




'''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      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Union[str, Any]										=      CycleDiffusionPipeline
       __lowercase	:   Dict										=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           '''negative_prompt''',
           '''height''',
           '''width''',
           '''negative_prompt_embeds''',
       }
       __lowercase	:   Union[str, Any]										=      PipelineTesterMixin.required_optional_params - {'''latents'''}
       __lowercase	:   List[Any]										=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''})
       __lowercase	:   Optional[Any]										=      IMAGE_TO_IMAGE_IMAGE_PARAMS
       __lowercase	:   Tuple										=      IMAGE_TO_IMAGE_IMAGE_PARAMS


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



            '''simple docstring'''




            torch.manual_seed(0	)
            __snake_case            =	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     ,	)
            __snake_case            =	DDIMScheduler(
                beta_start=0.00_085     ,	beta_end=0.012     ,	beta_schedule='''scaled_linear'''     ,	num_train_timesteps=1000     ,	clip_sample=__SCREAMING_SNAKE_CASE     ,	set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,	)
            torch.manual_seed(0	)
            __snake_case            =	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	)
            __snake_case            =	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     ,	)
            __snake_case            =	CLIPTextModel(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

            __snake_case            =	{
                '''unet''': unet,
                '''scheduler''': scheduler,
                '''vae''': vae,
                '''text_encoder''': text_encoder,
                '''tokenizer''': tokenizer,
                '''safety_checker''': None,
                '''feature_extractor''': None,
            }
            return components


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=0	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(__SCREAMING_SNAKE_CASE	)	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	image / 2 + 0.5
            if str(__SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
                 __snake_case            =	torch.manual_seed(__SCREAMING_SNAKE_CASE	)
            else:
                 __snake_case            =	torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''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	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

            __snake_case            =	self.get_dummy_components()
            __snake_case            =	CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	pipe.to(__SCREAMING_SNAKE_CASE	)
            pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	output.images

            __snake_case            =	images[0, -3:, -3:, -1]

            assert images.shape == (1, 32, 32, 3)
            __snake_case            =	np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

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


       @unittest.skipIf(torch_device != '''cuda'''     ,	'''This test requires a GPU'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.get_dummy_components()
            for name, module in components.items():
                 if hasattr(__SCREAMING_SNAKE_CASE     ,	'''half'''	):
                      __snake_case            =	module.half()
            __snake_case            =	CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	pipe.to(__SCREAMING_SNAKE_CASE	)
            pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	pipe(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	output.images

            __snake_case            =	images[0, -3:, -3:, -1]

            assert images.shape == (1, 32, 32, 3)
            __snake_case            =	np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

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


       @skip_mps
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            return super().test_save_load_local()


       @unittest.skip('''non-deterministic pipeline'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            return super().test_inference_batch_single_identical()


       @skip_mps
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            return super().test_dict_tuple_outputs_equivalent()


       @skip_mps
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            return super().test_save_load_optional_components()




       @skip_mps
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            return super().test_attention_slicing_forward_pass()





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


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



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/cycle-diffusion/black_colored_car.png'''	)
            __snake_case            =	load_numpy(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''	)
            __snake_case            =	init_image.resize((512, 512)	)

            __snake_case            =	'''CompVis/stable-diffusion-v1-4'''
            __snake_case            =	DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE     ,	subfolder='''scheduler'''	)
            __snake_case            =	CycleDiffusionPipeline.from_pretrained(
                __SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE     ,	safety_checker=__SCREAMING_SNAKE_CASE     ,	torch_dtype=torch.floataa     ,	revision='''fp16'''	)

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

            __snake_case            =	'''A black colored car'''
            __snake_case            =	'''A blue colored car'''

            __snake_case            =	torch.manual_seed(0	)
            __snake_case            =	pipe(
                prompt=__SCREAMING_SNAKE_CASE     ,	source_prompt=__SCREAMING_SNAKE_CASE     ,	image=__SCREAMING_SNAKE_CASE     ,	num_inference_steps=100     ,	eta=0.1     ,	strength=0.85     ,	guidance_scale=3     ,	source_guidance_scale=1     ,	generator=__SCREAMING_SNAKE_CASE     ,	output_type='''np'''     ,	)
            __snake_case            =	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	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/cycle-diffusion/black_colored_car.png'''	)
            __snake_case            =	load_numpy(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''	)
            __snake_case            =	init_image.resize((512, 512)	)

            __snake_case            =	'''CompVis/stable-diffusion-v1-4'''
            __snake_case            =	DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE     ,	subfolder='''scheduler'''	)
            __snake_case            =	CycleDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE     ,	safety_checker=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	'''A black colored car'''
            __snake_case            =	'''A blue colored car'''

            __snake_case            =	torch.manual_seed(0	)
            __snake_case            =	pipe(
                prompt=__SCREAMING_SNAKE_CASE     ,	source_prompt=__SCREAMING_SNAKE_CASE     ,	image=__SCREAMING_SNAKE_CASE     ,	num_inference_steps=100     ,	eta=0.1     ,	strength=0.85     ,	guidance_scale=3     ,	source_guidance_scale=1     ,	generator=__SCREAMING_SNAKE_CASE     ,	output_type='''np'''     ,	)
            __snake_case            =	output.images

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











'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_					:      Tuple    			=    {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      int    			=    ['LayoutLMv2TokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Union[str, Any]    			=    ['LayoutLMv2FeatureExtractor']
					snake_case_					:      str    			=    ['LayoutLMv2ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Optional[int]    			=    [
					    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMv2ForQuestionAnswering',
					    'LayoutLMv2ForSequenceClassification',
					    'LayoutLMv2ForTokenClassification',
					    'LayoutLMv2Layer',
					    'LayoutLMv2Model',
					    'LayoutLMv2PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaLayer,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)
else:
					import sys

					snake_case_					:      int    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


