



"""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








"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



