




'''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, KandinskyImgaImgPipeline, 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 			_snake_case		(     lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_					:				str							=   KandinskyImgaImgPipeline
			lowerCAmelCase_					:				Union[str, Any]							=   ["prompt", "image_embeds", "negative_image_embeds", "image"]
			lowerCAmelCase_					:				Optional[int]							=   [
			    "prompt",
			    "negative_prompt",
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			]
			lowerCAmelCase_					:				Optional[Any]							=   [
			    "generator",
			    "height",
			    "width",
			    "strength",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCAmelCase_					:				Optional[Any]							=   False




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

										'''simple docstring'''




										return 32




			@property
			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										return 32




			@property
			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										return self.time_input_dim




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

										'''simple docstring'''




										return self.time_input_dim * 4




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

										'''simple docstring'''




										return 100




			@property
			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"							)
										return tokenizer




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

										'''simple docstring'''




										torch.manual_seed(0							)
										snake_case_      =				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=1_005    ,							)

										snake_case_      =				MultilingualCLIP(a__							)
										snake_case_      =				text_encoder.eval()

										return text_encoder




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

										'''simple docstring'''




										torch.manual_seed(0							)

										snake_case_      =				{
										    "in_channels": 4,
										    # 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,
										}

										snake_case_      =				UNetaDConditionModel(**a__							)
										return model




			@property
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''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        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




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




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

										'''simple docstring'''




										snake_case_      =				self.dummy_text_encoder
										snake_case_      =				self.dummy_tokenizer
										snake_case_      =				self.dummy_unet
										snake_case_      =				self.dummy_movq

										snake_case_      =				{
										    "num_train_timesteps": 1_000,
										    "beta_schedule": "linear",
										    "beta_start": 0.0_0_0_8_5,
										    "beta_end": 0.0_1_2,
										    "clip_sample": False,
										    "set_alpha_to_one": False,
										    "steps_offset": 0,
										    "prediction_type": "epsilon",
										    "thresholding": False,
										}

										snake_case_      =				DDIMScheduler(**a__							)

										snake_case_      =				{
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "movq": movq,
										}

										return components




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__=0							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				floats_tensor((1, self.cross_attention_dim)    ,							rng=random.Random(a__							)							).to(a__							)
										snake_case_      =				floats_tensor((1, self.cross_attention_dim)    ,							rng=random.Random(seed + 1							)							).to(a__							)
										# create init_image
										snake_case_      =				floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(a__							)							).to(a__							)
										snake_case_      =				image.cpu().permute(0    ,							2    ,							3    ,							1							)[0]
										snake_case_      =				Image.fromarray(np.uinta(a__							)							).convert("RGB"							).resize((256, 256)							)

										if str(a__							).startswith("mps"							):
																	snake_case_      =				torch.manual_seed(a__							)
										else:
																	snake_case_      =				torch.Generator(device=a__							).manual_seed(a__							)
										snake_case_      =				{
										    "prompt": "horse",
										    "image": init_image,
										    "image_embeds": image_embeds,
										    "negative_image_embeds": negative_image_embeds,
										    "generator": generator,
										    "height": 64,
										    "width": 64,
										    "num_inference_steps": 10,
										    "guidance_scale": 7.0,
										    "strength": 0.2,
										    "output_type": "np",
										}
										return inputs




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				"cpu"

										snake_case_      =				self.get_dummy_components()

										snake_case_      =				self.pipeline_class(**a__							)
										snake_case_      =				pipe.to(a__							)

										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				pipe(**self.get_dummy_inputs(a__							)							)
										snake_case_      =				output.images

										snake_case_      =				pipe(
										    **self.get_dummy_inputs(a__							)    ,							return_dict=a__    ,							)[0]

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

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

										snake_case_      =				np.array(
										    [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]							)
										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 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinsky/kandinsky_img2img_frog.npy"							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"							)
										snake_case_      =				"A red cartoon frog, 4k"

										snake_case_      =				KandinskyPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-1-prior"    ,							torch_dtype=torch.floataa							)
										pipe_prior.to(a__							)

										snake_case_      =				KandinskyImgaImgPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-1"    ,							torch_dtype=torch.floataa							)
										snake_case_      =				pipeline.to(a__							)

										pipeline.set_progress_bar_config(disable=a__							)

										snake_case_      =				torch.Generator(device="cpu"							).manual_seed(0							)
										snake_case_ ,				snake_case_      =				pipe_prior(
										    a__    ,							generator=a__    ,							num_inference_steps=5    ,							negative_prompt=""    ,							).to_tuple()

										snake_case_      =				pipeline(
										    a__    ,							image=a__    ,							image_embeds=a__    ,							negative_image_embeds=a__    ,							generator=a__    ,							num_inference_steps=100    ,							height=768    ,							width=768    ,							strength=0.2    ,							output_type="np"    ,							)

										snake_case_      =				output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(a__    ,							a__							)










'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   "mgp-str"




			def __init__( self    ,							a__=[32, 128]    ,							a__=4    ,							a__=3    ,							a__=27    ,							a__=38    ,							a__=50_257    ,							a__=30_522    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__=4.0    ,							a__=True    ,							a__=False    ,							a__=1e-5    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0    ,							a__=False    ,							a__=0.0_2    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				max_token_length
										snake_case_      =				num_character_labels
										snake_case_      =				num_bpe_labels
										snake_case_      =				num_wordpiece_labels
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				mlp_ratio
										snake_case_      =				distilled
										snake_case_      =				layer_norm_eps
										snake_case_      =				drop_rate
										snake_case_      =				qkv_bias
										snake_case_      =				attn_drop_rate
										snake_case_      =				drop_path_rate
										snake_case_      =				output_aa_attentions
										snake_case_      =				initializer_range




