



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 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	KandinskyInpaintPipeline
     _SCREAMING_SNAKE_CASE						=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
     _SCREAMING_SNAKE_CASE						=	[
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
         """mask_image""",
     ]
     _SCREAMING_SNAKE_CASE						=	[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     _SCREAMING_SNAKE_CASE						=	False






     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            return 3_2






     @property
     def    lowercase_   (       self  :  Dict					)   ->Tuple:
            return 3_2






     @property
     def    lowercase_   (       self  :  List[str]					)   ->int:
            return self.time_input_dim






     @property
     def    lowercase_   (       self  :  List[str]					)   ->Optional[int]:
            return self.time_input_dim * 4






     @property
     def    lowercase_   (       self  :  str					)   ->List[Any]:
            return 1_0_0






     @property
     def    lowercase_   (       self  :  Union[str, Any]					)   ->Tuple:
            snake_case__							:	List[Any]   	=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
            return tokenizer






     @property
     def    lowercase_   (       self  :  List[Any]					)   ->str:
            torch.manual_seed(0					)
            snake_case__							:	Tuple   	=	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, )

            snake_case__							:	Dict   	=	MultilingualCLIP(_snake_case					)
            snake_case__							:	Any   	=	text_encoder.eval()

            return text_encoder






     @property
     def    lowercase_   (       self  :  Optional[Any]					)   ->Union[str, Any]:
            torch.manual_seed(0					)

            snake_case__							:	Union[str, 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,
            }

            snake_case__							:	Optional[int]   	=	UNetaDConditionModel(**_snake_case					)
            return model






     @property
     def    lowercase_   (       self  :  Any					)   ->Tuple:
            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    lowercase_   (       self  :  str					)   ->Dict:
            torch.manual_seed(0					)
            snake_case__							:	List[str]   	=	VQModel(**self.dummy_movq_kwargs					)
            return model






     def    lowercase_   (       self  :  str					)   ->List[Any]:
            snake_case__							:	str   	=	self.dummy_text_encoder
            snake_case__							:	Optional[Any]   	=	self.dummy_tokenizer
            snake_case__							:	List[Any]   	=	self.dummy_unet
            snake_case__							:	Optional[int]   	=	self.dummy_movq

            snake_case__							:	Optional[Any]   	=	DDIMScheduler(
                num_train_timesteps=1_0_0_0, beta_schedule='linear', beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, clip_sample=_snake_case, set_alpha_to_one=_snake_case, steps_offset=1, prediction_type='epsilon', thresholding=_snake_case, )

            snake_case__							:	Union[str, Any]   	=	{
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }

            return components






     def    lowercase_   (       self  :  str, _snake_case  :  Any, _snake_case  :  Tuple=0					)   ->str:
            snake_case__							:	List[str]   	=	floats_tensor((1, self.cross_attention_dim), rng=random.Random(_snake_case					)					).to(_snake_case					)
            snake_case__							:	List[str]   	=	floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed + 1					)					).to(_snake_case					)
            # create init_image
            snake_case__							:	Any   	=	floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(_snake_case					)					).to(_snake_case					)
            snake_case__							:	int   	=	image.cpu().permute(0, 2, 3, 1					)[0]
            snake_case__							:	Dict   	=	Image.fromarray(np.uinta(_snake_case					)					).convert('RGB'					).resize((2_5_6, 2_5_6)					)
            # create mask
            snake_case__							:	Optional[Any]   	=	np.ones((6_4, 6_4), dtype=np.floataa					)
            snake_case__							:	Optional[Any]   	=	0

            if str(_snake_case					).startswith('mps'					):
                   snake_case__							:	Any   	=	torch.manual_seed(_snake_case					)
            else:
                   snake_case__							:	int   	=	torch.Generator(device=_snake_case					).manual_seed(_snake_case					)
            snake_case__							:	int   	=	{
                '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    lowercase_   (       self  :  List[str]					)   ->Any:
            snake_case__							:	Any   	=	'cpu'

            snake_case__							:	str   	=	self.get_dummy_components()

            snake_case__							:	Any   	=	self.pipeline_class(**_snake_case					)
            snake_case__							:	Any   	=	pipe.to(_snake_case					)

            pipe.set_progress_bar_config(disable=_snake_case					)

            snake_case__							:	Optional[int]   	=	pipe(**self.get_dummy_inputs(_snake_case					)					)
            snake_case__							:	int   	=	output.images

            snake_case__							:	str   	=	pipe(
                **self.get_dummy_inputs(_snake_case					), return_dict=_snake_case, )[0]

            snake_case__							:	Optional[Any]   	=	image[0, -3:, -3:, -1]
            snake_case__							:	Union[str, Any]   	=	image_from_tuple[0, -3:, -3:, -1]

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

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

            snake_case__							:	Any   	=	np.array(
                [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]					)

            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    lowercase_   (       self  :  Dict					)   ->Optional[int]:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3					)




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






     """simple docstring"""


     def    lowercase_   (       self  :  int					)   ->Dict:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






     def    lowercase_   (       self  :  Any					)   ->Tuple:
            snake_case__							:	Optional[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__							:	Dict   	=	np.ones((7_6_8, 7_6_8), dtype=np.floataa					)
            snake_case__							:	Union[str, Any]   	=	0

            snake_case__							:	Optional[Any]   	=	'a hat'

            snake_case__							:	Optional[int]   	=	KandinskyPriorPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-prior', torch_dtype=torch.floataa					)
            pipe_prior.to(_snake_case					)

            snake_case__							:	List[str]   	=	KandinskyInpaintPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-inpaint', torch_dtype=torch.floataa					)
            snake_case__							:	Any   	=	pipeline.to(_snake_case					)
            pipeline.set_progress_bar_config(disable=_snake_case					)

            snake_case__							:	Dict   	=	torch.Generator(device='cpu'					).manual_seed(0					)
            snake_case__	,				snake_case__							:	List[Any]   	=	pipe_prior(
                _snake_case, generator=_snake_case, num_inference_steps=5, negative_prompt='', ).to_tuple()

            snake_case__							:	Dict   	=	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_0_0, height=7_6_8, width=7_6_8, output_type='np', )

            snake_case__							:	List[Any]   	=	output.images[0]

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

            assert_mean_pixel_difference(_snake_case, _snake_case					)











from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

            snake_case__							:	Dict   	=	hidden_size
            snake_case__							:	Optional[int]   	=	num_hidden_layers
            snake_case__							:	Any   	=	num_attention_heads
            snake_case__							:	List[Any]   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Dict   	=	attention_probs_dropout_prob
            snake_case__							:	List[str]   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	Optional[int]   	=	relative_attention
            snake_case__							:	Tuple   	=	max_relative_positions
            snake_case__							:	Union[str, Any]   	=	pad_token_id
            snake_case__							:	Optional[int]   	=	position_biased_input

            # Backwards compatibility
            if type(_snake_case					) == str:
                   snake_case__							:	int   	=	[x.strip() for x in pos_att_type.lower().split('|'					)]

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	List[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]					)
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional["TensorType"] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






