
'''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  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     KandinskyInpaintPipeline
					lowerCAmelCase_        =     ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
					lowerCAmelCase_        =     [
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					    '''mask_image''',
					]
					lowerCAmelCase_        =     [
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowerCAmelCase_        =     False






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 32






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 32






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.time_input_dim






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.time_input_dim * 4






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 1_00






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
											return tokenizer






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_	:     Optional[Any]													=       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				,							)

											lowercase_	:     int													=       MultilingualCLIP(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       text_encoder.eval()

											return text_encoder






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)

											lowercase_	:     Any													=       {
											    '''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,
											}

											lowercase_	:     int													=       UNetaDConditionModel(**__SCREAMING_SNAKE_CASE		)
											return model






					@property
					def 	_snake_case    (   self		):


											"""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 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_	:     Tuple													=       VQModel(**self.dummy_movq_kwargs		)
											return model






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       self.dummy_text_encoder
											lowercase_	:     int													=       self.dummy_tokenizer
											lowercase_	:     Tuple													=       self.dummy_unet
											lowercase_	:     List[Any]													=       self.dummy_movq

											lowercase_	:     Dict													=       DDIMScheduler(
											    num_train_timesteps=10_00				,							beta_schedule='''linear'''				,							beta_start=0.00_085				,							beta_end=0.012				,							clip_sample=__SCREAMING_SNAKE_CASE				,							set_alpha_to_one=__SCREAMING_SNAKE_CASE				,							steps_offset=1				,							prediction_type='''epsilon'''				,							thresholding=__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     List[Any]													=       {
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''movq''': movq,
											}

											return components






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





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

											if str(__SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
																	lowercase_	:     str													=       torch.manual_seed(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     str													=       torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       '''cpu'''

											lowercase_	:     Dict													=       self.get_dummy_components()

											lowercase_	:     List[str]													=       self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       pipe.to(__SCREAMING_SNAKE_CASE		)

											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_	:     List[Any]													=       output.images

											lowercase_	:     str													=       pipe(
											    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)				,							return_dict=__SCREAMING_SNAKE_CASE				,							)[0]

											lowercase_	:     Optional[Any]													=       image[0, -3:, -3:, -1]
											lowercase_	:     Tuple													=       image_from_tuple[0, -3:, -3:, -1]

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

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

											lowercase_	:     Optional[int]													=       np.array(
											    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]		)

											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 	_snake_case    (   self		):


											"""simple docstring"""





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







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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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

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

											lowercase_	:     int													=       '''a hat'''

											lowercase_	:     int													=       KandinskyPriorPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-prior'''				,							torch_dtype=torch.floataa		)
											pipe_prior.to(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       KandinskyInpaintPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-inpaint'''				,							torch_dtype=torch.floataa		)
											lowercase_	:     Dict													=       pipeline.to(__SCREAMING_SNAKE_CASE		)
											pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       torch.Generator(device='''cpu'''		).manual_seed(0		)
											lowercase_						, lowercase_	:     Dict													=       pipe_prior(
											    __SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							num_inference_steps=5				,							negative_prompt=''''''				,							).to_tuple()

											lowercase_	:     Dict													=       pipeline(
											    __SCREAMING_SNAKE_CASE				,							image=__SCREAMING_SNAKE_CASE				,							mask_image=__SCREAMING_SNAKE_CASE				,							image_embeds=__SCREAMING_SNAKE_CASE				,							negative_image_embeds=__SCREAMING_SNAKE_CASE				,							generator=__SCREAMING_SNAKE_CASE				,							num_inference_steps=1_00				,							height=7_68				,							width=7_68				,							output_type='''np'''				,							)

											lowercase_	:     Tuple													=       output.images[0]

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

											assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)







'''simple docstring'''
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__    					=      logging.get_logger(__name__)





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





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				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)
    ):
        UpperCAmelCase__			:		Union[str, 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:
        UpperCAmelCase__			:		Optional[int]													=				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:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

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

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

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

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

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

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

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

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        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
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
