
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			(   __lowercase       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	KandinskyImgaImgPipeline
			__UpperCamelCase :  Any    =	['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
			__UpperCamelCase :  int    =	[
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			__UpperCamelCase :  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''',
			]
			__UpperCamelCase :  Any    =	False

			@property
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							return 32

			@property
			def  lowerCAmelCase__		(    self	:    str ):
							return 32

			@property
			def  lowerCAmelCase__		(    self	:    Tuple ):
							return self.time_input_dim

			@property
			def  lowerCAmelCase__		(    self	:    List[str] ):
							return self.time_input_dim * 4

			@property
			def  lowerCAmelCase__		(    self	:    Dict ):
							return 100

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

			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							torch.manual_seed(0 )
							UpperCamelCase_:							int		=  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  ,		)

							UpperCamelCase_:							Tuple		=  MultilingualCLIP(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  text_encoder.eval()

							return text_encoder

			@property
			def  lowerCAmelCase__		(    self	:    Tuple ):
							torch.manual_seed(0 )

							UpperCamelCase_:							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,
							}

							UpperCamelCase_:							Tuple		=  UNetaDConditionModel(**snake_case_ )
							return model

			@property
			def  lowerCAmelCase__		(    self	:    str ):
							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  lowerCAmelCase__		(    self	:    Optional[Any] ):
							torch.manual_seed(0 )
							UpperCamelCase_:							int		=  VQModel(**self.dummy_movq_kwargs )
							return model

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Tuple		=  self.dummy_text_encoder
							UpperCamelCase_:							Dict		=  self.dummy_tokenizer
							UpperCamelCase_:							Dict		=  self.dummy_unet
							UpperCamelCase_:							int		=  self.dummy_movq

							UpperCamelCase_:							List[Any]		=  {
							    """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,
							}

							UpperCamelCase_:							Dict		=  DDIMScheduler(**snake_case_ )

							UpperCamelCase_:							Any		=  {
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    str  ,		snake_case_	:    Union[str, Any]=0 ):
							UpperCamelCase_:							Dict		=  floats_tensor((1, self.cross_attention_dim)  ,		rng=random.Random(snake_case_ ) ).to(snake_case_ )
							UpperCamelCase_:							int		=  floats_tensor((1, self.cross_attention_dim)  ,		rng=random.Random(seed + 1 ) ).to(snake_case_ )
							# create init_image
							UpperCamelCase_:							Any		=  floats_tensor((1, 3, 64, 64)  ,		rng=random.Random(snake_case_ ) ).to(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  image.cpu().permute(0  ,		2  ,		3  ,		1 )[0]
							UpperCamelCase_:							Optional[int]		=  Image.fromarray(np.uinta(snake_case_ ) ).convert("""RGB""" ).resize((256, 256) )

							if str(snake_case_ ).startswith("""mps""" ):
											UpperCamelCase_:							str		=  torch.manual_seed(snake_case_ )
							else:
											UpperCamelCase_:							str		=  torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  {
							    """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  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							Dict		=  """cpu"""

							UpperCamelCase_:							Union[str, Any]		=  self.get_dummy_components()

							UpperCamelCase_:							List[str]		=  self.pipeline_class(**snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  pipe.to(snake_case_ )

							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							List[str]		=  pipe(**self.get_dummy_inputs(snake_case_ ) )
							UpperCamelCase_:							List[str]		=  output.images

							UpperCamelCase_:							Any		=  pipe(
							    **self.get_dummy_inputs(snake_case_ )  ,		return_dict=snake_case_  ,		)[0]

							UpperCamelCase_:							Optional[int]		=  image[0, -3:, -3:, -1]
							UpperCamelCase_:							str		=  image_from_tuple[0, -3:, -3:, -1]

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

							UpperCamelCase_:							int		=  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					):






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    List[str] ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							Union[str, Any]		=  load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_img2img_frog.npy""" )

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

							UpperCamelCase_:							str		=  KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""  ,		torch_dtype=torch.floataa )
							pipe_prior.to(snake_case_ )

							UpperCamelCase_:							Union[str, Any]		=  KandinskyImgaImgPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1"""  ,		torch_dtype=torch.floataa )
							UpperCamelCase_:							Any		=  pipeline.to(snake_case_ )

							pipeline.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							List[str]		=  torch.Generator(device="""cpu""" ).manual_seed(0 )
							UpperCamelCase_:							Optional[Any]		=  pipe_prior(
							    snake_case_  ,		generator=snake_case_  ,		num_inference_steps=5  ,		negative_prompt=""""""  ,		).to_tuple()

							UpperCamelCase_:							List[str]		=  pipeline(
							    snake_case_  ,		image=snake_case_  ,		image_embeds=snake_case_  ,		negative_image_embeds=snake_case_  ,		generator=snake_case_  ,		num_inference_steps=100  ,		height=768  ,		width=768  ,		strength=0.2  ,		output_type="""np"""  ,		)

							UpperCamelCase_:							Dict		=  output.images[0]

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

							assert_mean_pixel_difference(snake_case_  ,		snake_case_ )








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_  :	Dict 				=					{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							lowerCamelCase_  :	Any 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






