import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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__				(    UpperCAmelCase_,      unittest.TestCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					int						      =							KandinskyPipeline
	lowerCAmelCase__    :					str						      =							[
	    """prompt""",
	    """image_embeds""",
	    """negative_image_embeds""",
	]
	lowerCAmelCase__    :					Tuple						      =							["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
	lowerCAmelCase__    :					str						      =							[
	    """generator""",
	    """height""",
	    """width""",
	    """latents""",
	    """guidance_scale""",
	    """negative_prompt""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	lowerCAmelCase__    :					Union[str, Any]						      =							False







	@property
	def       A    (			self )    ->	str:
							return 3_2







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







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







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







	@property
	def       A    (			self )    ->	Any:
							return 1_0_0







	@property
	def       A    (			self )    ->	Tuple:
							a_							:			Any     		=					XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
							return tokenizer







	@property
	def       A    (			self )    ->	str:
							torch.manual_seed(0 )
							a_							:			Any     		=					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       ,     )

							a_							:			Dict     		=					MultilingualCLIP(__UpperCamelCase )
							a_							:			Optional[int]     		=					text_encoder.eval()

							return text_encoder







	@property
	def       A    (			self )    ->	Any:
							torch.manual_seed(0 )

							a_							:			int     		=					{
							    "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,
							}

							a_							:			Tuple     		=					UNetaDConditionModel(**__UpperCamelCase )
							return model







	@property
	def       A    (			self )    ->	Optional[Any]:
							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       A    (			self )    ->	int:
							torch.manual_seed(0 )
							a_							:			Dict     		=					VQModel(**self.dummy_movq_kwargs )
							return model







	def       A    (			self )    ->	Optional[int]:
							a_							:			int     		=					self.dummy_text_encoder
							a_							:			Dict     		=					self.dummy_tokenizer
							a_							:			Union[str, Any]     		=					self.dummy_unet
							a_							:			str     		=					self.dummy_movq

							a_							:			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=__UpperCamelCase       ,     set_alpha_to_one=__UpperCamelCase       ,     steps_offset=1       ,     prediction_type="epsilon"       ,     thresholding=__UpperCamelCase       ,     )

							a_							:			Tuple     		=					{
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "movq": movq,
							}
							return components







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=0 )    ->	Any:
							a_							:			int     		=					floats_tensor((1, self.cross_attention_dim)       ,     rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
							a_							:			str     		=					floats_tensor((1, self.cross_attention_dim)       ,     rng=random.Random(seed + 1 ) ).to(__UpperCamelCase )
							if str(__UpperCamelCase ).startswith("mps" ):
													a_							:			int     		=					torch.manual_seed(__UpperCamelCase )
							else:
													a_							:			Optional[Any]     		=					torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
							a_							:			Union[str, Any]     		=					{
							    "prompt": "horse",
							    "image_embeds": image_embeds,
							    "negative_image_embeds": negative_image_embeds,
							    "generator": generator,
							    "height": 6_4,
							    "width": 6_4,
							    "guidance_scale": 4.0,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
							return inputs







	def       A    (			self )    ->	Any:
							a_							:			Dict     		=					"cpu"

							a_							:			Dict     		=					self.get_dummy_components()

							a_							:			List[str]     		=					self.pipeline_class(**__UpperCamelCase )
							a_							:			Union[str, Any]     		=					pipe.to(__UpperCamelCase )

							pipe.set_progress_bar_config(disable=__UpperCamelCase )

							a_							:			Optional[int]     		=					pipe(**self.get_dummy_inputs(__UpperCamelCase ) )
							a_							:			str     		=					output.images

							a_							:			Optional[Any]     		=					pipe(
							    **self.get_dummy_inputs(__UpperCamelCase )       ,     return_dict=__UpperCamelCase       ,     )[0]

							a_							:			Tuple     		=					image[0, -3:, -3:, -1]
							a_							:			List[Any]     		=					image_from_tuple[0, -3:, -3:, -1]

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

							a_							:			Dict     		=					np.array(
							    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3] )

							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   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







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







	def       A    (			self )    ->	Any:
							a_							:			str     		=					load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/kandinsky/kandinsky_text2img_cat_fp16.npy" )

							a_							:			Union[str, Any]     		=					KandinskyPriorPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-1-prior"       ,     torch_dtype=torch.floataa )
							pipe_prior.to(__UpperCamelCase )

							a_							:			Optional[int]     		=					KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"       ,     torch_dtype=torch.floataa )
							a_							:			int     		=					pipeline.to(__UpperCamelCase )
							pipeline.set_progress_bar_config(disable=__UpperCamelCase )

							a_							:			Tuple     		=					"red cat, 4k photo"

							a_							:			List[str]     		=					torch.Generator(device="cuda" ).manual_seed(0 )
							a_		,					a_							:			Any     		=					pipe_prior(
							    __UpperCamelCase       ,     generator=__UpperCamelCase       ,     num_inference_steps=5       ,     negative_prompt=""       ,     ).to_tuple()

							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )
							a_							:			Any     		=					pipeline(
							    __UpperCamelCase       ,     image_embeds=__UpperCamelCase       ,     negative_image_embeds=__UpperCamelCase       ,     generator=__UpperCamelCase       ,     num_inference_steps=1_0_0       ,     output_type="np"       ,     )

							a_							:			Dict     		=					output.images[0]

							assert image.shape == (5_1_2, 5_1_2, 3)

							assert_mean_pixel_difference(__UpperCamelCase       ,     __UpperCamelCase )





"""simple docstring"""






def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str  )				-> int:
						a_							:			Optional[int]     		=					0
						for letter in data:
												a_							:			Union[str, Any]     		=					(hash_ << 4) + ord(_SCREAMING_SNAKE_CASE  )
												a_							:			Optional[int]     		=					hash_ & 0xf000_0000
												if x != 0:
																		hash_ ^= x >> 24
												hash_ &= ~x
						return hash_


if __name__ == "__main__":
				import doctest

				doctest.testmod()




