


'''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 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				Optional[int]  		=	KandinskyInpaintPipeline
				__magic_name__    :				Any  		=	["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
				__magic_name__    :				Union[str, Any]  		=	[
				    "prompt",
				    "negative_prompt",
				    "image_embeds",
				    "negative_image_embeds",
				    "image",
				    "mask_image",
				]
				__magic_name__    :				List[Any]  		=	[
				    "generator",
				    "height",
				    "width",
				    "latents",
				    "guidance_scale",
				    "negative_prompt",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				__magic_name__    :				Tuple  		=	False




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

										"""simple docstring"""




										return 32




				@property
				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										return 32




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

										"""simple docstring"""




										return self.time_input_dim




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

										"""simple docstring"""




										return self.time_input_dim * 4




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

										"""simple docstring"""




										return 100




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

										"""simple docstring"""




										UpperCAmelCase					    =     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
										return tokenizer




				@property
				def 					a__(		self      :			Tuple				)->					Optional[int]:

										"""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=37				,   num_attention_heads=4				,   num_hidden_layers=5				,   vocab_size=1005				,   )

										UpperCAmelCase					    =     MultilingualCLIP(lowerCAmelCase				)
										UpperCAmelCase					    =     text_encoder.eval()

										return text_encoder




				@property
				def 					a__(		self      :			Optional[int]				)->					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(**lowerCAmelCase				)
										return model




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

										"""simple docstring"""




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




				def 					a__(		self      :			Any				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.dummy_text_encoder
										UpperCAmelCase					    =     self.dummy_tokenizer
										UpperCAmelCase					    =     self.dummy_unet
										UpperCAmelCase					    =     self.dummy_movq

										UpperCAmelCase					    =     DDIMScheduler(
										    num_train_timesteps=1000				,   beta_schedule='''linear'''				,   beta_start=0.00085				,   beta_end=0.012				,   clip_sample=lowerCAmelCase				,   set_alpha_to_one=lowerCAmelCase				,   steps_offset=1				,   prediction_type='''epsilon'''				,   thresholding=lowerCAmelCase				,   )

										UpperCAmelCase					    =     {
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components




				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Optional[int]=0				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     floats_tensor((1, self.cross_attention_dim)				,   rng=random.Random(lowerCAmelCase				)				).to(lowerCAmelCase				)
										UpperCAmelCase					    =     floats_tensor((1, self.cross_attention_dim)				,   rng=random.Random(seed + 1				)				).to(lowerCAmelCase				)
										# create init_image
										UpperCAmelCase					    =     floats_tensor((1, 3, 64, 64)				,   rng=random.Random(lowerCAmelCase				)				).to(lowerCAmelCase				)
										UpperCAmelCase					    =     image.cpu().permute(0				,   2				,   3				,   1				)[0]
										UpperCAmelCase					    =     Image.fromarray(np.uinta(lowerCAmelCase				)				).convert('''RGB'''				).resize((256, 256)				)
										# create mask
										UpperCAmelCase					    =     np.ones((64, 64)				,   dtype=np.floataa				)
										UpperCAmelCase					    =     0

										if str(lowerCAmelCase				).startswith('''mps'''				):
																UpperCAmelCase					    =     torch.manual_seed(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(lowerCAmelCase				)
										UpperCAmelCase					    =     {
										    '''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 					a__(		self      :			Optional[int]				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     '''cpu'''

										UpperCAmelCase					    =     self.get_dummy_components()

										UpperCAmelCase					    =     self.pipeline_class(**lowerCAmelCase				)
										UpperCAmelCase					    =     pipe.to(lowerCAmelCase				)

										pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     pipe(**self.get_dummy_inputs(lowerCAmelCase				)				)
										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     pipe(
										    **self.get_dummy_inputs(lowerCAmelCase				)				,   return_dict=lowerCAmelCase				,   )[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, 64, 64, 3)

										UpperCAmelCase					    =     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 					a__(		self      :			List[str]				)->					Tuple:

										"""simple docstring"""




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







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




				def 					a__(		self      :			Union[str, Any]				)->					List[str]:

										"""simple docstring"""




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





				def 					a__(		self      :			Tuple				)->					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((768, 768)				,   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(lowerCAmelCase				)

										UpperCAmelCase					    =     KandinskyInpaintPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-1-inpaint'''				,   torch_dtype=torch.floataa				)
										UpperCAmelCase					    =     pipeline.to(lowerCAmelCase				)
										pipeline.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     torch.Generator(device='''cpu'''				).manual_seed(0				)
										UpperCAmelCase    ,    UpperCAmelCase					    =     pipe_prior(
										    lowerCAmelCase				,   generator=lowerCAmelCase				,   num_inference_steps=5				,   negative_prompt=''''''				,   ).to_tuple()

										UpperCAmelCase					    =     pipeline(
										    lowerCAmelCase				,   image=lowerCAmelCase				,   mask_image=lowerCAmelCase				,   image_embeds=lowerCAmelCase				,   negative_image_embeds=lowerCAmelCase				,   generator=lowerCAmelCase				,   num_inference_steps=100				,   height=768				,   width=768				,   output_type='''np'''				,   )

										UpperCAmelCase					    =     output.images[0]

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

										assert_mean_pixel_difference(lowerCAmelCase				,   lowerCAmelCase				)









'''simple docstring'''



from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)





