'''simple docstring'''





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        __A      ( unittest.TestCase	):
    a__       :						Union[str, Any]          =  StableDiffusionLDMaDPipeline
    a__       :						str          =  TEXT_TO_IMAGE_PARAMS
    a__       :						Dict          =  TEXT_TO_IMAGE_BATCH_PARAMS
    a__       :						str          =  TEXT_TO_IMAGE_IMAGE_PARAMS



    def 					_lowercase						(self    :    int ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   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   ,			)
         UpperCAmelCase_           =   DDIMScheduler(
             beta_start=0.0_00_85   ,			beta_end=0.0_12   ,			beta_schedule="scaled_linear"   ,			clip_sample=__a   ,			set_alpha_to_one=__a   ,			)
         torch.manual_seed(0 )
         UpperCAmelCase_           =   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 )
         UpperCAmelCase_           =   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   ,			)
         UpperCAmelCase_           =   CLIPTextModel(__a )
         UpperCAmelCase_           =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

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



    def 					_lowercase						(self    :    Any   ,			__a    :    str   ,			__a    :    Any=0 ):
         if str(__a ).startswith("mps" ):
              UpperCAmelCase_           =   torch.manual_seed(__a )
         else:
              UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(__a )
         UpperCAmelCase_           =   {
             "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 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase_           =   self.get_dummy_components()
         UpperCAmelCase_           =   StableDiffusionLDMaDPipeline(**__a )
         UpperCAmelCase_           =   ldmad_pipe.to(__a )
         ldmad_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs(__a )
         UpperCAmelCase_           =   ldmad_pipe(**__a )
         UpperCAmelCase_    , UpperCAmelCase_           =   output.rgb, output.depth

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

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

         UpperCAmelCase_           =   np.array(
             [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62] )
         UpperCAmelCase_           =   np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36] )

         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 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.get_dummy_components()
         UpperCAmelCase_           =   StableDiffusionLDMaDPipeline(**__a )
         UpperCAmelCase_           =   ldmad_pipe.to(__a )
         ldmad_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs(__a )
         UpperCAmelCase_           =   3 * [inputs["prompt"]]

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

         UpperCAmelCase_           =   self.get_dummy_inputs(__a )
         UpperCAmelCase_           =   3 * [inputs.pop("prompt" )]

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

         UpperCAmelCase_           =   ldmad_pipe.text_encoder(__a )[0]

         UpperCAmelCase_           =   prompt_embeds

         # forward
         UpperCAmelCase_           =   ldmad_pipe(**__a )
         UpperCAmelCase_    , UpperCAmelCase_           =   output.rgb, output.depth
         UpperCAmelCase_           =   rgb_slice_a[0, -3:, -3:, -1]
         UpperCAmelCase_           =   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 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =   self.get_dummy_components()
         UpperCAmelCase_           =   PNDMScheduler(skip_prk_steps=__a )
         UpperCAmelCase_           =   StableDiffusionLDMaDPipeline(**__a )
         UpperCAmelCase_           =   ldmad_pipe.to(__a )
         ldmad_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs(__a )
         UpperCAmelCase_           =   "french fries"
         UpperCAmelCase_           =   ldmad_pipe(**__a   ,			negative_prompt=__a )

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

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

         UpperCAmelCase_           =   np.array(
             [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17] )
         UpperCAmelCase_           =   np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35] )
         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        __A      ( unittest.TestCase	):



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



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    List[Any]   ,			__a    :    int="cpu"   ,			__a    :    Dict=torch.floataa   ,			__a    :    Dict=0 ):
         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(__a )
         UpperCAmelCase_           =   np.random.RandomState(__a ).standard_normal((1, 4, 64, 64) )
         UpperCAmelCase_           =   torch.from_numpy(__a ).to(device=__a   ,			dtype=__a )
         UpperCAmelCase_           =   {
             "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 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
         UpperCAmelCase_           =   ldmad_pipe.to(__a )
         ldmad_pipe.set_progress_bar_config(disable=__a )

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

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

         UpperCAmelCase_           =   np.array(
             [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06] )
         UpperCAmelCase_           =   np.array(
             [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06] )
         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        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    List[Any] ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    def 					_lowercase						(self    :    int   ,			__a    :    Union[str, Any]   ,			__a    :    List[str]="cpu"   ,			__a    :    List[str]=torch.floataa   ,			__a    :    Tuple=0 ):
         UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(__a )
         UpperCAmelCase_           =   np.random.RandomState(__a ).standard_normal((1, 4, 64, 64) )
         UpperCAmelCase_           =   torch.from_numpy(__a ).to(device=__a   ,			dtype=__a )
         UpperCAmelCase_           =   {
             "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 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(__a )
         ldmad_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_inputs(__a )
         UpperCAmelCase_           =   ldmad_pipe(**__a )
         UpperCAmelCase_    , UpperCAmelCase_           =   output.rgb, output.depth

         UpperCAmelCase_           =   0.49_55_86
         UpperCAmelCase_           =   0.33_79_55_15
         UpperCAmelCase_           =   1_12.4_85_18
         UpperCAmelCase_           =   98.48_97_46
         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 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(__a )
         ldmad_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_inputs(__a )
         UpperCAmelCase_           =   ldmad_pipe(**__a )
         UpperCAmelCase_    , UpperCAmelCase_           =   output.rgb, output.depth

         UpperCAmelCase_           =   0.4_19_41_27
         UpperCAmelCase_           =   0.35_37_55_86
         UpperCAmelCase_           =   0.5_63_85_02
         UpperCAmelCase_           =   0.34_68_61_03

         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








'''simple docstring'''

import warnings

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


lowerCamelCase							:   str  						=	logging.get_logger(__name__)

lowerCamelCase							:   int  						=	{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[str, Any]  					=							"""mvp"""
      lowerCAmelCase__					:     Optional[Any]  					=							["""past_key_values"""]
      lowerCAmelCase__					:     List[str]  					=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(self    :    Any   ,						UpperCamelCase    :    Optional[int]=50267   ,						UpperCamelCase    :    Tuple=1024   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Tuple=4096   ,						UpperCamelCase    :    Dict=16   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Optional[int]=4096   ,						UpperCamelCase    :    Optional[int]=16   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    List[Any]="gelu"   ,						UpperCamelCase    :    Union[str, Any]=1024   ,						UpperCamelCase    :    Optional[Any]=0.1   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    Optional[Any]=0.02   ,						UpperCamelCase    :    List[str]=0.0   ,						UpperCamelCase    :    List[str]=False   ,						UpperCamelCase    :    Optional[int]=True   ,						UpperCamelCase    :    Any=1   ,						UpperCamelCase    :    int=0   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Any=True   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    int=100   ,						UpperCamelCase    :    Optional[Any]=800   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= d_model
             lowercase__      			= encoder_ffn_dim
             lowercase__      			= encoder_layers
             lowercase__      			= encoder_attention_heads
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_attention_heads
             lowercase__      			= dropout
             lowercase__      			= attention_dropout
             lowercase__      			= activation_dropout
             lowercase__      			= activation_function
             lowercase__      			= init_std
             lowercase__      			= encoder_layerdrop
             lowercase__      			= decoder_layerdrop
             lowercase__      			= classifier_dropout
             lowercase__      			= use_cache
             lowercase__      			= encoder_layers
             lowercase__      			= scale_embedding  # scale factor will be sqrt(d_model) if True
             lowercase__      			= use_prompt
             lowercase__      			= prompt_length
             lowercase__      			= prompt_mid_dim

             super().__init__(
                 pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						is_encoder_decoder=UpperCamelCase   ,						decoder_start_token_id=UpperCamelCase   ,						forced_eos_token_id=UpperCamelCase   ,						**UpperCamelCase   ,						)

             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''   ,						UpperCamelCase ):
                    lowercase__      			= self.bos_token_id
                    warnings.warn(
                        f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                        '''The config can simply be saved and uploaded again to be fixed.''' )




