


'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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 						a__					(					a_      ,  a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				List[str]					      =			StableDiffusionXLImgaImgPipeline
			A						:				str					      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
			A						:				Optional[Any]					      =			PipelineTesterMixin.required_optional_params - {'''latents'''}
			A						:				Dict					      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			A						:				Optional[int]					      =			IMAGE_TO_IMAGE_IMAGE_PARAMS
			A						:				str					      =			IMAGE_TO_IMAGE_IMAGE_PARAMS
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Dict:
						torch.manual_seed(0	)
						__A=	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')			,    attention_head_dim=(2, 4)			,    use_linear_projection=lowerCAmelCase_			,    addition_embed_type='text_time'			,    addition_time_embed_dim=8			,    transformer_layers_per_block=(1, 2)			,    projection_class_embeddings_input_dim=80			,    cross_attention_dim=64			,    )
						__A=	EulerDiscreteScheduler(
						    beta_start=0.0_00_85			,    beta_end=0.0_12			,    steps_offset=1			,    beta_schedule='scaled_linear'			,    timestep_spacing='leading'			,    )
						torch.manual_seed(0	)
						__A=	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			,    sample_size=128			,    )
						torch.manual_seed(0	)
						__A=	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=1_000			,    hidden_act='gelu'			,    projection_dim=32			,    )
						__A=	CLIPTextModel(lowerCAmelCase_	)
						__A=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,    local_files_only=lowerCAmelCase_	)

						__A=	CLIPTextModelWithProjection(lowerCAmelCase_	)
						__A=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,    local_files_only=lowerCAmelCase_	)

						__A=	{
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'text_encoder_2': text_encoder_a,
						    'tokenizer_2': tokenizer_a,
						    # "safety_checker": None,
						    # "feature_extractor": None,
						}
						return components
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Tuple=0	)      ->		Optional[Any]:
						__A=	floats_tensor((1, 3, 32, 32)			,    rng=random.Random(lowerCAmelCase_	)	).to(lowerCAmelCase_	)
						__A=	image / 2 + 0.5
						if str(lowerCAmelCase_	).startswith('mps'	):
									__A=	torch.manual_seed(lowerCAmelCase_	)
						else:
									__A=	torch.Generator(device=lowerCAmelCase_	).manual_seed(lowerCAmelCase_	)
						__A=	{
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 5.0,
						    'output_type': 'numpy',
						    'strength': 0.75,
						}
						return inputs
			def 					lowerCAmelCase     (  self							:			str	)      ->		Dict:
						__A=	'cpu'  # ensure determinism for the device-dependent torch.Generator
						__A=	self.get_dummy_components()
						__A=	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_	)
						__A=	sd_pipe.to(lowerCAmelCase_	)
						sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs(lowerCAmelCase_	)
						__A=	sd_pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)

						__A=	np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		str:
						super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[str]:
						super().test_inference_batch_single_identical(expected_max_diff=3E-3	)
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Dict:
						pass

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						__A=	self.get_dummy_components()
						__A=	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_	)
						__A=	sd_pipe.to(lowerCAmelCase_	)
						__A=	sd_pipe.to(lowerCAmelCase_	)
						sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						# forward without prompt embeds
						__A=	self.get_dummy_inputs(lowerCAmelCase_	)
						__A=	3 * ['this is a negative prompt']
						__A=	negative_prompt
						__A=	3 * [inputs['prompt']]

						__A=	sd_pipe(**lowerCAmelCase_	)
						__A=	output.images[0, -3:, -3:, -1]

						# forward with prompt embeds
						__A=	self.get_dummy_inputs(lowerCAmelCase_	)
						__A=	3 * ['this is a negative prompt']
						__A=	3 * [inputs.pop('prompt'	)]

						(
						    (
						    __A
						),	(
						    __A
						),	(
						    __A
						),	(
						    __A
						),	
						)=	sd_pipe.encode_prompt(lowerCAmelCase_			,    negative_prompt=lowerCAmelCase_	)

						__A=	sd_pipe(
						    **lowerCAmelCase_			,    prompt_embeds=lowerCAmelCase_			,    negative_prompt_embeds=lowerCAmelCase_			,    pooled_prompt_embeds=lowerCAmelCase_			,    negative_pooled_prompt_embeds=lowerCAmelCase_			,    )
						__A=	output.images[0, -3:, -3:, -1]

						# make sure that it's equal
						assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4


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





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Any:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[Any]="cpu"			,    lowerCAmelCase_							:			List[Any]=torch.floataa			,    lowerCAmelCase_							:			List[Any]=0	)      ->		str:
						__A=	torch.Generator(device=lowerCAmelCase_	).manual_seed(lowerCAmelCase_	)
						__A=	np.random.RandomState(lowerCAmelCase_	).standard_normal((1, 4, 64, 64)	)
						__A=	torch.from_numpy(lowerCAmelCase_	).to(device=lowerCAmelCase_			,    dtype=lowerCAmelCase_	)
						__A=	{
						    'prompt': 'a photograph of an astronaut riding a horse',
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						}
						return inputs

			def 					lowerCAmelCase     (  self							:			int	)      ->		Tuple:
						__A=	DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'	)
						pipe.to(lowerCAmelCase_	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_inputs(lowerCAmelCase_	)
						__A=	pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						__A=	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]	)
						assert np.abs(image_slice - expected_slice	).max() < 7E-3







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__  			=				{
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TrOCRForCausalLM''',
					    '''TrOCRPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



