



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class      UpperCamelCase    (						unittest.TestCase     ):
	lowerCAmelCase	:   List[Any]				=	StableDiffusionLDMaDPipeline
	lowerCAmelCase	:   Tuple				=	TEXT_TO_IMAGE_PARAMS
	lowerCAmelCase	:   List[Any]				=	TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCAmelCase	:   Any				=	TEXT_TO_IMAGE_IMAGE_PARAMS



	def 			__A							(       self		):
							torch.manual_seed(0		)
							A__							=       UNetaDConditionModel(
							    block_out_channels=(32, 64)			,	layers_per_block=2			,	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(
							    beta_start=0.00_085			,	beta_end=0.012			,	beta_schedule="scaled_linear"			,	clip_sample=UpperCAmelCase__			,	set_alpha_to_one=UpperCAmelCase__			,	)
							torch.manual_seed(0		)
							A__							=       AutoencoderKL(
							    block_out_channels=[32, 64]			,	in_channels=6			,	out_channels=6			,	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-0_5			,	num_attention_heads=4			,	num_hidden_layers=5			,	pad_token_id=1			,	vocab_size=1_000			,	)
							A__							=       CLIPTextModel(UpperCAmelCase__		)
							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			,	UpperCAmelCase__			,	UpperCAmelCase__=0		):
							if str(UpperCAmelCase__		).startswith("mps"		):
													A__							=       torch.manual_seed(UpperCAmelCase__		)
							else:
													A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       {
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs



	def 			__A							(       self		):
							A__							=       "cpu"  # ensure determinism for the device-dependent torch.Generator

							A__							=       self.get_dummy_components()
							A__							=       StableDiffusionLDMaDPipeline(**UpperCAmelCase__		)
							A__							=       ldmad_pipe.to(UpperCAmelCase__		)
							ldmad_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       ldmad_pipe(**UpperCAmelCase__		)
							A__						,       A__							=       output.rgb, output.depth

							A__							=       rgb[0, -3:, -3:, -1]
							A__							=       depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							A__							=       np.array(
							    [0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262]		)
							A__							=       np.array([103.46_727, 85.812_004, 87.849_236]		)

							assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb		).max() < 1e-2
							assert np.abs(image_slice_depth.flatten() - expected_slice_depth		).max() < 1e-2



	def 			__A							(       self		):
							A__							=       self.get_dummy_components()
							A__							=       StableDiffusionLDMaDPipeline(**UpperCAmelCase__		)
							A__							=       ldmad_pipe.to(UpperCAmelCase__		)
							ldmad_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       3 * [inputs["prompt"]]

							# forward
							A__							=       ldmad_pipe(**UpperCAmelCase__		)
							A__						,       A__							=       output.rgb, output.depth
							A__							=       rgb_slice_a[0, -3:, -3:, -1]
							A__							=       depth_slice_a[0, -3:, -1]

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       3 * [inputs.pop("prompt"		)]

							A__							=       ldmad_pipe.tokenizer(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=ldmad_pipe.tokenizer.model_max_length			,	truncation=UpperCAmelCase__			,	return_tensors="pt"			,	)
							A__							=       text_inputs["input_ids"].to(UpperCAmelCase__		)

							A__							=       ldmad_pipe.text_encoder(UpperCAmelCase__		)[0]

							A__							=       prompt_embeds

							# forward
							A__							=       ldmad_pipe(**UpperCAmelCase__		)
							A__						,       A__							=       output.rgb, output.depth
							A__							=       rgb_slice_a[0, -3:, -3:, -1]
							A__							=       depth_slice_a[0, -3:, -1]

							assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()		).max() < 1e-4
							assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()		).max() < 1e-4



	def 			__A							(       self		):
							A__							=       "cpu"  # ensure determinism for the device-dependent torch.Generator
							A__							=       self.get_dummy_components()
							A__							=       PNDMScheduler(skip_prk_steps=UpperCAmelCase__		)
							A__							=       StableDiffusionLDMaDPipeline(**UpperCAmelCase__		)
							A__							=       ldmad_pipe.to(UpperCAmelCase__		)
							ldmad_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       "french fries"
							A__							=       ldmad_pipe(**UpperCAmelCase__			,	negative_prompt=UpperCAmelCase__		)

							A__						,       A__							=       output.rgb, output.depth
							A__							=       rgb[0, -3:, -3:, -1]
							A__							=       depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							A__							=       np.array(
							    [0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217]		)
							A__							=       np.array([107.84_738, 84.62_802, 89.962_135]		)
							assert np.abs(rgb_slice.flatten() - expected_slice_rgb		).max() < 1e-2
							assert np.abs(depth_slice.flatten() - expected_slice_depth		).max() < 1e-2






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



	def 			__A							(       self		):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__="cpu"			,	UpperCAmelCase__=torch.floataa			,	UpperCAmelCase__=0		):
							A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       np.random.RandomState(UpperCAmelCase__		).standard_normal((1, 4, 64, 64)		)
							A__							=       torch.from_numpy(UpperCAmelCase__		).to(device=UpperCAmelCase__			,	dtype=UpperCAmelCase__		)
							A__							=       {
							    "prompt": "a photograph of an astronaut riding a horse",
							    "latents": latents,
							    "generator": generator,
							    "num_inference_steps": 3,
							    "guidance_scale": 7.5,
							    "output_type": "numpy",
							}
							return inputs



	def 			__A							(       self		):
							A__							=       StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		)
							A__							=       ldmad_pipe.to(UpperCAmelCase__		)
							ldmad_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_inputs(UpperCAmelCase__		)
							A__							=       ldmad_pipe(**UpperCAmelCase__		)
							A__						,       A__							=       output.rgb, output.depth
							A__							=       rgb[0, -3:, -3:, -1].flatten()
							A__							=       rgb[0, -3:, -1].flatten()

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512)

							A__							=       np.array(
							    [0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706]		)
							A__							=       np.array(
							    [0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706]		)
							assert np.abs(rgb_slice - expected_slice_rgb		).max() < 3e-3
							assert np.abs(depth_slice - expected_slice_depth		).max() < 3e-3






@nightly
@require_torch_gpu
class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self		):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__="cpu"			,	UpperCAmelCase__=torch.floataa			,	UpperCAmelCase__=0		):
							A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       np.random.RandomState(UpperCAmelCase__		).standard_normal((1, 4, 64, 64)		)
							A__							=       torch.from_numpy(UpperCAmelCase__		).to(device=UpperCAmelCase__			,	dtype=UpperCAmelCase__		)
							A__							=       {
							    "prompt": "a photograph of an astronaut riding a horse",
							    "latents": latents,
							    "generator": generator,
							    "num_inference_steps": 50,
							    "guidance_scale": 7.5,
							    "output_type": "numpy",
							}
							return inputs



	def 			__A							(       self		):
							A__							=       StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		).to(UpperCAmelCase__		)
							ldmad_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_inputs(UpperCAmelCase__		)
							A__							=       ldmad_pipe(**UpperCAmelCase__		)
							A__						,       A__							=       output.rgb, output.depth

							A__							=       0.495_586
							A__							=       0.33_795_515
							A__							=       112.48_518
							A__							=       98.489_746
							assert np.abs(expected_rgb_mean - rgb.mean()		) < 1e-3
							assert np.abs(expected_rgb_std - rgb.std()		) < 1e-3
							assert np.abs(expected_depth_mean - depth.mean()		) < 1e-3
							assert np.abs(expected_depth_std - depth.std()		) < 1e-3



	def 			__A							(       self		):
							A__							=       StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"		).to(UpperCAmelCase__		)
							ldmad_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_inputs(UpperCAmelCase__		)
							A__							=       ldmad_pipe(**UpperCAmelCase__		)
							A__						,       A__							=       output.rgb, output.depth

							A__							=       0.4_194_127
							A__							=       0.35_375_586
							A__							=       0.5_638_502
							A__							=       0.34_686_103

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512, 1)
							assert np.abs(expected_rgb_mean - rgb.mean()		) < 1e-3
							assert np.abs(expected_rgb_std - rgb.std()		) < 1e-3
							assert np.abs(expected_depth_mean - depth.mean()		) < 1e-3
							assert np.abs(expected_depth_std - depth.std()		) < 1e-3










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      List[Any]      =  {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""gpt_neox"""



	def __init__(       self			,	UpperCAmelCase__=50_432			,	UpperCAmelCase__=6_144			,	UpperCAmelCase__=44			,	UpperCAmelCase__=64			,	UpperCAmelCase__=24_576			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.25			,	UpperCAmelCase__=10_000			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=2_048			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0			,	UpperCAmelCase__=2			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(bos_token_id=UpperCAmelCase__			,	eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       vocab_size
							A__							=       max_position_embeddings
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       rotary_pct
							A__							=       rotary_emb_base
							A__							=       attention_dropout
							A__							=       hidden_dropout
							A__							=       classifier_dropout
							A__							=       initializer_range
							A__							=       layer_norm_eps
							A__							=       use_cache
							A__							=       tie_word_embeddings
							A__							=       use_parallel_residual
							A__							=       rope_scaling
							self._rope_scaling_validation()

							if self.hidden_size % self.num_attention_heads != 0:
													raise ValueError(
													    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"		)



	def 			__A							(       self		):
							if self.rope_scaling is None:
													return

							if not isinstance(self.rope_scaling			,	UpperCAmelCase__		) or len(self.rope_scaling		) != 2:
													raise ValueError(
													    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
													    F"""got {self.rope_scaling}"""		)
							A__							=       self.rope_scaling.get("type"			,	UpperCAmelCase__		)
							A__							=       self.rope_scaling.get("factor"			,	UpperCAmelCase__		)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""		)
							if rope_scaling_factor is None or not isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) or rope_scaling_factor <= 1.0:
													raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""		)





