
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						List[Any]        =		KandinskyImgaImgPipeline
		a__       :						Dict        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a__       :						Tuple        =		[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a__       :						List[Any]        =		[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a__       :						Optional[int]        =		False






		@property
		def  __A       (		self		:							List[Any]  )     ->							int:
						return 32






		@property
		def  __A       (		self		:							Tuple  )     ->							Optional[int]:
						return 32






		@property
		def  __A       (		self		:							List[Any]  )     ->							List[Any]:
						return self.time_input_dim






		@property
		def  __A       (		self		:							List[str]  )     ->							List[Any]:
						return self.time_input_dim * 4






		@property
		def  __A       (		self		:							Union[str, Any]  )     ->							str:
						return 1_00






		@property
		def  __A       (		self		:							int  )     ->							List[str]:
						__lowerCamelCase    					=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''  )
						return tokenizer






		@property
		def  __A       (		self		:							List[Any]  )     ->							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=37							,			num_attention_heads=4							,			num_hidden_layers=5							,			vocab_size=10_05							,			)

						__lowerCamelCase    					=	MultilingualCLIP(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	text_encoder.eval()

						return text_encoder






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

						__lowerCamelCase    					=	{
						    '''in_channels''': 4,
						    # 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(**SCREAMING_SNAKE_CASE__  )
						return model






		@property
		def  __A       (		self		:							Union[str, Any]  )     ->							Tuple:
						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		:							Any  )     ->							Optional[Any]:
						torch.manual_seed(0  )
						__lowerCamelCase    					=	VQModel(**self.dummy_movq_kwargs  )
						return model






		def  __A       (		self		:							int  )     ->							Dict:
						__lowerCamelCase    					=	self.dummy_text_encoder
						__lowerCamelCase    					=	self.dummy_tokenizer
						__lowerCamelCase    					=	self.dummy_unet
						__lowerCamelCase    					=	self.dummy_movq

						__lowerCamelCase    					=	{
						    '''num_train_timesteps''': 10_00,
						    '''beta_schedule''': '''linear''',
						    '''beta_start''': 0.00085,
						    '''beta_end''': 0.012,
						    '''clip_sample''': False,
						    '''set_alpha_to_one''': False,
						    '''steps_offset''': 0,
						    '''prediction_type''': '''epsilon''',
						    '''thresholding''': False,
						}

						__lowerCamelCase    					=	DDIMScheduler(**SCREAMING_SNAKE_CASE__  )

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

						return components






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int=0  )     ->							Dict:
						__lowerCamelCase    					=	floats_tensor((1, self.cross_attention_dim)							,			rng=random.Random(SCREAMING_SNAKE_CASE__  )  ).to(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	floats_tensor((1, self.cross_attention_dim)							,			rng=random.Random(seed + 1  )  ).to(SCREAMING_SNAKE_CASE__  )
						# create init_image
						__lowerCamelCase    					=	floats_tensor((1, 3, 64, 64)							,			rng=random.Random(SCREAMING_SNAKE_CASE__  )  ).to(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	image.cpu().permute(0							,			2							,			3							,			1  )[0]
						__lowerCamelCase    					=	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__  )  ).convert('''RGB'''  ).resize((2_56, 2_56)  )

						if str(SCREAMING_SNAKE_CASE__  ).startswith('''mps'''  ):
										__lowerCamelCase    					=	torch.manual_seed(SCREAMING_SNAKE_CASE__  )
						else:
										__lowerCamelCase    					=	torch.Generator(device=SCREAMING_SNAKE_CASE__  ).manual_seed(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	{
						    '''prompt''': '''horse''',
						    '''image''': init_image,
						    '''image_embeds''': image_embeds,
						    '''negative_image_embeds''': negative_image_embeds,
						    '''generator''': generator,
						    '''height''': 64,
						    '''width''': 64,
						    '''num_inference_steps''': 10,
						    '''guidance_scale''': 7.0,
						    '''strength''': 0.2,
						    '''output_type''': '''np''',
						}
						return inputs







		def  __A       (		self		:							Tuple  )     ->							str:
						__lowerCamelCase    					=	'''cpu'''

						__lowerCamelCase    					=	self.get_dummy_components()

						__lowerCamelCase    					=	self.pipeline_class(**SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	pipe.to(SCREAMING_SNAKE_CASE__  )

						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )  )
						__lowerCamelCase    					=	output.images

						__lowerCamelCase    					=	pipe(
						    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )							,			return_dict=SCREAMING_SNAKE_CASE__							,			)[0]

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

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

						__lowerCamelCase    					=	np.array(
						    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]  )
						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()}'''





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






		def  __A       (		self		:							Optional[int]  )     ->							Tuple:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







		def  __A       (		self		:							str  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/kandinsky/kandinsky_img2img_frog.npy'''  )

						__lowerCamelCase    					=	load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
						__lowerCamelCase    					=	'''A red cartoon frog, 4k'''

						__lowerCamelCase    					=	KandinskyPriorPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-1-prior'''							,			torch_dtype=torch.floataa  )
						pipe_prior.to(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	KandinskyImgaImgPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-1'''							,			torch_dtype=torch.floataa  )
						__lowerCamelCase    					=	pipeline.to(SCREAMING_SNAKE_CASE__  )

						pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

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

						__lowerCamelCase    					=	pipeline(
						    SCREAMING_SNAKE_CASE__							,			image=SCREAMING_SNAKE_CASE__							,			image_embeds=SCREAMING_SNAKE_CASE__							,			negative_image_embeds=SCREAMING_SNAKE_CASE__							,			generator=SCREAMING_SNAKE_CASE__							,			num_inference_steps=1_00							,			height=7_68							,			width=7_68							,			strength=0.2							,			output_type='''np'''							,			)

						__lowerCamelCase    					=	output.images[0]

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

						assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



