'''simple docstring'''

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



class 			UpperCAmelCase  (						A__    ,						A__    ,						A__    ,						unittest.TestCase					):





       '''simple docstring'''



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

       SCREAMING_SNAKE_CASE							=    True

       @property
       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    Any  =					1
        lowercase__				:    Optional[int]  =					4
        lowercase__				:    Optional[int]  =					(16, 16)

        lowercase__				:    Dict  =					floats_tensor((batch_size, num_channels) + sizes	,   rng=random.Random(0 ) ).to(_A )
        return image

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        torch.manual_seed(0 )
        lowercase__				:    str  =					UNetaDConditionModel(
            act_fn='''gelu'''	,   attention_head_dim=8	,   norm_num_groups=_A	,   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=_A	,   only_cross_attention=_A	,   out_channels=5	,   resnet_time_scale_shift='''scale_shift'''	,   time_embedding_type='''fourier'''	,   timestep_post_act='''gelu'''	,   up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')	,   )
        lowercase__				:    Union[str, Any]  =					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	,   )
        lowercase__				:    int  =					EulerDiscreteScheduler(prediction_type='''sample''' )
        lowercase__				:    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	,   hidden_act='''quick_gelu'''	,   projection_dim=512	,   )
        lowercase__				:    Tuple  =					CLIPTextModel(_A )
        lowercase__				:    int  =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        lowercase__				:    Union[str, Any]  =					{
            "unet": model.eval(),
            "vae": vae.eval(),
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
        }

        return components

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  Optional[Any]:






        if str(_A ).startswith('''mps''' ):
         lowercase__				:    Optional[int]  =					torch.manual_seed(_A )
        else:
         lowercase__				:    Dict  =					torch.Generator(device=_A ).manual_seed(_A )
        lowercase__				:    Tuple  =					{
            "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 _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    List[Any]  =					"cpu"

        lowercase__				:    List[str]  =					self.get_dummy_components()
        lowercase__				:    List[str]  =					self.pipeline_class(**_A )
        pipe.to(_A )
        pipe.set_progress_bar_config(disable=_A )

        lowercase__				:    Tuple  =					self.get_dummy_inputs(_A )
        lowercase__				:    Dict  =					pipe(**_A ).images
        lowercase__				:    Optional[int]  =					image[0, -3:, -3:, -1]

        self.assertEqual(image.shape	,   (1, 256, 256, 3) )
        lowercase__				:    Optional[Any]  =					np.array(
            [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5] )
        lowercase__				:    Optional[int]  =					np.abs(image_slice.flatten() - expected_slice ).max()
        self.assertLessEqual(_A	,   1E-3 )

       def _lowerCAmelCase(						self )  ->  List[Any]:
        super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )

       def _lowerCAmelCase(						self )  ->  int:
        super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )

       def _lowerCAmelCase(						self )  ->  str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )

       def _lowerCAmelCase(						self )  ->  str:
        super().test_inference_batch_single_identical(expected_max_diff=7E-3 )

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )

       def _lowerCAmelCase(						self )  ->  List[str]:
        super().test_save_load_local(expected_max_difference=3E-3 )

       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        super().test_save_load_optional_components(expected_max_difference=3E-3 )


       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Dict  =					[
            "DDIMScheduler",
            "DDPMScheduler",
            "PNDMScheduler",
            "HeunDiscreteScheduler",
            "EulerAncestralDiscreteScheduler",
            "KDPM2DiscreteScheduler",
            "KDPM2AncestralDiscreteScheduler",
            "DPMSolverSDEScheduler",
        ]
        lowercase__				:    Union[str, Any]  =					self.get_dummy_components()
        lowercase__				:    Any  =					self.pipeline_class(**_A )

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

        pipe.to(_A )
        pipe.set_progress_bar_config(disable=_A )
        lowercase__				:    Any  =					self.get_dummy_inputs(_A )
        lowercase__				:    Optional[Any]  =					2

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

         lowercase__				:    Tuple  =					getattr(_A	,   scheduler_enum.name )
         lowercase__				:    Union[str, Any]  =					scheduler_cls.from_config(pipe.scheduler.config )
         lowercase__				:    Optional[int]  =					pipe(**_A )[0]
         outputs.append(_A )

        assert check_same_shape(_A )



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





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    int  =					torch.manual_seed(33 )

        lowercase__				:    Optional[int]  =					StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''	,   torch_dtype=torch.floataa )
        pipe.to('''cuda''' )

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

        lowercase__				:    Tuple  =					"a photo of an astronaut high resolution, unreal engine, ultra realistic"

        lowercase__				:    str  =					pipe(_A	,   generator=_A	,   output_type='''latent''' ).images

        lowercase__				:    Optional[Any]  =					upscaler(
            prompt=_A	,   image=_A	,   num_inference_steps=20	,   guidance_scale=0	,   generator=_A	,   output_type='''np'''	,   ).images[0]

        lowercase__				:    str  =					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 _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					torch.manual_seed(33 )

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

        lowercase__				:    Any  =					"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

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

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

        lowercase__				:    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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase					:	Union[str, Any]       =  {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  ['''ChineseCLIPFeatureExtractor''']
		__lowerCamelCase					:	Optional[int]       =  ['''ChineseCLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	int       =  [
		    '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ChineseCLIPModel''',
		    '''ChineseCLIPPreTrainedModel''',
		    '''ChineseCLIPTextModel''',
		    '''ChineseCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ChineseCLIPConfig,
		    ChineseCLIPOnnxConfig,
		    ChineseCLIPTextConfig,
		    ChineseCLIPVisionConfig,
		)
		from .processing_chinese_clip import ChineseCLIPProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ChineseCLIPModel,
				    ChineseCLIPPreTrainedModel,
				    ChineseCLIPTextModel,
				    ChineseCLIPVisionModel,
				)

else:
		import sys

		__lowerCamelCase					:	Any       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
