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__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=KandinskyImgaImgPipeline
			UpperCamelCase__    							=["prompt", "image_embeds", "negative_image_embeds", "image"]
			UpperCamelCase__    							=[
			    "prompt",
			    "negative_prompt",
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			]
			UpperCamelCase__    							=[
			    "generator",
			    "height",
			    "width",
			    "strength",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			UpperCamelCase__    							=False





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										return 32





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return 32





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										return self.time_input_dim





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





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										return 1_00





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'						)
										return tokenizer





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										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(lowercase__						)
										_lowerCAmelCase							=							text_encoder.eval()

										return text_encoder





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[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(**lowercase__						)
										return model





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										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   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										torch.manual_seed(0						)
										_lowerCAmelCase							=							VQModel(**self.dummy_movq_kwargs						)
										return model





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_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': 0.0_0_0_8_5,
										    'beta_end': 0.0_1_2,
										    'clip_sample': False,
										    'set_alpha_to_one': False,
										    'steps_offset': 0,
										    'prediction_type': 'epsilon',
										    'thresholding': False,
										}

										_lowerCAmelCase							=							DDIMScheduler(**lowercase__						)

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

										return components





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	Union[str, Any]=0						):
										_lowerCAmelCase							=							floats_tensor((1, self.cross_attention_dim)  ,  rng=random.Random(lowercase__						)						).to(lowercase__						)
										_lowerCAmelCase							=							floats_tensor((1, self.cross_attention_dim)  ,  rng=random.Random(seed + 1						)						).to(lowercase__						)
										# create init_image
										_lowerCAmelCase							=							floats_tensor((1, 3, 64, 64)  ,  rng=random.Random(lowercase__						)						).to(lowercase__						)
										_lowerCAmelCase							=							image.cpu().permute(0  ,  2  ,  3  ,  1						)[0]
										_lowerCAmelCase							=							Image.fromarray(np.uinta(lowercase__						)						).convert('RGB'						).resize((2_56, 2_56)						)

										if str(lowercase__						).startswith('mps'						):
																	_lowerCAmelCase							=							torch.manual_seed(lowercase__						)
										else:
																	_lowerCAmelCase							=							torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
										_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   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							'cpu'

										_lowerCAmelCase							=							self.get_dummy_components()

										_lowerCAmelCase							=							self.pipeline_class(**lowercase__						)
										_lowerCAmelCase							=							pipe.to(lowercase__						)

										pipe.set_progress_bar_config(disable=lowercase__						)

										_lowerCAmelCase							=							pipe(**self.get_dummy_inputs(lowercase__						)						)
										_lowerCAmelCase							=							output.images

										_lowerCAmelCase							=							pipe(
										    **self.get_dummy_inputs(lowercase__						)  ,  return_dict=lowercase__  ,  )[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(
										    [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_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        lowerCamelCase__				(   unittest.TestCase				):





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





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_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(lowercase__						)

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

										pipeline.set_progress_bar_config(disable=lowercase__						)

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

										_lowerCAmelCase							=							pipeline(
										    lowercase__  ,  image=lowercase__  ,  image_embeds=lowercase__  ,  negative_image_embeds=lowercase__  ,  generator=lowercase__  ,  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(lowercase__  ,  lowercase__						)







import re






def _lowerCamelCase  (		snake_case  ):

							if len(re.findall('[ATCG]'     , snake_case  )  ) != len(snake_case  ):
														raise ValueError('Invalid Strand'  )

							return dna.translate(dna.maketrans('ATCG'     , 'TAGC'  )  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






