



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 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			StableDiffusionPanoramaPipeline
   _UpperCAmelCase     	=			TEXT_TO_IMAGE_PARAMS
   _UpperCAmelCase     	=			TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCAmelCase     	=			TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCAmelCase     	=			TEXT_TO_IMAGE_IMAGE_PARAMS

   def 		A							(      self			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    torch.manual_seed(0							)
    a						=		UNetaDConditionModel(
        block_out_channels=(32, 64)							,   layers_per_block=1							,   sample_size=32							,   in_channels=4							,   out_channels=4							,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,   cross_attention_dim=32							,   )
    a						=		DDIMScheduler()
    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							,   )
    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=1000							,   )
    a						=		CLIPTextModel(__lowerCAmelCase							)
    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 		A							(      self			:       Any							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Dict=0							)       ->				Optional[int]:


    """simple docstring"""






    a						=		torch.manual_seed(__lowerCAmelCase							)
    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 		A							(      self			:       Optional[int]							)       ->				str:


    """simple docstring"""






    a						=		"cpu"  # ensure determinism for the device-dependent torch.Generator
    a						=		self.get_dummy_components()
    a						=		StableDiffusionPanoramaPipeline(**__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, 64, 64, 3)

    a						=		np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7]							)

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

   def 		A							(      self			:       str							)       ->				Dict:


    """simple docstring"""






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

   def 		A							(      self			:       Any							)       ->				Optional[Any]:


    """simple docstring"""






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

   def 		A							(      self			:       Optional[Any]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		"cpu"  # ensure determinism for the device-dependent torch.Generator
    a						=		self.get_dummy_components()
    a						=		StableDiffusionPanoramaPipeline(**__lowerCAmelCase							)
    a						=		sd_pipe.to(__lowerCAmelCase							)
    sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

    a						=		self.get_dummy_inputs(__lowerCAmelCase							)
    a						=		"french fries"
    a						=		sd_pipe(**__lowerCAmelCase							,   negative_prompt=__lowerCAmelCase							)
    a						=		output.images
    a						=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)

    a						=		np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]							)

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

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


    """simple docstring"""






    a						=		"cpu"  # ensure determinism for the device-dependent torch.Generator
    a						=		self.get_dummy_components()
    a						=		StableDiffusionPanoramaPipeline(**__lowerCAmelCase							)
    a						=		sd_pipe.to(__lowerCAmelCase							)
    sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

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

    assert image.shape == (1, 64, 64, 3)

    a						=		np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]							)

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

   def 		A							(      self			:       str							)       ->				Any:


    """simple docstring"""






    a						=		"cpu"  # ensure determinism for the device-dependent torch.Generator
    a						=		self.get_dummy_components()
    a						=		EulerAncestralDiscreteScheduler(
        beta_start=0.0_0_0_8_5							,   beta_end=0.0_1_2							,   beta_schedule="scaled_linear"							)
    a						=		StableDiffusionPanoramaPipeline(**__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, 64, 64, 3)

    a						=		np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2]							)

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




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


    """simple docstring"""






    a						=		"cpu"  # ensure determinism for the device-dependent torch.Generator
    a						=		self.get_dummy_components()
    a						=		PNDMScheduler(
        beta_start=0.0_0_0_8_5							,   beta_end=0.0_1_2							,   beta_schedule="scaled_linear"							,   skip_prk_steps=__lowerCAmelCase							)
    a						=		StableDiffusionPanoramaPipeline(**__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, 64, 64, 3)

    a						=		np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9]							)

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



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

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


    """simple docstring"""






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

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       List[str]=0							)       ->				Tuple:


    """simple docstring"""






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

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


    """simple docstring"""






    a						=		"stabilityai/stable-diffusion-2-base"
    a						=		DDIMScheduler.from_pretrained(__lowerCAmelCase							,   subfolder="scheduler"							)
    a						=		StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase							,   scheduler=__lowerCAmelCase							,   safety_checker=__lowerCAmelCase							)
    pipe.to(__lowerCAmelCase							)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase							)
    pipe.enable_attention_slicing()

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

    assert image.shape == (1, 512, 2048, 3)

    a						=		np.array(
        [
            0.3_6_9_6_8_3_9_2,
            0.2_7_0_2_5_3_7_2,
            0.3_2_4_4_6_7_6_6,
            0.2_8_3_7_9_3_8_7,
            0.3_6_3_6_3_2_7_4,
            0.3_0_7_3_3_3_4_7,
            0.2_7_1_0_0_0_2_7,
            0.2_7_0_5_4_1_2_5,
            0.2_5_5_3_6_0_9_6,
        ]							)

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

   def 		A							(      self			:       Optional[int]							)       ->				List[Any]:


    """simple docstring"""






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

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

    assert image.shape == (1, 512, 2048, 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 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    a						=		0

    def callback_fn(__lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       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, 64, 256)
      a						=		latents[0, -3:, -3:, -1]

      a						=		np.array(
          [
              0.1_8_6_8_1_8_6_9,
              0.3_3_9_0_7_8_1_6,
              0.5_3_6_1_2_7_6,
              0.1_4_4_3_2_8_6_5,
              -0.0_2_8_5_6_6_1_1,
              -0.7_3_9_4_1_1_2_3,
              0.2_3_3_9_7_9_8_7,
              0.4_7_3_2_2_6_8_2,
              -0.3_7_8_2_3_1_6_4,
          ]							)
      assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
     elif step == 2:
      a						=		latents.detach().cpu().numpy()
      assert latents.shape == (1, 4, 64, 256)
      a						=		latents[0, -3:, -3:, -1]

      a						=		np.array(
          [
              0.1_8_5_3_9_6_4_5,
              0.3_3_9_8_7_2_4_8,
              0.5_3_7_8_5_5_9,
              0.1_4_4_3_7_1_4_2,
              -0.0_2_4_5_5_2_6_1,
              -0.7_3_3_8_3_1_7,
              0.2_3_9_9_0_7_5_5,
              0.4_7_3_5_6_2_7_2,
              -0.3_7_8_6_5_0_5,
          ]							)

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

    a						=		False

    a						=		"stabilityai/stable-diffusion-2-base"
    a						=		DDIMScheduler.from_pretrained(__lowerCAmelCase							,   subfolder="scheduler"							)
    a						=		StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase							,   scheduler=__lowerCAmelCase							,   safety_checker=__lowerCAmelCase							)
    a						=		pipe.to(__lowerCAmelCase							)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase							)
    pipe.enable_attention_slicing()

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




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


    """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(__lowerCAmelCase							,   subfolder="scheduler"							)
    a						=		StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase							,   scheduler=__lowerCAmelCase							,   safety_checker=__lowerCAmelCase							)
    a						=		pipe.to(__lowerCAmelCase							)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase							)
    pipe.enable_attention_slicing(1							)
    pipe.enable_sequential_cpu_offload()

    a						=		self.get_inputs()
    a						=		pipe(**__lowerCAmelCase							)

    a						=		torch.cuda.max_memory_allocated()
    # make sure that less than 5.2 GB is allocated
    assert mem_bytes < 5.5 * 10**9






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt



class 		_lowercase		:

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


    """simple docstring"""






    a						=		""
    a						=		""
    a						=		[]
    a						=		0
    a						=		256
    a						=		0
    a						=		0
    a						=		0
    a						=		0

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Any							)       ->				int:


    """simple docstring"""






    a						=		cva.imread(__lowerCAmelCase							,   0							)
    a						=		copy.deepcopy(self.img							)
    a		,  a		,  a						=		plt.hist(self.img.ravel()							,   256							,   [0, 256]							,   label="x"							)
    a						=		np.sum(__lowerCAmelCase							)
    for i in range(len(__lowerCAmelCase							)							):
     a						=		x[i] / self.k
     self.sk += prk
     a						=		(self.L - 1) * self.sk
     if self.rem != 0:
      a						=		int(last % last							)
     a						=		int(last + 1 if self.rem >= 0.5 else last							)
     self.last_list.append(__lowerCAmelCase							)
     a						=		int(np.ma.count(self.img							) / self.img[1].size							)
     a						=		self.img[1].size
    for i in range(self.number_of_cols							):
     for j in range(self.number_of_rows							):
      a						=		self.img[j][i]
      if num != self.last_list[num]:
       a						=		self.last_list[num]
    cva.imwrite("output_data/output.jpg"							,   self.img							)

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    plt.hist(self.img.ravel()							,   256							,   [0, 256]							)




   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    cva.imshow("Output-Image"							,   self.img							)
    cva.imshow("Input-Image"							,   self.original_image							)
    cva.waitKey(5000							)
    cva.destroyAllWindows()


if __name__ == "__main__":
     A_		:     List[Any]        =     os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     A_		:     int        =     ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()

