



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 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__     , unittest.TestCase   ):
							__lowerCamelCase		:		Union[str, Any]      =KandinskyPipeline
							__lowerCamelCase		:		Any      =[
							    'prompt',
							    'image_embeds',
							    'negative_image_embeds',
							]
							__lowerCamelCase		:		int      =['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
							__lowerCamelCase		:		Optional[Any]      =[
							    'generator',
							    'height',
							    'width',
							    'latents',
							    'guidance_scale',
							    'negative_prompt',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							__lowerCamelCase		:		List[str]      =False





							@property
							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											return 32





							@property
							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											return 32





							@property
							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											return self.time_input_dim





							@property
							def   UpperCamelCase_   (				self						:						Optional[int]		):

											'''simple docstring'''
											return self.time_input_dim * 4





							@property
							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											return 100





							@property
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""		)
											return tokenizer





							@property
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											torch.manual_seed(0		)
											__a					       =						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 ,			)

											__a					       =						MultilingualCLIP(__lowercase		)
											__a					       =						text_encoder.eval()

											return text_encoder





							@property
							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											torch.manual_seed(0		)

											__a					       =						{
											    """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					       =						UNetaDConditionModel(**__lowercase		)
											return model





							@property
							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''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   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											torch.manual_seed(0		)
											__a					       =						VQModel(**self.dummy_movq_kwargs		)
											return model





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											__a					       =						self.dummy_text_encoder
											__a					       =						self.dummy_tokenizer
											__a					       =						self.dummy_unet
											__a					       =						self.dummy_movq

											__a					       =						DDIMScheduler(
											    num_train_timesteps=1000 ,			beta_schedule="""linear""" ,			beta_start=0.00085 ,			beta_end=0.012 ,			clip_sample=__lowercase ,			set_alpha_to_one=__lowercase ,			steps_offset=1 ,			prediction_type="""epsilon""" ,			thresholding=__lowercase ,			)

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





							def   UpperCamelCase_   (				self						:						Union[str, Any] ,			__lowercase						:						List[str] ,			__lowercase						:						Dict=0		):

											'''simple docstring'''
											__a					       =						floats_tensor((1, self.cross_attention_dim) ,			rng=random.Random(__lowercase		)		).to(__lowercase		)
											__a					       =						floats_tensor((1, self.cross_attention_dim) ,			rng=random.Random(seed + 1		)		).to(__lowercase		)
											if str(__lowercase		).startswith("""mps"""		):
															__a					       =						torch.manual_seed(__lowercase		)
											else:
															__a					       =						torch.Generator(device=__lowercase		).manual_seed(__lowercase		)
											__a					       =						{
											    """prompt""": """horse""",
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """generator""": generator,
											    """height""": 64,
											    """width""": 64,
											    """guidance_scale""": 4.0,
											    """num_inference_steps""": 2,
											    """output_type""": """np""",
											}
											return inputs







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						"""cpu"""

											__a					       =						self.get_dummy_components()

											__a					       =						self.pipeline_class(**__lowercase		)
											__a					       =						pipe.to(__lowercase		)

											pipe.set_progress_bar_config(disable=__lowercase		)

											__a					       =						pipe(**self.get_dummy_inputs(__lowercase		)		)
											__a					       =						output.images

											__a					       =						pipe(
											    **self.get_dummy_inputs(__lowercase		) ,			return_dict=__lowercase ,			)[0]

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

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

											__a					       =						np.array(
											    [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]		)

											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 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											__a					       =						load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""		)

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

											__a					       =						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1""" ,			torch_dtype=torch.floataa		)
											__a					       =						pipeline.to(__lowercase		)
											pipeline.set_progress_bar_config(disable=__lowercase		)

											__a					       =						"""red cat, 4k photo"""

											__a					       =						torch.Generator(device="""cuda"""		).manual_seed(0		)
											__a       ,   __a					       =						pipe_prior(
											    __lowercase ,			generator=__lowercase ,			num_inference_steps=5 ,			negative_prompt="""""" ,			).to_tuple()

											__a					       =						torch.Generator(device="""cuda"""		).manual_seed(0		)
											__a					       =						pipeline(
											    __lowercase ,			image_embeds=__lowercase ,			negative_image_embeds=__lowercase ,			generator=__lowercase ,			num_inference_steps=100 ,			output_type="""np""" ,			)

											__a					       =						output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(__lowercase ,			__lowercase		)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__     =	{
    """configuration_oneformer""": ["""ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OneFormerConfig"""],
    """processing_oneformer""": ["""OneFormerProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	["""OneFormerImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """OneFormerForUniversalSegmentation""",
						    """OneFormerModel""",
						    """OneFormerPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
						from .processing_oneformer import OneFormerProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_oneformer import OneFormerImageProcessor
						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_oneformer import (
												    ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OneFormerForUniversalSegmentation,
												    OneFormerModel,
												    OneFormerPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
