






'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
    import PIL

if is_torch_available():
    import torch


lowerCamelCase_				   =   logging.get_logger(__name__)

class   _UpperCAmelCase     (	SCREAMING_SNAKE_CASE__		):




   """simple docstring"""

   snake_case       							=		['''pixel_values''']

   def __init__(						self					:      int , __UpperCAmelCase					:      bool = True , __UpperCAmelCase					:      Dict[str, int] = None , __UpperCAmelCase					:      PILImageResampling = PILImageResampling.BILINEAR , __UpperCAmelCase					:      bool = True , __UpperCAmelCase					:      Union[int, float] = 1 / 255 , __UpperCAmelCase					:      bool = True , __UpperCAmelCase					:      Dict[str, int] = None , __UpperCAmelCase					:      bool = True , **__UpperCAmelCase					:      Dict , ):





         '''simple docstring'''





         super().__init__(**_lowercase						)
         _A     	=      size if size is not None else {"shortest_edge": 224}
         _A     	=      get_size_dict(_lowercase , default_to_square=_lowercase						)
         _A     	=      crop_size if crop_size is not None else {"height": 256, "width": 256}
         _A     	=      get_size_dict(_lowercase , param_name="crop_size"						)

         _A     	=      do_resize
         _A     	=      size
         _A     	=      resample
         _A     	=      do_rescale
         _A     	=      rescale_factor
         _A     	=      do_center_crop
         _A     	=      crop_size
         _A     	=      do_flip_channel_order

   def 		lowerCAmelCase					(						self					:      int , __UpperCAmelCase					:      np.ndarray , __UpperCAmelCase					:      Dict[str, int] , __UpperCAmelCase					:      PILImageResampling = PIL.Image.BILINEAR , __UpperCAmelCase					:      Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase					:      List[Any] , ):





         '''simple docstring'''





         _A     	=      get_size_dict(_lowercase , default_to_square=_lowercase						)
         if "shortest_edge" not in size:
               raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''						)
         _A     	=      get_resize_output_image_size(_lowercase , size=size["shortest_edge"] , default_to_square=_lowercase						)
         return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase						)

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      np.ndarray , __UpperCAmelCase					:      Dict[str, int] , __UpperCAmelCase					:      Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase					:      str , ):





         '''simple docstring'''





         _A     	=      get_size_dict(_lowercase						)
         if "height" not in size or "width" not in size:
               raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''						)
         return center_crop(_lowercase , size=(size["height"], size["width"]) , data_format=_lowercase , **_lowercase						)

   def 		lowerCAmelCase					(						self					:      Tuple , __UpperCAmelCase					:      np.ndarray , __UpperCAmelCase					:      Union[int, float] , __UpperCAmelCase					:      Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase					:      Optional[int] , ):





         '''simple docstring'''





         return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase						)

   def 		lowerCAmelCase					(						self					:      Tuple , __UpperCAmelCase					:      np.ndarray , __UpperCAmelCase					:      Optional[Union[str, ChannelDimension]] = None						):





         '''simple docstring'''





         return flip_channel_order(_lowercase , data_format=_lowercase						)

   def 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      ImageInput , __UpperCAmelCase					:      bool = None , __UpperCAmelCase					:      Dict[str, int] = None , __UpperCAmelCase					:      PILImageResampling = None , __UpperCAmelCase					:      bool = None , __UpperCAmelCase					:      float = None , __UpperCAmelCase					:      bool = None , __UpperCAmelCase					:      Dict[str, int] = None , __UpperCAmelCase					:      bool = None , __UpperCAmelCase					:      Optional[Union[str, TensorType]] = None , __UpperCAmelCase					:      ChannelDimension = ChannelDimension.FIRST , **__UpperCAmelCase					:      List[Any] , ):





         '''simple docstring'''





         _A     	=      do_resize if do_resize is not None else self.do_resize
         _A     	=      resample if resample is not None else self.resample
         _A     	=      do_rescale if do_rescale is not None else self.do_rescale
         _A     	=      rescale_factor if rescale_factor is not None else self.rescale_factor
         _A     	=      do_center_crop if do_center_crop is not None else self.do_center_crop
         _A     	=      (
             do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
         )

         _A     	=      size if size is not None else self.size
         _A     	=      get_size_dict(_lowercase , default_to_square=_lowercase						)
         _A     	=      crop_size if crop_size is not None else self.crop_size
         _A     	=      get_size_dict(_lowercase , param_name="crop_size"						)

         _A     	=      make_list_of_images(_lowercase						)

         if not valid_images(_lowercase						):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."						)

         if do_resize and size is None:
               raise ValueError("Size must be specified if do_resize is True."						)

         if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."						)

         if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."						)

         # All transformations expect numpy arrays.
         _A     	=      [to_numpy_array(_lowercase						) for image in images]

         if do_resize:
               _A     	=      [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase						) for image in images]

         if do_center_crop:
               _A     	=      [self.center_crop(image=_lowercase , size=_lowercase						) for image in images]

         if do_rescale:
               _A     	=      [self.rescale(image=_lowercase , scale=_lowercase						) for image in images]

         # the pretrained checkpoints assume images are BGR, not RGB
         if do_flip_channel_order:
               _A     	=      [self.flip_channel_order(image=_lowercase						) for image in images]

         _A     	=      [to_channel_dimension_format(_lowercase , _lowercase						) for image in images]

         _A     	=      {"pixel_values": images}
         return BatchFeature(data=_lowercase , tensor_type=_lowercase						)

   def 		lowerCAmelCase					(						self					:      Union[str, Any] , __UpperCAmelCase					:      int , __UpperCAmelCase					:      List[Tuple] = None						):





         '''simple docstring'''





         _A     	=      outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
               if len(_lowercase						) != len(_lowercase						):
                     raise ValueError(
                         "Make sure that you pass in as many target sizes as the batch dimension of the logits"						)

               if is_torch_tensor(_lowercase						):
                     _A     	=      target_sizes.numpy()

               _A     	=      []

               for idx in range(len(_lowercase						)						):
                     _A     	=      torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0						) , size=target_sizes[idx] , mode="bilinear" , align_corners=_lowercase						)
                     _A     	=      resized_logits[0].argmax(dim=0						)
                     semantic_segmentation.append(_lowercase						)
         else:
               _A     	=      logits.argmax(dim=1						)
               _A     	=      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

         return semantic_segmentation










"""simple docstring"""







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  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     StableDiffusionPanoramaPipeline
			__magic_name__     					=     TEXT_TO_IMAGE_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_BATCH_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_IMAGE_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_IMAGE_PARAMS



			def 				__lowercase					(			self							:				Optional[int]		)							->					int:
									torch.manual_seed(0		)
									snake_case   :			Optional[Any]						    = 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   ,	)
									snake_case   :			Tuple						    = DDIMScheduler()
									torch.manual_seed(0		)
									snake_case   :			List[Any]						    = 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		)
									snake_case   :			str						    = 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   ,	)
									snake_case   :			int						    = CLIPTextModel(_lowercase		)
									snake_case   :			Union[str, Any]						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

									snake_case   :			Any						    = {
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				int   ,	_lowercase							:				Dict=0		)							->					Optional[int]:
									snake_case   :			Union[str, Any]						    = torch.manual_seed(_lowercase		)
									snake_case   :			str						    = {
									    "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 				__lowercase					(			self							:				str		)							->					List[str]:
									snake_case   :			Any						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			Dict						    = self.get_dummy_components()
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			List[Any]						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Union[str, Any]						    = sd_pipe(**_lowercase		).images
									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									snake_case   :			List[Any]						    = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]		)

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



			def 				__lowercase					(			self							:				int		)							->					Union[str, Any]:
									super().test_inference_batch_consistent(batch_sizes=[1, 2]		)



			def 				__lowercase					(			self							:				Tuple		)							->					Tuple:
									super().test_inference_batch_single_identical(batch_size=2   ,	expected_max_diff=3.25E-3		)



			def 				__lowercase					(			self							:				Any		)							->					List[Any]:
									snake_case   :			Any						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			Union[str, Any]						    = self.get_dummy_components()
									snake_case   :			Tuple						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			Tuple						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			List[str]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			int						    = "french fries"
									snake_case   :			Union[str, Any]						    = sd_pipe(**_lowercase   ,	negative_prompt=_lowercase		)
									snake_case   :			str						    = output.images
									snake_case   :			Optional[Any]						    = image[0, -3:, -3:, -1]

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

									snake_case   :			Union[str, Any]						    = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]		)

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



			def 				__lowercase					(			self							:				str		)							->					Any:
									snake_case   :			Optional[Any]						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			List[Any]						    = self.get_dummy_components()
									snake_case   :			List[Any]						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			int						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Tuple						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			str						    = sd_pipe(**_lowercase   ,	view_batch_size=2		)
									snake_case   :			Optional[Any]						    = output.images
									snake_case   :			int						    = image[0, -3:, -3:, -1]

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

									snake_case   :			str						    = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]		)

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



			def 				__lowercase					(			self							:				int		)							->					Optional[Any]:
									snake_case   :			Dict						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			List[str]						    = self.get_dummy_components()
									snake_case   :			Any						    = EulerAncestralDiscreteScheduler(
									    beta_start=0.00085   ,	beta_end=0.012   ,	beta_schedule="scaled_linear"		)
									snake_case   :			List[str]						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			List[str]						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			List[Any]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[Any]						    = sd_pipe(**_lowercase		).images
									snake_case   :			int						    = image[0, -3:, -3:, -1]

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

									snake_case   :			Tuple						    = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]		)

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



			def 				__lowercase					(			self							:				Tuple		)							->					Union[str, Any]:
									snake_case   :			str						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			str						    = self.get_dummy_components()
									snake_case   :			Optional[int]						    = PNDMScheduler(
									    beta_start=0.00085   ,	beta_end=0.012   ,	beta_schedule="scaled_linear"   ,	skip_prk_steps=_lowercase		)
									snake_case   :			Dict						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			Dict						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[int]						    = sd_pipe(**_lowercase		).images
									snake_case   :			str						    = image[0, -3:, -3:, -1]

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

									snake_case   :			Optional[int]						    = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]		)

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



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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					str:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Any=0		)							->					Optional[Any]:
									snake_case   :			Any						    = torch.manual_seed(_lowercase		)
									snake_case   :			Any						    = {
									    "prompt": "a photo of the dolomites",
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs



			def 				__lowercase					(			self							:				Optional[Any]		)							->					Union[str, Any]:
									snake_case   :			List[Any]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			Dict						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

									snake_case   :			str						    = self.get_inputs()
									snake_case   :			List[Any]						    = pipe(**_lowercase		).images
									snake_case   :			Optional[int]						    = image[0, -3:, -3:, -1].flatten()

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

									snake_case   :			List[str]						    = np.array(
									    [
									        0.36968392,
									        0.27025372,
									        0.32446766,
									        0.28379387,
									        0.36363274,
									        0.30733347,
									        0.27100027,
									        0.27054125,
									        0.25536096,
									    ]		)

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



			def 				__lowercase					(			self							:				Tuple		)							->					List[str]:
									snake_case   :			List[str]						    = StableDiffusionPanoramaPipeline.from_pretrained(
									    "stabilityai/stable-diffusion-2-base"   ,	safety_checker=_lowercase		)
									snake_case   :			int						    = LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
									pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

									snake_case   :			Dict						    = self.get_inputs()
									snake_case   :			int						    = pipe(**_lowercase		).images
									snake_case   :			Optional[Any]						    = image[0, -3:, -3:, -1].flatten()

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

									snake_case   :			List[str]						    = 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 				__lowercase					(			self							:				str		)							->					Any:
									snake_case   :			Any						    = 0

									def callback_fn(_lowercase							:				int   ,	_lowercase							:				int   ,	_lowercase							:				torch.FloatTensor		) -> None:
															snake_case   :			List[Any]						    = True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					snake_case   :			List[Any]						    = latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					snake_case   :			int						    = latents[0, -3:, -3:, -1]

																					snake_case   :			Tuple						    = np.array(
																					    [
																					        0.18681869,
																					        0.33907816,
																					        0.5361276,
																					        0.14432865,
																					        -0.02856611,
																					        -0.73941123,
																					        0.23397987,
																					        0.47322682,
																					        -0.37823164,
																					    ]		)
																					assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2
															elif step == 2:
																					snake_case   :			Union[str, Any]						    = latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					snake_case   :			Tuple						    = latents[0, -3:, -3:, -1]

																					snake_case   :			Tuple						    = np.array(
																					    [
																					        0.18539645,
																					        0.33987248,
																					        0.5378559,
																					        0.14437142,
																					        -0.02455261,
																					        -0.7338317,
																					        0.23990755,
																					        0.47356272,
																					        -0.3786505,
																					    ]		)

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

									snake_case   :			Dict						    = False

									snake_case   :			List[Any]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			List[str]						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Optional[Any]						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

									snake_case   :			Tuple						    = self.get_inputs()
									pipe(**_lowercase   ,	callback=_lowercase   ,	callback_steps=1		)
									assert callback_fn.has_been_called
									assert number_of_steps == 3



			def 				__lowercase					(			self							:				Any		)							->					Tuple:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									snake_case   :			Optional[int]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			Tuple						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									snake_case   :			Tuple						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing(1		)
									pipe.enable_sequential_cpu_offload()

									snake_case   :			Any						    = self.get_inputs()
									snake_case   :			Optional[int]						    = pipe(**_lowercase		)

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



