
'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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  A  (						SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :List[Any]   =  KandinskyInpaintPipeline
	snake_case__   :str   =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	snake_case__   :List[str]   =  [
	    'prompt',
	    'negative_prompt',
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	snake_case__   :List[Any]   =  [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'negative_prompt',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	snake_case__   :Tuple   =  False





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				return 32





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


				"""simple docstring"""

				return 32





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				return self.time_input_dim





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self.time_input_dim * 4





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return 100





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

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





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				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=1005   ,				)

				lowerCAmelCase__										=				MultilingualCLIP(__magic_name__				)
				lowerCAmelCase__										=				text_encoder.eval()

				return text_encoder





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				torch.manual_seed(0				)

				lowerCAmelCase__										=				{
				    "in_channels": 9,
				    # 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(**__magic_name__				)
				return model





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


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.dummy_text_encoder
				lowerCAmelCase__										=				self.dummy_tokenizer
				lowerCAmelCase__										=				self.dummy_unet
				lowerCAmelCase__										=				self.dummy_movq

				lowerCAmelCase__										=				DDIMScheduler(
				    num_train_timesteps=1000   ,				beta_schedule="linear"   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				clip_sample=__magic_name__   ,				set_alpha_to_one=__magic_name__   ,				steps_offset=1   ,				prediction_type="epsilon"   ,				thresholding=__magic_name__   ,				)

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

				return components





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Union[str, Any]=0				):


				"""simple docstring"""

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

				if str(__magic_name__				).startswith("mps"				):
							lowerCAmelCase__										=				torch.manual_seed(__magic_name__				)
				else:
							lowerCAmelCase__										=				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
				lowerCAmelCase__										=				{
				    "prompt": "horse",
				    "image": init_image,
				    "mask_image": mask,
				    "image_embeds": image_embeds,
				    "negative_image_embeds": negative_image_embeds,
				    "generator": generator,
				    "height": 64,
				    "width": 64,
				    "num_inference_steps": 2,
				    "guidance_scale": 4.0,
				    "output_type": "np",
				}
				return inputs





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				"cpu"

				lowerCAmelCase__										=				self.get_dummy_components()

				lowerCAmelCase__										=				self.pipeline_class(**__magic_name__				)
				lowerCAmelCase__										=				pipe.to(__magic_name__				)

				pipe.set_progress_bar_config(disable=__magic_name__				)

				lowerCAmelCase__										=				pipe(**self.get_dummy_inputs(__magic_name__				)				)
				lowerCAmelCase__										=				output.images

				lowerCAmelCase__										=				pipe(
				    **self.get_dummy_inputs(__magic_name__				)   ,				return_dict=__magic_name__   ,				)[0]

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

				print(f"""image.shape {image.shape}"""				)

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

				lowerCAmelCase__										=				np.array(
				    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]				)

				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()}"""







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				super().test_inference_batch_single_identical(expected_max_diff=3E-3				)






@slow
@require_torch_gpu
class  A  (						unittest.TestCase     ):





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"				)

				lowerCAmelCase__										=				load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"				)
				lowerCAmelCase__										=				np.ones((768, 768)   ,				dtype=np.floataa				)
				lowerCAmelCase__										=				0

				lowerCAmelCase__										=				"a hat"

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

				lowerCAmelCase__										=				KandinskyInpaintPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-1-inpaint"   ,				torch_dtype=torch.floataa				)
				lowerCAmelCase__										=				pipeline.to(__magic_name__				)
				pipeline.set_progress_bar_config(disable=__magic_name__				)

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

				lowerCAmelCase__										=				pipeline(
				    __magic_name__   ,				image=__magic_name__   ,				mask_image=__magic_name__   ,				image_embeds=__magic_name__   ,				negative_image_embeds=__magic_name__   ,				generator=__magic_name__   ,				num_inference_steps=100   ,				height=768   ,				width=768   ,				output_type="np"   ,				)

				lowerCAmelCase__										=				output.images[0]

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

				assert_mean_pixel_difference(__magic_name__   ,				__magic_name__				)








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)