




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase		:				Optional[Any]       			=		{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[int]       			=		[
		    'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'XCLIPModel',
		    'XCLIPPreTrainedModel',
		    'XCLIPTextModel',
		    'XCLIPVisionModel',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		__lowerCAmelCase		:				str       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











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 		UpperCamelCase	(   snake_case__       :    Tuple	)	->     List[str]:
   UpperCamelCase    :  Optional[Any]							=	[tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]	)



class 				lowerCAmelCase_		(  a__       ,    a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					str        =  StableDiffusionLatentUpscalePipeline
       UpperCAmelCase__				:					Optional[Any]        =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       UpperCAmelCase__				:					str        =  PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       UpperCAmelCase__				:					Any        =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       UpperCAmelCase__				:					List[str]        =  frozenset(
           []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCAmelCase__				:					Union[str, Any]        =  frozenset([]							)

       UpperCAmelCase__				:					Optional[int]        =  True
       @property
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  List[Any]							=	1
          UpperCamelCase    :  List[str]							=	4
          UpperCamelCase    :  List[str]							=	(16, 16)

          UpperCamelCase    :  Optional[int]							=	floats_tensor((batch_size, num_channels) + sizes,			rng=random.Random(0       )       ).to(SCREAMING_SNAKE_CASE_       )
          return image
       def 					snake_case_       (			self       )     ->		int:
          torch.manual_seed(0       )
          UpperCamelCase    :  int							=	UNetaDConditionModel(
              act_fn='gelu',			attention_head_dim=8,			norm_num_groups=SCREAMING_SNAKE_CASE_,			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=SCREAMING_SNAKE_CASE_,			only_cross_attention=SCREAMING_SNAKE_CASE_,			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    :  Tuple							=	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    :  Tuple							=	EulerDiscreteScheduler(prediction_type='sample'       )
          UpperCamelCase    :  Optional[int]							=	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    :  Any							=	CLIPTextModel(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

          UpperCamelCase    :  Optional[int]							=	{
              'unet': model.eval(),
              'vae': vae.eval(),
              'scheduler': scheduler,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
          }

          return components
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=0       )     ->		List[str]:
          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             UpperCamelCase    :  List[str]							=	torch.manual_seed(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  Optional[Any]							=	torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	{
              '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 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  str							=	'cpu'

          UpperCamelCase    :  Tuple							=	self.get_dummy_components()
          UpperCamelCase    :  Dict							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
          pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	pipe(**SCREAMING_SNAKE_CASE_       ).images
          UpperCamelCase    :  str							=	image[0, -3:, -3:, -1]

          self.assertEqual(image.shape,			(1, 256, 256, 3)       )
          UpperCamelCase    :  List[Any]							=	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    :  Dict							=	np.abs(image_slice.flatten() - expected_slice       ).max()
          self.assertLessEqual(SCREAMING_SNAKE_CASE_,			1e-3       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=7e-3       )
       def 					snake_case_       (			self       )     ->		Tuple:
          super().test_cpu_offload_forward_pass(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		Dict:
          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )
       def 					snake_case_       (			self       )     ->		Any:
          super().test_inference_batch_single_identical(expected_max_diff=7e-3       )
       def 					snake_case_       (			self       )     ->		List[str]:
          super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          super().test_save_load_local(expected_max_difference=3e-3       )
       def 					snake_case_       (			self       )     ->		int:
          super().test_save_load_optional_components(expected_max_difference=3e-3       )






       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Optional[Any]							=	[
              'DDIMScheduler',
              'DDPMScheduler',
              'PNDMScheduler',
              'HeunDiscreteScheduler',
              'EulerAncestralDiscreteScheduler',
              'KDPM2DiscreteScheduler',
              'KDPM2AncestralDiscreteScheduler',
              'DPMSolverSDEScheduler',
          ]
          UpperCamelCase    :  Tuple							=	self.get_dummy_components()
          UpperCamelCase    :  Tuple							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )

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

          pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  int							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	2

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

             UpperCamelCase    :  List[Any]							=	getattr(SCREAMING_SNAKE_CASE_,			scheduler_enum.name       )
             UpperCamelCase    :  List[str]							=	scheduler_cls.from_config(pipe.scheduler.config       )
             UpperCamelCase    :  List[str]							=	pipe(**SCREAMING_SNAKE_CASE_       )[0]
             outputs.append(SCREAMING_SNAKE_CASE_       )

          assert check_same_shape(SCREAMING_SNAKE_CASE_       )






@require_torch_gpu
@slow
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  Dict							=	torch.manual_seed(33       )

          UpperCamelCase    :  Union[str, Any]							=	StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4',			torch_dtype=torch.floataa       )
          pipe.to('cuda'       )

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

          UpperCamelCase    :  Union[str, Any]							=	'a photo of an astronaut high resolution, unreal engine, ultra realistic'

          UpperCamelCase    :  int							=	pipe(SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_,			output_type='latent'       ).images

          UpperCamelCase    :  List[str]							=	upscaler(
              prompt=SCREAMING_SNAKE_CASE_,			image=SCREAMING_SNAKE_CASE_,			num_inference_steps=20,			guidance_scale=0,			generator=SCREAMING_SNAKE_CASE_,			output_type='np',			).images[0]

          UpperCamelCase    :  Optional[int]							=	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 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	torch.manual_seed(33       )

          UpperCamelCase    :  Union[str, Any]							=	StableDiffusionLatentUpscalePipeline.from_pretrained(
              'stabilityai/sd-x2-latent-upscaler',			torch_dtype=torch.floataa       )
          upscaler.to('cuda'       )

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

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

          UpperCamelCase    :  str							=	upscaler(
              prompt=SCREAMING_SNAKE_CASE_,			image=SCREAMING_SNAKE_CASE_,			num_inference_steps=20,			guidance_scale=0,			generator=SCREAMING_SNAKE_CASE_,			output_type='np',			).images[0]

          UpperCamelCase    :  Optional[Any]							=	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



