


from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__snake_case            =   {
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """ernie_m"""
			lowercase						= {"dropout": "classifier_dropout", "num_classes": "num_labels"}




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		int = 2_5_0_0_0_2       ,       __magic_name__							:		int = 7_6_8       ,       __magic_name__							:		int = 1_2       ,       __magic_name__							:		int = 1_2       ,       __magic_name__							:		int = 3_0_7_2       ,       __magic_name__							:		str = "gelu"       ,       __magic_name__							:		float = 0.1       ,       __magic_name__							:		float = 0.1       ,       __magic_name__							:		int = 5_1_4       ,       __magic_name__							:		float = 0.02       ,       __magic_name__							:		int = 1       ,       __magic_name__							:		float = 1e-05       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		List[Any]=False       ,       __magic_name__							:		Union[str, Any]=0.0       ,       **__magic_name__							:		Union[str, Any]       ,       ):







						"""simple docstring"""







						super().__init__(pad_token_id=__magic_name__       ,       **__magic_name__						)
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					layer_norm_eps
						UpperCamelCase         =					classifier_dropout
						UpperCamelCase         =					is_decoder
						UpperCamelCase         =					act_dropout








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					(       __snake_case      , unittest.TestCase    ):
			lowercase						= KandinskyInpaintPipeline
			lowercase						= ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowercase						= [
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowercase						= [
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowercase						= False




			@property
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						return 3_2




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







						"""simple docstring"""







						return self.time_input_dim




			@property
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						return self.time_input_dim * 4




			@property
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						return 1_0_0




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







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




			@property
			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					MCLIPConfig(
						    numDims=self.cross_attention_dim       ,       transformerDimensions=self.text_embedder_hidden_size       ,       hidden_size=self.text_embedder_hidden_size       ,       intermediate_size=3_7       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       vocab_size=1_0_0_5       ,       )

						UpperCamelCase         =					MultilingualCLIP(__magic_name__						)
						UpperCamelCase         =					text_encoder.eval()

						return text_encoder




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







						"""simple docstring"""







						torch.manual_seed(0						)

						UpperCamelCase         =					{
						    """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         =					UNetaDConditionModel(**__magic_name__						)
						return model




			@property
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						return {
						    "block_out_channels": [3_2, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}




			@property
			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					self.dummy_text_encoder
						UpperCamelCase         =					self.dummy_tokenizer
						UpperCamelCase         =					self.dummy_unet
						UpperCamelCase         =					self.dummy_movq

						UpperCamelCase         =					DDIMScheduler(
						    num_train_timesteps=1_0_0_0       ,       beta_schedule="""linear"""       ,       beta_start=0.00_085       ,       beta_end=0.012       ,       clip_sample=__magic_name__       ,       set_alpha_to_one=__magic_name__       ,       steps_offset=1       ,       prediction_type="""epsilon"""       ,       thresholding=__magic_name__       ,       )

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

						return components




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]=0						):







						"""simple docstring"""







						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(seed + 1						)						).to(__magic_name__						)
						# create init_image
						UpperCamelCase         =					floats_tensor((1, 3, 6_4, 6_4)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					image.cpu().permute(0       ,       2       ,       3       ,       1						)[0]
						UpperCamelCase         =					Image.fromarray(np.uinta(__magic_name__						)						).convert("""RGB"""						).resize((2_5_6, 2_5_6)						)
						# create mask
						UpperCamelCase         =					np.ones((6_4, 6_4)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						if str(__magic_name__						).startswith("""mps"""						):
									UpperCamelCase         =					torch.manual_seed(__magic_name__						)
						else:
									UpperCamelCase         =					torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
						UpperCamelCase         =					{
						    """prompt""": """horse""",
						    """image""": init_image,
						    """mask_image""": mask,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 4.0,
						    """output_type""": """np""",
						}
						return inputs




			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					"""cpu"""

						UpperCamelCase         =					self.get_dummy_components()

						UpperCamelCase         =					self.pipeline_class(**__magic_name__						)
						UpperCamelCase         =					pipe.to(__magic_name__						)

						pipe.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					pipe(**self.get_dummy_inputs(__magic_name__						)						)
						UpperCamelCase         =					output.images

						UpperCamelCase         =					pipe(
						    **self.get_dummy_inputs(__magic_name__						)       ,       return_dict=__magic_name__       ,       )[0]

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

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

						assert image.shape == (1, 6_4, 6_4, 3)

						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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




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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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





			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""						)

						UpperCamelCase         =					load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
						UpperCamelCase         =					np.ones((7_6_8, 7_6_8)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						UpperCamelCase         =					"""a hat"""

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

						UpperCamelCase         =					KandinskyInpaintPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-inpaint"""       ,       torch_dtype=torch.floataa						)
						UpperCamelCase         =					pipeline.to(__magic_name__						)
						pipeline.set_progress_bar_config(disable=__magic_name__						)

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

						UpperCamelCase         =					pipeline(
						    __magic_name__       ,       image=__magic_name__       ,       mask_image=__magic_name__       ,       image_embeds=__magic_name__       ,       negative_image_embeds=__magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=1_0_0       ,       height=7_6_8       ,       width=7_6_8       ,       output_type="""np"""       ,       )

						UpperCamelCase         =					output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(__magic_name__       ,       __magic_name__						)




