



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      __lowerCAmelCase     (		UpperCamelCase__							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    KandinskyImgaImgPipeline
					A__				:		Union[str, Any]			       =    ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
					A__				:		List[Any]			       =    [
					    """prompt""",
					    """negative_prompt""",
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					]
					A__				:		Any			       =    [
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """negative_prompt""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					A__				:		Union[str, Any]			       =    False



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






								"""simple docstring"""


								return 32



					@property
					def     _a							(     self					:       int					):






								"""simple docstring"""


								return 32



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






								"""simple docstring"""


								return self.time_input_dim



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return self.time_input_dim * 4



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






								"""simple docstring"""


								return 1_00



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






								"""simple docstring"""


								A__       =    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
								return tokenizer



					@property
					def     _a							(     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=10_05				,					)

								A__       =    MultilingualCLIP(_snake_case					)
								A__       =    text_encoder.eval()

								return text_encoder



					@property
					def     _a							(     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(**_snake_case					)
								return model



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






								"""simple docstring"""


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



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.dummy_text_encoder
								A__       =    self.dummy_tokenizer
								A__       =    self.dummy_unet
								A__       =    self.dummy_movq

								A__       =    {
								    'num_train_timesteps': 10_00,
								    '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,
								}

								A__       =    DDIMScheduler(**_snake_case					)

								A__       =    {
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}

								return components



					def     _a							(     self					:       int				,					_snake_case					:       Tuple				,					_snake_case					:       Dict=0					):






								"""simple docstring"""


								A__       =    floats_tensor((1, self.cross_attention_dim)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, self.cross_attention_dim)				,					rng=random.Random(seed + 1					)					).to(_snake_case					)
								# create init_image
								A__       =    floats_tensor((1, 3, 64, 64)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    image.cpu().permute(0				,					2				,					3				,					1					)[0]
								A__       =    Image.fromarray(np.uinta(_snake_case					)					).convert('RGB'					).resize((2_56, 2_56)					)

								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)
								A__       =    {
								    '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     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    'cpu'

								A__       =    self.get_dummy_components()

								A__       =    self.pipeline_class(**_snake_case					)
								A__       =    pipe.to(_snake_case					)

								pipe.set_progress_bar_config(disable=_snake_case					)

								A__       =    pipe(**self.get_dummy_inputs(_snake_case					)					)
								A__       =    output.images

								A__       =    pipe(
								    **self.get_dummy_inputs(_snake_case					)				,					return_dict=_snake_case				,					)[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(
								    [0.6147_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      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinsky/kandinsky_img2img_frog.npy'					)

								A__       =    load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
								A__       =    'A red cartoon frog, 4k'

								A__       =    KandinskyPriorPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-1-prior'				,					torch_dtype=torch.floataa					)
								pipe_prior.to(_snake_case					)

								A__       =    KandinskyImgaImgPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-1'				,					torch_dtype=torch.floataa					)
								A__       =    pipeline.to(_snake_case					)

								pipeline.set_progress_bar_config(disable=_snake_case					)

								A__       =    torch.Generator(device='cpu'					).manual_seed(0					)
								A__     , A__       =    pipe_prior(
								    _snake_case				,					generator=_snake_case				,					num_inference_steps=5				,					negative_prompt=''				,					).to_tuple()

								A__       =    pipeline(
								    _snake_case				,					image=_snake_case				,					image_embeds=_snake_case				,					negative_image_embeds=_snake_case				,					generator=_snake_case				,					num_inference_steps=1_00				,					height=7_68				,					width=7_68				,					strength=0.2				,					output_type='np'				,					)

								A__       =    output.images[0]

								assert image.shape == (7_68, 7_68, 3)

								assert_mean_pixel_difference(_snake_case				,					_snake_case					)





import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__       =      '''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE__       =      '''patrickvonplaten/t5-tiny-random'''


@require_torch
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								return AutoConfig.from_pretrained(_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=1					)
								self.assertEqual(student.config.num_hidden_layers				,					1					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=_snake_case					)
								self.assertEqual(student.config.encoder_layers				,					1					)
								self.assertEqual(student.config.decoder_layers				,					self.teacher_config.encoder_layers					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=1					)
								self.assertEqual(student.config.encoder_layers				,					1					)
								self.assertEqual(student.config.decoder_layers				,					1					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								with self.assertRaises(_snake_case					):
											create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=_snake_case				,					d=_snake_case					)




