

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							StableDiffusionPanoramaPipeline
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS


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





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   UNetaDConditionModel(
            block_out_channels=(3_2, 6_4),layers_per_block=1,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),cross_attention_dim=3_2,)
        A__          =   DDIMScheduler()
        torch.manual_seed(0		)
        A__          =   AutoencoderKL(
            block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,)
        torch.manual_seed(0		)
        A__          =   CLIPTextConfig(
            bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,)
        A__          =   CLIPTextModel(lowercase_		)
        A__          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

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


    def 						snake_case__     (      self :       str,lowercase_ :       Tuple,lowercase_ :       Optional[int]=0		)->    int:





        '''simple docstring'''







        A__          =   torch.manual_seed(lowercase_		)
        A__          =   {
            'prompt': 'a photo of the dolomites',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs


    def 						snake_case__     (      self :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        A__          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        A__          =   self.get_dummy_components()
        A__          =   StableDiffusionPanoramaPipeline(**lowercase_		)
        A__          =   sd_pipe.to(lowercase_		)
        sd_pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   sd_pipe(**lowercase_		).images
        A__          =   image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        A__          =   np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757]		)

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


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





        '''simple docstring'''







        super().test_inference_batch_consistent(batch_sizes=[1, 2]		)


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





        '''simple docstring'''







        super().test_inference_batch_single_identical(batch_size=2,expected_max_diff=3.25E-3		)


    def 						snake_case__     (      self :       Optional[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        A__          =   self.get_dummy_components()
        A__          =   StableDiffusionPanoramaPipeline(**lowercase_		)
        A__          =   sd_pipe.to(lowercase_		)
        sd_pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   'french fries'
        A__          =   sd_pipe(**lowercase_,negative_prompt=lowercase_		)
        A__          =   output.images
        A__          =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        A__          =   np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]		)

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


    def 						snake_case__     (      self :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        A__          =   self.get_dummy_components()
        A__          =   StableDiffusionPanoramaPipeline(**lowercase_		)
        A__          =   sd_pipe.to(lowercase_		)
        sd_pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   sd_pipe(**lowercase_,view_batch_size=2		)
        A__          =   output.images
        A__          =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        A__          =   np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]		)

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


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        A__          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        A__          =   self.get_dummy_components()
        A__          =   EulerAncestralDiscreteScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear'		)
        A__          =   StableDiffusionPanoramaPipeline(**lowercase_		)
        A__          =   sd_pipe.to(lowercase_		)
        sd_pipe.set_progress_bar_config(disable=lowercase_		)

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

        assert image.shape == (1, 6_4, 6_4, 3)

        A__          =   np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952]		)

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


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





        '''simple docstring'''







        A__          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        A__          =   self.get_dummy_components()
        A__          =   PNDMScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear',skip_prk_steps=lowercase_		)
        A__          =   StableDiffusionPanoramaPipeline(**lowercase_		)
        A__          =   sd_pipe.to(lowercase_		)
        sd_pipe.set_progress_bar_config(disable=lowercase_		)

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

        assert image.shape == (1, 6_4, 6_4, 3)

        A__          =   np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539]		)

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




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







    """simple docstring"""





    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[Any],lowercase_ :       int=0		)->    Optional[int]:





        '''simple docstring'''







        A__          =   torch.manual_seed(lowercase_		)
        A__          =   {
            'prompt': 'a photo of the dolomites',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs


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





        '''simple docstring'''







        A__          =   'stabilityai/stable-diffusion-2-base'
        A__          =   DDIMScheduler.from_pretrained(lowercase_,subfolder='scheduler'		)
        A__          =   StableDiffusionPanoramaPipeline.from_pretrained(lowercase_,scheduler=lowercase_,safety_checker=lowercase_		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

        A__          =   np.array(
            [
                0.36_968_392,
                0.27_025_372,
                0.32_446_766,
                0.28_379_387,
                0.36_363_274,
                0.30_733_347,
                0.27_100_027,
                0.27_054_125,
                0.25_536_096,
            ]		)

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


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





        '''simple docstring'''







        A__          =   StableDiffusionPanoramaPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-base',safety_checker=lowercase_		)
        A__          =   LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

        A__          =   np.array(
            [
                [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                ]
            ]		)

        assert np.abs(expected_slice - image_slice		).max() < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        A__          =   0

        def callback_fn(lowercase_ :       int,lowercase_ :       int,lowercase_ :       torch.FloatTensor		) -> None:
            A__          =   True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
                A__          =   latents.detach().cpu().numpy()
                assert latents.shape == (1, 4, 6_4, 2_5_6)
                A__          =   latents[0, -3:, -3:, -1]

                A__          =   np.array(
                    [
                        0.18_681_869,
                        0.33_907_816,
                        0.5_361_276,
                        0.14_432_865,
                        -0.02_856_611,
                        -0.73_941_123,
                        0.23_397_987,
                        0.47_322_682,
                        -0.37_823_164,
                    ]		)
                assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2
            elif step == 2:
                A__          =   latents.detach().cpu().numpy()
                assert latents.shape == (1, 4, 6_4, 2_5_6)
                A__          =   latents[0, -3:, -3:, -1]

                A__          =   np.array(
                    [
                        0.18_539_645,
                        0.33_987_248,
                        0.5_378_559,
                        0.14_437_142,
                        -0.02_455_261,
                        -0.7_338_317,
                        0.23_990_755,
                        0.47_356_272,
                        -0.3_786_505,
                    ]		)

                assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2

        A__          =   False

        A__          =   'stabilityai/stable-diffusion-2-base'
        A__          =   DDIMScheduler.from_pretrained(lowercase_,subfolder='scheduler'		)
        A__          =   StableDiffusionPanoramaPipeline.from_pretrained(lowercase_,scheduler=lowercase_,safety_checker=lowercase_		)
        A__          =   pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)
        pipe.enable_attention_slicing()

        A__          =   self.get_inputs()
        pipe(**lowercase_,callback=lowercase_,callback_steps=1		)
        assert callback_fn.has_been_called
        assert number_of_steps == 3


    def 						snake_case__     (      self :       Dict		)->    List[str]:





        '''simple docstring'''







        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A__          =   'stabilityai/stable-diffusion-2-base'
        A__          =   DDIMScheduler.from_pretrained(lowercase_,subfolder='scheduler'		)
        A__          =   StableDiffusionPanoramaPipeline.from_pretrained(lowercase_,scheduler=lowercase_,safety_checker=lowercase_		)
        A__          =   pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)
        pipe.enable_attention_slicing(1		)
        pipe.enable_sequential_cpu_offload()

        A__          =   self.get_inputs()
        A__          =   pipe(**lowercase_		)

        A__          =   torch.cuda.max_memory_allocated()
        # make sure that less than 5.2 GB is allocated
        assert mem_bytes < 5.5 * 1_0**9







import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowerCamelCase				:		Union[str, Any]     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  int						,							*A_					:  str						,							**A_					:  Optional[int]  )   ->	None:

       """simple docstring"""






       warnings.warn(
           'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use GLPNImageProcessor instead.'						,							A_						,							)
       super().__init__(*A_						,							**A_  )



