






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        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Tuple 					= KandinskyPipeline
		UpperCAmelCase_							:       Dict 					= [
		    'prompt',
		    'image_embeds',
		    'negative_image_embeds',
		]
		UpperCAmelCase_							:       str 					= ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
		UpperCAmelCase_							:       int 					= [
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'negative_prompt',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		UpperCAmelCase_							:       List[Any] 					= False






		@property
		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				return 32






		@property
		def 					_lowerCamelCase					(      self						)				->				int:
				return 32






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				return self.time_input_dim






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






		@property
		def 					_lowerCamelCase					(      self						)				->				Tuple:
				return 100






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
				return tokenizer






		@property
		def 					_lowerCamelCase					(      self						)				->				List[str]:
				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 					_lowerCamelCase					(      self						)				->				str:
				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 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				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						)				->				List[Any]:
				torch.manual_seed(0						)
				__A    =							VQModel(**self.dummy_movq_kwargs						)
				return model






		def 					_lowerCamelCase					(      self						)				->				int:
				__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=0.0_0085							, 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 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=0						)				->				int:
				__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 					_lowerCamelCase					(      self						)				->				List[str]:
				__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(
				    [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953]						)

				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        a__      (	unittest.TestCase		):






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__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_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_   :	Union[str, Any]							      ={'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	int							      =[
					    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''GPTJForCausalLM''',
					    '''GPTJForQuestionAnswering''',
					    '''GPTJForSequenceClassification''',
					    '''GPTJModel''',
					    '''GPTJPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''TFGPTJForCausalLM''',
					    '''TFGPTJForQuestionAnswering''',
					    '''TFGPTJForSequenceClassification''',
					    '''TFGPTJModel''',
					    '''TFGPTJPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	List[Any]							      =[
					    '''FlaxGPTJForCausalLM''',
					    '''FlaxGPTJModel''',
					    '''FlaxGPTJPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					snake_case_   :	List[str]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

