


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class   lowercase      (				A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							CycleDiffusionPipeline
		_a       					=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
		    'negative_prompt',
		    'height',
		    'width',
		    'negative_prompt_embeds',
		}
		_a       					=							PipelineTesterMixin.required_optional_params - {'latents'}
		_a       					=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}   )
		_a       					=							IMAGE_TO_IMAGE_IMAGE_PARAMS
		_a       					=							IMAGE_TO_IMAGE_IMAGE_PARAMS




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :int				   =					UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=32       ,  )
							UpperCamelCase__  :Optional[Any]				   =					DDIMScheduler(
							    beta_start=0.00085       ,  beta_end=0.012       ,  beta_schedule='''scaled_linear'''       ,  num_train_timesteps=1000       ,  clip_sample=UpperCamelCase_       ,  set_alpha_to_one=UpperCamelCase_       ,  )
							torch.manual_seed(0				)
							UpperCamelCase__  :Dict				   =					AutoencoderKL(
							    block_out_channels=[32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,  latent_channels=4       ,  )
							torch.manual_seed(0				)
							UpperCamelCase__  :Dict				   =					CLIPTextConfig(
							    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
							UpperCamelCase__  :str				   =					CLIPTextModel(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

							UpperCamelCase__  :Optional[Any]				   =					{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					image / 2 + 0.5
							if str(UpperCamelCase_				).startswith('''mps'''				):
												UpperCamelCase__  :Union[str, Any]				   =					torch.manual_seed(UpperCamelCase_				)
							else:
												UpperCamelCase__  :Union[str, Any]				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :int				   =					{
							    '''prompt''': '''An astronaut riding an elephant''',
							    '''source_prompt''': '''An astronaut riding a horse''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''eta''': 0.1,
							    '''strength''': 0.8,
							    '''guidance_scale''': 3,
							    '''source_guidance_scale''': 1,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_components()
							UpperCamelCase__  :Tuple				   =					CycleDiffusionPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :str				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					pipe(**UpperCamelCase_				)
							UpperCamelCase__  :str				   =					output.images

							UpperCamelCase__  :str				   =					images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							UpperCamelCase__  :List[str]				   =					np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2




		@unittest.skipIf(torch_device != '''cuda'''       ,  '''This test requires a GPU'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					self.get_dummy_components()
							for name, module in components.items():
												if hasattr(UpperCamelCase_       ,  '''half'''				):
																	UpperCamelCase__  :int				   =					module.half()
							UpperCamelCase__  :List[str]				   =					CycleDiffusionPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Any				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					output.images

							UpperCamelCase__  :Optional[Any]				   =					images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							UpperCamelCase__  :List[Any]				   =					np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2




		@skip_mps
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return super().test_save_load_local()




		@unittest.skip('''non-deterministic pipeline'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return super().test_inference_batch_single_identical()




		@skip_mps
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return super().test_dict_tuple_outputs_equivalent()




		@skip_mps
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return super().test_save_load_optional_components()




		@skip_mps
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return super().test_attention_slicing_forward_pass()



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

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/cycle-diffusion/black_colored_car.png'''				)
							UpperCamelCase__  :List[str]				   =					load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
							UpperCamelCase__  :int				   =					init_image.resize((512, 512)				)

							UpperCamelCase__  :str				   =					'''CompVis/stable-diffusion-v1-4'''
							UpperCamelCase__  :int				   =					DDIMScheduler.from_pretrained(UpperCamelCase_       ,  subfolder='''scheduler'''				)
							UpperCamelCase__  :Optional[int]				   =					CycleDiffusionPipeline.from_pretrained(
							    UpperCamelCase_       ,  scheduler=UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa       ,  revision='''fp16'''				)

							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Union[str, Any]				   =					'''A black colored car'''
							UpperCamelCase__  :Dict				   =					'''A blue colored car'''

							UpperCamelCase__  :int				   =					torch.manual_seed(0				)
							UpperCamelCase__  :Tuple				   =					pipe(
							    prompt=UpperCamelCase_       ,  source_prompt=UpperCamelCase_       ,  image=UpperCamelCase_       ,  num_inference_steps=100       ,  eta=0.1       ,  strength=0.85       ,  guidance_scale=3       ,  source_guidance_scale=1       ,  generator=UpperCamelCase_       ,  output_type='''np'''       ,  )
							UpperCamelCase__  :Optional[int]				   =					output.images

							# the values aren't exactly equal, but the images look the same visually
							assert np.abs(image - expected_image				).max() < 5e-1




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/cycle-diffusion/black_colored_car.png'''				)
							UpperCamelCase__  :Dict				   =					load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
							UpperCamelCase__  :Optional[Any]				   =					init_image.resize((512, 512)				)

							UpperCamelCase__  :Any				   =					'''CompVis/stable-diffusion-v1-4'''
							UpperCamelCase__  :str				   =					DDIMScheduler.from_pretrained(UpperCamelCase_       ,  subfolder='''scheduler'''				)
							UpperCamelCase__  :Union[str, Any]				   =					CycleDiffusionPipeline.from_pretrained(UpperCamelCase_       ,  scheduler=UpperCamelCase_       ,  safety_checker=UpperCamelCase_				)

							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Any				   =					'''A black colored car'''
							UpperCamelCase__  :Dict				   =					'''A blue colored car'''

							UpperCamelCase__  :str				   =					torch.manual_seed(0				)
							UpperCamelCase__  :Optional[int]				   =					pipe(
							    prompt=UpperCamelCase_       ,  source_prompt=UpperCamelCase_       ,  image=UpperCamelCase_       ,  num_inference_steps=100       ,  eta=0.1       ,  strength=0.85       ,  guidance_scale=3       ,  source_guidance_scale=1       ,  generator=UpperCamelCase_       ,  output_type='''np'''       ,  )
							UpperCamelCase__  :int				   =					output.images

							assert np.abs(image - expected_image				).max() < 2e-2



'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Union[str, Any]				   =					torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor(
							    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Any				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-large'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Any				   =					torch.Size((1, 12, 1024)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :List[Any]				   =					torch.tensor(
							    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)