
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    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					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       :  List[Any] ):
												return 3_2

					@property
					def  lowercase__   (						self       :  Union[str, Any] ):
												return 3_2

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.time_input_dim

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

					@property
					def  lowercase__   (						self       :  str ):
												return 1_0_0

					@property
					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
												return tokenizer

					@property
					def  lowercase__   (						self       :  List[str] ):
												torch.manual_seed(0 )
												lowerCamelCase_					=       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 ,       )

												lowerCamelCase_					=       MultilingualCLIP(__UpperCamelCase )
												lowerCamelCase_					=       text_encoder.eval()

												return text_encoder

					@property
					def  lowercase__   (						self       :  Union[str, Any] ):
												torch.manual_seed(0 )

												lowerCamelCase_					=       {
												    """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,
												}

												lowerCamelCase_					=       UNetaDConditionModel(**__UpperCamelCase )
												return model

					@property
					def  lowercase__   (						self       :  str ):
												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       :  Dict ):
												torch.manual_seed(0 )
												lowerCamelCase_					=       VQModel(**self.dummy_movq_kwargs )
												return model

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.dummy_text_encoder
												lowerCamelCase_					=       self.dummy_tokenizer
												lowerCamelCase_					=       self.dummy_unet
												lowerCamelCase_					=       self.dummy_movq

												lowerCamelCase_					=       DDIMScheduler(
												    num_train_timesteps=1_0_0_0 ,       beta_schedule="""linear""" ,       beta_start=0.00085 ,       beta_end=0.012 ,       clip_sample=__UpperCamelCase ,       set_alpha_to_one=__UpperCamelCase ,       steps_offset=1 ,       prediction_type="""epsilon""" ,       thresholding=__UpperCamelCase ,       )

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

												return components

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Optional[Any]=0 ):
												lowerCamelCase_					=       floats_tensor((1, self.cross_attention_dim) ,       rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
												lowerCamelCase_					=       floats_tensor((1, self.cross_attention_dim) ,       rng=random.Random(seed + 1 ) ).to(__UpperCamelCase )
												# create init_image
												lowerCamelCase_					=       floats_tensor((1, 3, 6_4, 6_4) ,       rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 )[0]
												lowerCamelCase_					=       Image.fromarray(np.uinta(__UpperCamelCase ) ).convert("""RGB""" ).resize((2_5_6, 2_5_6) )
												# create mask
												lowerCamelCase_					=       np.ones((6_4, 6_4) ,       dtype=np.floataa )
												lowerCamelCase_					=       0

												if str(__UpperCamelCase ).startswith("""mps""" ):
																			lowerCamelCase_					=       torch.manual_seed(__UpperCamelCase )
												else:
																			lowerCamelCase_					=       torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
												lowerCamelCase_					=       {
												    """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] ):
												lowerCamelCase_					=       """cpu"""

												lowerCamelCase_					=       self.get_dummy_components()

												lowerCamelCase_					=       self.pipeline_class(**__UpperCamelCase )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )

												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       pipe(**self.get_dummy_inputs(__UpperCamelCase ) )
												lowerCamelCase_					=       output.images

												lowerCamelCase_					=       pipe(
												    **self.get_dummy_inputs(__UpperCamelCase ) ,       return_dict=__UpperCamelCase ,       )[0]

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

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

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

												lowerCamelCase_					=       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  lowercase__   (						self       :  Dict ):
												super().test_inference_batch_single_identical(expected_max_diff=3E-3 )





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

					def  lowercase__   (						self       :  Optional[Any] ):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



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

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

												lowerCamelCase_					=       """a hat"""

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

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

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

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

												lowerCamelCase_					=       output.images[0]

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

												assert_mean_pixel_difference(__UpperCamelCase ,       __UpperCamelCase )







import os
import sys

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)





