



"""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    __lowercase     (					_UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							List[str]							       =		KandinskyInpaintPipeline
						_A		:							List[str]							       =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						_A		:							List[Any]							       =		[
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						_A		:							Union[str, Any]							       =		[
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_A		:							Tuple							       =		False







						@property
						def 						__UpperCamelCase							(self       ):
											return 32







						@property
						def 						__UpperCamelCase							(self       ):
											return 32







						@property
						def 						__UpperCamelCase							(self       ):
											return self.time_input_dim







						@property
						def 						__UpperCamelCase							(self       ):
											return self.time_input_dim * 4







						@property
						def 						__UpperCamelCase							(self       ):
											return 1_00







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""       )
											return tokenizer







						@property
						def 						__UpperCamelCase							(self       ):
											torch.manual_seed(0       )
											snake_case_					:		Union[str, 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   ,		)

											snake_case_					:		List[Any] 					=					MultilingualCLIP(lowercase__       )
											snake_case_					:		Tuple 					=					text_encoder.eval()

											return text_encoder







						@property
						def 						__UpperCamelCase							(self       ):
											torch.manual_seed(0       )

											snake_case_					:		Dict 					=					{
											    """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,
											}

											snake_case_					:		Union[str, Any] 					=					UNetaDConditionModel(**lowercase__       )
											return model







						@property
						def 						__UpperCamelCase							(self       ):
											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       ):
											torch.manual_seed(0       )
											snake_case_					:		int 					=					VQModel(**self.dummy_movq_kwargs       )
											return model







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.dummy_text_encoder
											snake_case_					:		Tuple 					=					self.dummy_tokenizer
											snake_case_					:		Optional[Any] 					=					self.dummy_unet
											snake_case_					:		int 					=					self.dummy_movq

											snake_case_					:		Optional[int] 					=					DDIMScheduler(
											    num_train_timesteps=10_00   ,		beta_schedule="""linear"""   ,		beta_start=0.00085   ,		beta_end=0.012   ,		clip_sample=lowercase__   ,		set_alpha_to_one=lowercase__   ,		steps_offset=1   ,		prediction_type="""epsilon"""   ,		thresholding=lowercase__   ,		)

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

											return components







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

											if str(lowercase__       ).startswith("""mps"""       ):
																snake_case_					:		Any 					=					torch.manual_seed(lowercase__       )
											else:
																snake_case_					:		Dict 					=					torch.Generator(device=lowercase__       ).manual_seed(lowercase__       )
											snake_case_					:		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       ):
											snake_case_					:		Union[str, Any] 					=					"""cpu"""

											snake_case_					:		Dict 					=					self.get_dummy_components()

											snake_case_					:		Any 					=					self.pipeline_class(**lowercase__       )
											snake_case_					:		Any 					=					pipe.to(lowercase__       )

											pipe.set_progress_bar_config(disable=lowercase__       )

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

											snake_case_					:		Any 					=					pipe(
											    **self.get_dummy_inputs(lowercase__       )   ,		return_dict=lowercase__   ,		)[0]

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

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

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

											snake_case_					:		Dict 					=					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       ):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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







						"""simple docstring"""







						def 						__UpperCamelCase							(self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""       )

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

											snake_case_					:		str 					=					"""a hat"""

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

											snake_case_					:		Union[str, Any] 					=					KandinskyInpaintPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-1-inpaint"""   ,		torch_dtype=torch.floataa       )
											snake_case_					:		List[str] 					=					pipeline.to(lowercase__       )
											pipeline.set_progress_bar_config(disable=lowercase__       )

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

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

											snake_case_					:		Union[str, Any] 					=					output.images[0]

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

											assert_mean_pixel_difference(lowercase__   ,		lowercase__       )








"""simple docstring"""

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


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""dpr"""







						def __init__(self   ,		lowercase__=3_05_22   ,		lowercase__=7_68   ,		lowercase__=12   ,		lowercase__=12   ,		lowercase__=30_72   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=1e-12   ,		lowercase__=0   ,		lowercase__="absolute"   ,		lowercase__ = 0   ,		**lowercase__   ,		):
											super().__init__(pad_token_id=lowercase__   ,		**lowercase__       )

											snake_case_					:		List[Any] 					=					vocab_size
											snake_case_					:		List[str] 					=					hidden_size
											snake_case_					:		Tuple 					=					num_hidden_layers
											snake_case_					:		List[Any] 					=					num_attention_heads
											snake_case_					:		int 					=					hidden_act
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		int 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		str 					=					max_position_embeddings
											snake_case_					:		List[str] 					=					type_vocab_size
											snake_case_					:		List[str] 					=					initializer_range
											snake_case_					:		Optional[int] 					=					layer_norm_eps
											snake_case_					:		Union[str, Any] 					=					projection_dim
											snake_case_					:		str 					=					position_embedding_type




