






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[tensor.shape for tensor in tensor_list]
						return all(shape == shapes[0] for shape in shapes[1:]	)





class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      StableDiffusionLatentUpscalePipeline
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    "height",
	    "width",
	    "cross_attention_kwargs",
	    "negative_prompt_embeds",
	    "prompt_embeds",
	}
	UpperCamelCase__								=      PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	UpperCamelCase__								=      frozenset(
	    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	UpperCamelCase__								=      frozenset([])

	UpperCamelCase__								=      True

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			1
							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			(16, 16)

							_UpperCAmelCase									=			floats_tensor((batch_size, num_channels) + sizes       ,    rng=random.Random(0    )    ).to(UpperCAmelCase    )
							return image

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							torch.manual_seed(0    )
							_UpperCAmelCase									=			UNetaDConditionModel(
							    act_fn='gelu'       ,    attention_head_dim=8       ,    norm_num_groups=UpperCAmelCase       ,    block_out_channels=[32, 32, 64, 64]       ,    time_cond_proj_dim=160       ,    conv_in_kernel=1       ,    conv_out_kernel=1       ,    cross_attention_dim=32       ,    down_block_types=(
							        'KDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							    )       ,    in_channels=8       ,    mid_block_type=UpperCAmelCase       ,    only_cross_attention=UpperCAmelCase       ,    out_channels=5       ,    resnet_time_scale_shift='scale_shift'       ,    time_embedding_type='fourier'       ,    timestep_post_act='gelu'       ,    up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')       ,    )
							_UpperCAmelCase									=			AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=[
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							    ]       ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']       ,    latent_channels=4       ,    )
							_UpperCAmelCase									=			EulerDiscreteScheduler(prediction_type='sample'    )
							_UpperCAmelCase									=			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       ,    hidden_act='quick_gelu'       ,    projection_dim=512       ,    )
							_UpperCAmelCase									=			CLIPTextModel(UpperCAmelCase    )
							_UpperCAmelCase									=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

							_UpperCAmelCase									=			{
							    'unet': model.eval(),
							    'vae': vae.eval(),
							    'scheduler': scheduler,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							}

							return components

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': self.dummy_image.cpu(),
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'

							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )
							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe(**UpperCAmelCase    ).images
							_UpperCAmelCase									=			image[0, -3:, -3:, -1]

							self.assertEqual(image.shape       ,    (1, 256, 256, 3)    )
							_UpperCAmelCase									=			np.array(
							    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]    )
							_UpperCAmelCase									=			np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(UpperCAmelCase       ,    1e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_attention_slicing_forward_pass(expected_max_diff=7e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_cpu_offload_forward_pass(expected_max_diff=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_inference_batch_single_identical(expected_max_diff=7e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_save_load_local(expected_max_difference=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_save_load_optional_components(expected_max_difference=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[
							    'DDIMScheduler',
							    'DDPMScheduler',
							    'PNDMScheduler',
							    'HeunDiscreteScheduler',
							    'EulerAncestralDiscreteScheduler',
							    'KDPM2DiscreteScheduler',
							    'KDPM2AncestralDiscreteScheduler',
							    'DPMSolverSDEScheduler',
							]
							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase    )

							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )
							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			2

							_UpperCAmelCase									=			[]
							for scheduler_enum in KarrasDiffusionSchedulers:
													if scheduler_enum.name in skip_schedulers:
																			# no sigma schedulers are not supported
																			# no schedulers
																			continue

													_UpperCAmelCase									=			getattr(UpperCAmelCase       ,    scheduler_enum.name    )
													_UpperCAmelCase									=			scheduler_cls.from_config(pipe.scheduler.config    )
													_UpperCAmelCase									=			pipe(**UpperCAmelCase    )[0]
													outputs.append(UpperCAmelCase    )

							assert check_same_shape(UpperCAmelCase    )





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





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.manual_seed(33    )

							_UpperCAmelCase									=			StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       ,    torch_dtype=torch.floataa    )
							pipe.to('cuda'    )

							_UpperCAmelCase									=			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'       ,    torch_dtype=torch.floataa    )
							upscaler.to('cuda'    )

							_UpperCAmelCase									=			'a photo of an astronaut high resolution, unreal engine, ultra realistic'

							_UpperCAmelCase									=			pipe(UpperCAmelCase       ,    generator=UpperCAmelCase       ,    output_type='latent'    ).images

							_UpperCAmelCase									=			upscaler(
							    prompt=UpperCAmelCase       ,    image=UpperCAmelCase       ,    num_inference_steps=20       ,    guidance_scale=0       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    ).images[0]

							_UpperCAmelCase									=			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'    )
							assert np.abs((expected_image - image).mean()    ) < 5e-2

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.manual_seed(33    )

							_UpperCAmelCase									=			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'       ,    torch_dtype=torch.floataa    )
							upscaler.to('cuda'    )

							_UpperCAmelCase									=			'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'    )

							_UpperCAmelCase									=			upscaler(
							    prompt=UpperCAmelCase       ,    image=UpperCAmelCase       ,    num_inference_steps=20       ,    guidance_scale=0       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    ).images[0]

							_UpperCAmelCase									=			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'    )
							assert np.abs((expected_image - image).max()    ) < 5e-2







"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      str							=  '''beit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=81_92		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=[3, 5, 7, 11]		,SCREAMING_SNAKE_CASE__=[1, 2, 3, 6]		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0.4		,SCREAMING_SNAKE_CASE__=2_56		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2_55		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Any						=  vocab_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_size
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :str						=  layer_norm_eps

             __SCREAMING_SNAKE_CASE   :int						=  image_size
             __SCREAMING_SNAKE_CASE   :Tuple						=  patch_size
             __SCREAMING_SNAKE_CASE   :Any						=  num_channels
             __SCREAMING_SNAKE_CASE   :Any						=  use_mask_token
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  use_absolute_position_embeddings
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  use_relative_position_bias
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  use_shared_relative_position_bias
             __SCREAMING_SNAKE_CASE   :List[str]						=  layer_scale_init_value
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  drop_path_rate
             __SCREAMING_SNAKE_CASE   :str						=  use_mean_pooling
             # decode head attributes (semantic segmentation)
             __SCREAMING_SNAKE_CASE   :Dict						=  out_indices
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pool_scales
             # auxiliary head attributes (semantic segmentation)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  use_auxiliary_head
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  auxiliary_loss_weight
             __SCREAMING_SNAKE_CASE   :Dict						=  auxiliary_channels
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  auxiliary_num_convs
             __SCREAMING_SNAKE_CASE   :List[str]						=  auxiliary_concat_input
             __SCREAMING_SNAKE_CASE   :List[Any]						=  semantic_loss_ignore_index






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  version.parse('''1.11'''  )




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             return OrderedDict(
                 [
                     ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ]							)




       @property
       def     _UpperCamelCase     (						self							)			->      float:





             """simple docstring"""






             return 1E-4