






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 							A   (	UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					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   lowerCamelCase__					(self   :						Dict		)						->       Union[str, Any]:




							"""simple docstring"""


							return 32

					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Dict:




							"""simple docstring"""


							return 32

					@property
					def   lowerCamelCase__					(self   :						List[str]		)						->       Any:




							"""simple docstring"""


							return self.time_input_dim

					@property
					def   lowerCamelCase__					(self   :						List[Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							return self.time_input_dim * 4

					@property
					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							return 100

					@property
					def   lowerCamelCase__					(self   :						int		)						->       Optional[int]:




							"""simple docstring"""


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

					@property
					def   lowerCamelCase__					(self   :						Dict		)						->       str:




							"""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(_UpperCAmelCase		)
							lowercase__					      =		text_encoder.eval()

							return text_encoder

					@property
					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Optional[int]:




							"""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(**_UpperCAmelCase		)
							return model

					@property
					def   lowerCamelCase__					(self   :						int		)						->       Optional[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   lowerCamelCase__					(self   :						Optional[Any]		)						->       List[str]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Tuple		)						->       Dict:




							"""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""": 0.00_085,
							    """beta_end""": 0.012,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							lowercase__					      =		DDIMScheduler(**_UpperCAmelCase		)

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

							return components

					def   lowerCamelCase__					(self   :						Tuple				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Union[str, Any]=0		)						->       int:




							"""simple docstring"""


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

							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							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   lowerCamelCase__					(self   :						Optional[Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""

							lowercase__					      =		self.get_dummy_components()

							lowercase__					      =		self.pipeline_class(**_UpperCAmelCase		)
							lowercase__					      =		pipe.to(_UpperCAmelCase		)

							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

							lowercase__					      =		pipe(**self.get_dummy_inputs(_UpperCAmelCase		)		)
							lowercase__					      =		output.images

							lowercase__					      =		pipe(
							    **self.get_dummy_inputs(_UpperCAmelCase		)				, return_dict=_UpperCAmelCase				, )[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(
							    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]		)
							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							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Union[str, Any]:




							"""simple docstring"""


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


					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Optional[int]:




							"""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(_UpperCAmelCase		)

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

							pipeline.set_progress_bar_config(disable=_UpperCAmelCase		)

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

							lowercase__					      =		pipeline(
							    _UpperCAmelCase				, image=_UpperCAmelCase				, image_embeds=_UpperCAmelCase				, negative_image_embeds=_UpperCAmelCase				, generator=_UpperCAmelCase				, 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(_UpperCAmelCase				, _UpperCAmelCase		)





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

							lowercase__					      =		{
							    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]				, dtype=tf.intaa		),  # "My dog is cute"
							    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				, dtype=tf.intaa		),
							}

							lowercase__					      =		model(_UpperCAmelCase		)["""last_hidden_state"""]
							lowercase__					      =		tf.TensorShape((1, 6, 768)		)
							self.assertEqual(output.shape				, _UpperCAmelCase		)
							# compare the actual values for a slice.
							lowercase__					      =		tf.convert_to_tensor(
							    [
							        [
							            [0.0_681_762, 0.10_894_451, 0.06_772_504],
							            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
							            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
							        ]
							    ]				, dtype=tf.floataa				, )

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()				, expected_slice.numpy()				, atol=1E-4		)		)

