



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      __UpperCamelCase       (_UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					KandinskyImgaImgPipeline
	__A			  =					['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
	__A			  =					[
	    '''prompt''',
	    '''negative_prompt''',
	    '''image_embeds''',
	    '''negative_image_embeds''',
	    '''image''',
	]
	__A			  =					[
	    '''generator''',
	    '''height''',
	    '''width''',
	    '''strength''',
	    '''guidance_scale''',
	    '''negative_prompt''',
	    '''num_inference_steps''',
	    '''return_dict''',
	    '''guidance_scale''',
	    '''num_images_per_prompt''',
	    '''output_type''',
	    '''return_dict''',
	]
	__A			  =					False







	@property
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							return 32







	@property
	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							return 32







	@property
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							return self.time_input_dim







	@property
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							return self.time_input_dim * 4







	@property
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							return 100







	@property
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
							return tokenizer







	@property
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							torch.manual_seed(0						)
							lowercase        =							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 ,				)

							lowercase        =							MultilingualCLIP(_lowerCAmelCase						)
							lowercase        =							text_encoder.eval()

							return text_encoder







	@property
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							torch.manual_seed(0						)

							lowercase        =							{
							    """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,
							}

							lowercase        =							UNetaDConditionModel(**_lowerCAmelCase						)
							return model







	@property
	def   _a   (    self						)    ->    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						)    ->    Optional[Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							self.dummy_text_encoder
							lowercase        =							self.dummy_tokenizer
							lowercase        =							self.dummy_unet
							lowercase        =							self.dummy_movq

							lowercase        =							{
							    """num_train_timesteps""": 1000,
							    """beta_schedule""": """linear""",
							    """beta_start""": 0.0_0085,
							    """beta_end""": 0.012,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							lowercase        =							DDIMScheduler(**_lowerCAmelCase						)

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

							return components







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=0						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
							lowercase        =							floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(seed + 1						)						).to(_lowerCAmelCase						)
							# create init_image
							lowercase        =							floats_tensor((1, 3, 64, 64) ,				rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
							lowercase        =							image.cpu().permute(0 ,				2 ,				3 ,				1						)[0]
							lowercase        =							Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("""RGB"""						).resize((256, 256)						)

							if str(_lowerCAmelCase						).startswith("""mps"""						):
													lowercase        =							torch.manual_seed(_lowerCAmelCase						)
							else:
													lowercase        =							torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
							lowercase        =							{
							    """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						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							"""cpu"""

							lowercase        =							self.get_dummy_components()

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

							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

							lowercase        =							pipe(
							    **self.get_dummy_inputs(_lowerCAmelCase						) ,				return_dict=_lowerCAmelCase ,				)[0]

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

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

							lowercase        =							np.array(
							    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]						)
							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			):







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_img2img_frog.npy"""						)

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

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

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

							pipeline.set_progress_bar_config(disable=_lowerCAmelCase						)

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

							lowercase        =							pipeline(
							    _lowerCAmelCase ,				image=_lowerCAmelCase ,				image_embeds=_lowerCAmelCase ,				negative_image_embeds=_lowerCAmelCase ,				generator=_lowerCAmelCase ,				num_inference_steps=100 ,				height=768 ,				width=768 ,				strength=0.2 ,				output_type="""np""" ,				)

							lowercase        =							output.images[0]

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

							assert_mean_pixel_difference(_lowerCAmelCase ,				_lowerCAmelCase						)







'''simple docstring'''


from ... import PretrainedConfig


lowercase_      :				int   					=    {
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A			  =					'''nezha'''







	def __init__(    self ,				_lowerCAmelCase=2_1128 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    int:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCAmelCase ,				bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)

							lowercase        =							vocab_size
							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							hidden_act
							lowercase        =							intermediate_size
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							max_position_embeddings
							lowercase        =							max_relative_position
							lowercase        =							type_vocab_size
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							classifier_dropout
							lowercase        =							use_cache


