
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     _UpperCamelCase			(   _A       ,    _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	CycleDiffusionPipeline
			__UpperCamelCase :  List[Any]    =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    """negative_prompt""",
			    """height""",
			    """width""",
			    """negative_prompt_embeds""",
			}
			__UpperCamelCase :  List[str]    =	PipelineTesterMixin.required_optional_params - {"""latents"""}
			__UpperCamelCase :  List[str]    =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}					)
			__UpperCamelCase :  Optional[int]    =	IMAGE_TO_IMAGE_IMAGE_PARAMS
			__UpperCamelCase :  Tuple    =	IMAGE_TO_IMAGE_IMAGE_PARAMS

			def  lowerCAmelCase__		(    self	:    int ):
							torch.manual_seed(0 )
							UpperCamelCase_:							List[str]		=  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[int]		=  DDIMScheduler(
							    beta_start=0.0_0085  ,		beta_end=0.012  ,		beta_schedule="""scaled_linear"""  ,		num_train_timesteps=1000  ,		clip_sample=snake_case_  ,		set_alpha_to_one=snake_case_  ,		)
							torch.manual_seed(0 )
							UpperCamelCase_:							Tuple		=  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_:							List[Any]		=  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_:							int		=  CLIPTextModel(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

							UpperCamelCase_:							Optional[int]		=  {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Tuple=0 ):
							UpperCamelCase_:							List[Any]		=  floats_tensor((1, 3, 32, 32)  ,		rng=random.Random(snake_case_ ) ).to(snake_case_ )
							UpperCamelCase_:							Optional[int]		=  image / 2 + 0.5
							if str(snake_case_ ).startswith("""mps""" ):
											UpperCamelCase_:							List[Any]		=  torch.manual_seed(snake_case_ )
							else:
											UpperCamelCase_:							int		=  torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
							UpperCamelCase_:							Dict		=  {
							    """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	:    int ):
							UpperCamelCase_:							Any		=  """cpu"""  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase_:							Any		=  self.get_dummy_components()
							UpperCamelCase_:							List[Any]		=  CycleDiffusionPipeline(**snake_case_ )
							UpperCamelCase_:							int		=  pipe.to(snake_case_ )
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							Dict		=  self.get_dummy_inputs(snake_case_ )
							UpperCamelCase_:							List[str]		=  pipe(**snake_case_ )
							UpperCamelCase_:							Optional[int]		=  output.images

							UpperCamelCase_:							List[str]		=  images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							UpperCamelCase_:							int		=  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	:    Any ):
							UpperCamelCase_:							List[Any]		=  self.get_dummy_components()
							for name, module in components.items():
											if hasattr(snake_case_  ,		"""half""" ):
															UpperCamelCase_:							Union[str, Any]		=  module.half()
							UpperCamelCase_:							List[str]		=  CycleDiffusionPipeline(**snake_case_ )
							UpperCamelCase_:							Optional[int]		=  pipe.to(snake_case_ )
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							Any		=  self.get_dummy_inputs(snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  pipe(**snake_case_ )
							UpperCamelCase_:							int		=  output.images

							UpperCamelCase_:							Tuple		=  images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							UpperCamelCase_:							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	:    Union[str, Any] ):
							return super().test_save_load_local()

			@unittest.skip("""non-deterministic pipeline""" )
			def  lowerCAmelCase__		(    self	:    Any ):
							return super().test_inference_batch_single_identical()

			@skip_mps
			def  lowerCAmelCase__		(    self	:    Any ):
							return super().test_dict_tuple_outputs_equivalent()

			@skip_mps
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							return super().test_save_load_optional_components()




			@skip_mps
			def  lowerCAmelCase__		(    self	:    str ):
							return super().test_attention_slicing_forward_pass()




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






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Optional[int]		=  load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/cycle-diffusion/black_colored_car.png""" )
							UpperCamelCase_:							Union[str, Any]		=  load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy""" )
							UpperCamelCase_:							Optional[Any]		=  init_image.resize((512, 512) )

							UpperCamelCase_:							Dict		=  """CompVis/stable-diffusion-v1-4"""
							UpperCamelCase_:							Tuple		=  DDIMScheduler.from_pretrained(snake_case_  ,		subfolder="""scheduler""" )
							UpperCamelCase_:							Optional[Any]		=  CycleDiffusionPipeline.from_pretrained(
							    snake_case_  ,		scheduler=snake_case_  ,		safety_checker=snake_case_  ,		torch_dtype=torch.floataa  ,		revision="""fp16""" )

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

							UpperCamelCase_:							str		=  """A black colored car"""
							UpperCamelCase_:							Optional[Any]		=  """A blue colored car"""

							UpperCamelCase_:							List[str]		=  torch.manual_seed(0 )
							UpperCamelCase_:							int		=  pipe(
							    prompt=snake_case_  ,		source_prompt=snake_case_  ,		image=snake_case_  ,		num_inference_steps=100  ,		eta=0.1  ,		strength=0.85  ,		guidance_scale=3  ,		source_guidance_scale=1  ,		generator=snake_case_  ,		output_type="""np"""  ,		)
							UpperCamelCase_:							str		=  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	:    int ):
							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_:							Tuple		=  init_image.resize((512, 512) )

							UpperCamelCase_:							str		=  """CompVis/stable-diffusion-v1-4"""
							UpperCamelCase_:							List[str]		=  DDIMScheduler.from_pretrained(snake_case_  ,		subfolder="""scheduler""" )
							UpperCamelCase_:							int		=  CycleDiffusionPipeline.from_pretrained(snake_case_  ,		scheduler=snake_case_  ,		safety_checker=snake_case_ )

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

							UpperCamelCase_:							int		=  """A black colored car"""
							UpperCamelCase_:							Optional[Any]		=  """A blue colored car"""

							UpperCamelCase_:							Union[str, Any]		=  torch.manual_seed(0 )
							UpperCamelCase_:							List[str]		=  pipe(
							    prompt=snake_case_  ,		source_prompt=snake_case_  ,		image=snake_case_  ,		num_inference_steps=100  ,		eta=0.1  ,		strength=0.85  ,		guidance_scale=3  ,		source_guidance_scale=1  ,		generator=snake_case_  ,		output_type="""np"""  ,		)
							UpperCamelCase_:							Tuple		=  output.images

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








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ImageGPTImageProcessor

class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Tuple=7  ,		snake_case_	:    Optional[Any]=3  ,		snake_case_	:    Dict=18  ,		snake_case_	:    Dict=30  ,		snake_case_	:    Union[str, Any]=400  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    Any=None  ,		snake_case_	:    List[str]=True  ,		):
							UpperCamelCase_:							Dict		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Union[str, Any]		=  parent
							UpperCamelCase_:							Tuple		=  batch_size
							UpperCamelCase_:							List[str]		=  num_channels
							UpperCamelCase_:							Optional[int]		=  image_size
							UpperCamelCase_:							Dict		=  min_resolution
							UpperCamelCase_:							Optional[int]		=  max_resolution
							UpperCamelCase_:							str		=  do_resize
							UpperCamelCase_:							Tuple		=  size
							UpperCamelCase_:							Dict		=  do_normalize




			def  lowerCAmelCase__		(    self	:    str ):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
							            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
							        ] ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}




@require_torch
@require_vision
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	ImageGPTImageProcessor if is_vision_available() else None

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  ImageGPTImageProcessingTester(self )

			@property
			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case_  ,		"""clusters""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""size""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize""" ) )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18} )

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42 )
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42} )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict )
							UpperCamelCase_:							Optional[int]		=  json.loads(image_processor.to_json_string() )
							for key, value in self.image_processor_dict.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		obj[key] ) )
											else:
															self.assertEqual(obj[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							int		=  os.path.join(snake_case_  ,		"""image_processor.json""" )
											image_processor_first.to_json_file(snake_case_ )
											UpperCamelCase_:							Any		=  self.image_processing_class.from_json_file(snake_case_ ).to_dict()

							UpperCamelCase_:							str		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(snake_case_ )
											UpperCamelCase_:							Optional[int]		=  self.image_processing_class.from_pretrained(snake_case_ ).to_dict()

							UpperCamelCase_:							Union[str, Any]		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )




			@unittest.skip("""ImageGPT requires clusters at initialization""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass







def     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,      split="""test"""  )

				UpperCamelCase_:							Tuple		=  Image.open(dataset[4]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[5]["""file"""]  )

				UpperCamelCase_:							List[str]		=  [imagea, imagea]

				return images





@require_vision
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )

							UpperCamelCase_:							List[str]		=  prepare_images()

							# test non-batched
							UpperCamelCase_:							List[str]		=  image_processing(images[0]  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(1, 1024) )

							UpperCamelCase_:							Union[str, Any]		=  [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,		snake_case_ )

							# test batched
							UpperCamelCase_:							Optional[int]		=  image_processing(snake_case_  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(2, 1024) )

							UpperCamelCase_:							str		=  [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,		snake_case_ )






