





"""simple docstring"""





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      StableDiffusionSAGPipeline
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE					=      TEXT_TO_IMAGE_IMAGE_PARAMS
      _SCREAMING_SNAKE_CASE					=      False





      def     _snake_case			(       self )		->     Union[str, Any]:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       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      ,						)
            lowerCAmelCase  						=       DDIMScheduler(
                beta_start=0.00_085      ,						beta_end=0.012      ,						beta_schedule="""scaled_linear"""      ,						clip_sample=lowercase      ,						set_alpha_to_one=lowercase      ,						)
            torch.manual_seed(0 )
            lowerCAmelCase  						=       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 )
            lowerCAmelCase  						=       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=1_000      ,						)
            lowerCAmelCase  						=       CLIPTextModel(lowercase )
            lowerCAmelCase  						=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

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





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=0 )		->     Union[str, Any]:
            if str(lowercase ).startswith("""mps""" ):
                  lowerCAmelCase  						=       torch.manual_seed(lowercase )
            else:
                  lowerCAmelCase  						=       torch.Generator(device=lowercase ).manual_seed(lowercase )
            lowerCAmelCase  						=       {
                """prompt""": """.""",
                """generator""": generator,
                """num_inference_steps""": 2,
                """guidance_scale""": 1.0,
                """sag_scale""": 1.0,
                """output_type""": """numpy""",
            }
            return inputs





      def     _snake_case			(       self )		->     str:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3 )







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





      def     _snake_case			(       self )		->     Tuple:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
            lowerCAmelCase  						=       sag_pipe.to(lowercase )
            sag_pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       """."""
            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sag_pipe(
                [prompt]      ,						generator=lowercase      ,						guidance_scale=7.5      ,						sag_scale=1.0      ,						num_inference_steps=20      ,						output_type="""np""" )

            lowerCAmelCase  						=       output.images

            lowerCAmelCase  						=       image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            lowerCAmelCase  						=       np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949] )

            assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
            lowerCAmelCase  						=       sag_pipe.to(lowercase )
            sag_pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       """."""
            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sag_pipe(
                [prompt]      ,						generator=lowercase      ,						guidance_scale=7.5      ,						sag_scale=1.0      ,						num_inference_steps=20      ,						output_type="""np""" )

            lowerCAmelCase  						=       output.images

            lowerCAmelCase  						=       image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            lowerCAmelCase  						=       np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371] )

            assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2





      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
            lowerCAmelCase  						=       sag_pipe.to(lowercase )
            sag_pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       """."""
            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sag_pipe(
                [prompt]      ,						width=768      ,						height=512      ,						generator=lowercase      ,						guidance_scale=7.5      ,						sag_scale=1.0      ,						num_inference_steps=20      ,						output_type="""np"""      ,						)

            lowerCAmelCase  						=       output.images

            assert image.shape == (1, 512, 768, 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."						)
