'''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






# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# 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.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


SCREAMING_SNAKE_CASE							:List[str]							 =				'pytorch_model.bin'
SCREAMING_SNAKE_CASE							:str							 =				'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'adapter_config.json'
SCREAMING_SNAKE_CASE							:Dict							 =				'adapter_model.bin'
SCREAMING_SNAKE_CASE							:Dict							 =				'adapter_model.safetensors'
SCREAMING_SNAKE_CASE							:str							 =				'tf_model.h5'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE							:str							 =				'model.ckpt'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'flax_model.msgpack'
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE							:Tuple							 =				'model.safetensors'
SCREAMING_SNAKE_CASE							:List[Any]							 =				'model.safetensors.index.json'
SCREAMING_SNAKE_CASE							:str							 =				'config.json'
SCREAMING_SNAKE_CASE							:int							 =				'preprocessor_config.json'
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'generation_config.json'
SCREAMING_SNAKE_CASE							:List[str]							 =				'modelcard.json'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				'▁'
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

SCREAMING_SNAKE_CASE							:str							 =				[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE							:List[Any]							 =				[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def 						UpperCAmelCase			(	a_						)		->	Dict:







				"""simple docstring"""



				if version.parse(a_						) < version.parse(a_						):
								if "dev" in min_version:
												__A   			= (
												    "This example requires a source install from HuggingFace Transformers (see "
												    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
												)
								else:
												__A   			= F'''This example requires a minimum version of {min_version},'''
								error_message += F''' but the version found is {__version__}.\n'''
								raise ImportError(
								    error_message
								    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
								    "versions of HuggingFace Transformers."						)
