



'''simple docstring'''


# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


lowercase_      :				List[str]   					=    '''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
						import torch

						print('''Torch version:''', torch.__version__)
						print('''Cuda available:''', torch.cuda.is_available())
						print('''Cuda version:''', torch.version.cuda)
						print('''CuDNN version:''', torch.backends.cudnn.version())
						print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
						print('''Torch version:''', None)

try:
						import transformers

						print('''transformers version:''', transformers.__version__)
except ImportError:
						print('''transformers version:''', None)







'''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      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					StableDiffusionPanoramaPipeline
	__A			  =					TEXT_TO_IMAGE_PARAMS
	__A			  =					TEXT_TO_IMAGE_BATCH_PARAMS
	__A			  =					TEXT_TO_IMAGE_IMAGE_PARAMS
	__A			  =					TEXT_TO_IMAGE_IMAGE_PARAMS







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							torch.manual_seed(0						)
							lowercase        =							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 ,				)
							lowercase        =							DDIMScheduler()
							torch.manual_seed(0						)
							lowercase        =							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						)
							lowercase        =							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 ,				)
							lowercase        =							CLIPTextModel(_lowerCAmelCase						)
							lowercase        =							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

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







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=0						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							torch.manual_seed(_lowerCAmelCase						)
							lowercase        =							{
							    """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						)    ->    int:
							'''simple docstring'''






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

							lowercase        =							self.get_dummy_inputs(_lowerCAmelCase						)
							lowercase        =							sd_pipe(**_lowerCAmelCase						).images
							lowercase        =							image[0, -3:, -3:, -1]
							assert image.shape == (1, 64, 64, 3)

							lowercase        =							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   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






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

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

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

							lowercase        =							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   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






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

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

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

							lowercase        =							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   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase        =							self.get_dummy_components()
							lowercase        =							EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule="""scaled_linear"""						)
							lowercase        =							StableDiffusionPanoramaPipeline(**_lowerCAmelCase						)
							lowercase        =							sd_pipe.to(_lowerCAmelCase						)
							sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

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

							lowercase        =							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   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase        =							self.get_dummy_components()
							lowercase        =							PNDMScheduler(
							    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule="""scaled_linear""" ,				skip_prk_steps=_lowerCAmelCase						)
							lowercase        =							StableDiffusionPanoramaPipeline(**_lowerCAmelCase						)
							lowercase        =							sd_pipe.to(_lowerCAmelCase						)
							sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

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

							lowercase        =							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      __UpperCamelCase       (unittest.TestCase			):







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






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







	def   _a   (    self ,				_lowerCAmelCase=0						)    ->    Optional[int]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






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

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

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

							lowercase        =							np.array(
							    [
							        0.3696_8392,
							        0.2702_5372,
							        0.3244_6766,
							        0.2837_9387,
							        0.3636_3274,
							        0.3073_3347,
							        0.2710_0027,
							        0.2705_4125,
							        0.2553_6096,
							    ]						)

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







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






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

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

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

							lowercase        =							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						)    ->    Any:
							'''simple docstring'''






							lowercase        =							0

							def callback_fn(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						) -> None:
													lowercase        =							True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																			lowercase        =							latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 256)
																			lowercase        =							latents[0, -3:, -3:, -1]

																			lowercase        =							np.array(
																			    [
																			        0.1868_1869,
																			        0.3390_7816,
																			        0.536_1276,
																			        0.1443_2865,
																			        -0.0285_6611,
																			        -0.7394_1123,
																			        0.2339_7987,
																			        0.4732_2682,
																			        -0.3782_3164,
																			    ]						)
																			assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
													elif step == 2:
																			lowercase        =							latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 256)
																			lowercase        =							latents[0, -3:, -3:, -1]

																			lowercase        =							np.array(
																			    [
																			        0.1853_9645,
																			        0.3398_7248,
																			        0.537_8559,
																			        0.1443_7142,
																			        -0.0245_5261,
																			        -0.733_8317,
																			        0.2399_0755,
																			        0.4735_6272,
																			        -0.378_6505,
																			    ]						)

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

							lowercase        =							False

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

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







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






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

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

							lowercase        =							self.get_inputs()
							lowercase        =							pipe(**_lowerCAmelCase						)

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


