_dict(__a      )



'''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, KandinskyInpaintPipeline, 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 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Union[str, Any]				=							KandinskyInpaintPipeline
 lowercase_					:    Tuple				=							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 lowercase_					:    Dict				=							[
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 lowercase_					:    Optional[Any]				=							[
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_					:    Optional[Any]				=							False







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.time_input_dim







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return self.time_input_dim * 4







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return 1_00







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
   return tokenizer







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					Dict				       =							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=10_05			,				)

   UpperCAmelCase__      :					Any				       =							MultilingualCLIP(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							text_encoder.eval()

   return text_encoder







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   torch.manual_seed(0 )

   UpperCAmelCase__      :					str				       =							{
       "in_channels": 9,
       # 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__      :					List[str]				       =							UNetaDConditionModel(**snake_case__ )
   return model







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   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 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.dummy_text_encoder
   UpperCAmelCase__      :					Any				       =							self.dummy_tokenizer
   UpperCAmelCase__      :					Tuple				       =							self.dummy_unet
   UpperCAmelCase__      :					str				       =							self.dummy_movq

   UpperCAmelCase__      :					str				       =							DDIMScheduler(
       num_train_timesteps=10_00			,				beta_schedule="linear"			,				beta_start=0.00085			,				beta_end=0.012			,				clip_sample=snake_case__			,				set_alpha_to_one=snake_case__			,				steps_offset=1			,				prediction_type="epsilon"			,				thresholding=snake_case__			,				)

   UpperCAmelCase__      :					List[Any]				       =							{
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Any			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1 ) ).to(snake_case__ )
   # create init_image
   UpperCAmelCase__      :					Any				       =							floats_tensor((1, 3, 64, 64)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							image.cpu().permute(0			,				2			,				3			,				1 )[0]
   UpperCAmelCase__      :					Optional[int]				       =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((2_56, 2_56) )
   # create mask
   UpperCAmelCase__      :					Any				       =							np.ones((64, 64)			,				dtype=np.floataa )
   UpperCAmelCase__      :					Any				       =							0

   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Tuple				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					str				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							{
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"cpu"

   UpperCAmelCase__      :					Optional[Any]				       =							self.get_dummy_components()

   UpperCAmelCase__      :					int				       =							self.pipeline_class(**snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							pipe.to(snake_case__ )

   pipe.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							pipe(**self.get_dummy_inputs(snake_case__ ) )
   UpperCAmelCase__      :					int				       =							output.images

   UpperCAmelCase__      :					Tuple				       =							pipe(
       **self.get_dummy_inputs(snake_case__ )			,				return_dict=snake_case__			,				)[0]

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

   print(F"""image.shape {image.shape}""" )

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

   UpperCAmelCase__      :					Any				       =							np.array(
       [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )

   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()}"""







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






@slow
@require_torch_gpu
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

   UpperCAmelCase__      :					List[str]				       =							load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
   UpperCAmelCase__      :					Union[str, Any]				       =							np.ones((7_68, 7_68)			,				dtype=np.floataa )
   UpperCAmelCase__      :					List[Any]				       =							0

   UpperCAmelCase__      :					Tuple				       =							"a hat"

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

   UpperCAmelCase__      :					int				       =							KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Optional[int]				       =							pipeline.to(snake_case__ )
   pipeline.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							pipe_prior(
       snake_case__			,				generator=snake_case__			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

   UpperCAmelCase__      :					Optional[int]				       =							pipeline(
       snake_case__			,				image=snake_case__			,				mask_image=snake_case__			,				image_embeds=snake_case__			,				negative_image_embeds=snake_case__			,				generator=snake_case__			,				num_inference_steps=1_00			,				height=7_68			,				width=7_68			,				output_type="np"			,				)

   UpperCAmelCase__      :					int				       =							output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )



