"""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 	__lowerCAmelCase					(      a__  ,   a__  ,   unittest.TestCase):




    '''simple docstring'''






    __magic_name__   :			int       		=       StableDiffusionSAGPipeline
    __magic_name__   :			Any       		=       TEXT_TO_IMAGE_PARAMS
    __magic_name__   :			Optional[Any]       		=       TEXT_TO_IMAGE_BATCH_PARAMS
    __magic_name__   :			Tuple       		=       TEXT_TO_IMAGE_IMAGE_PARAMS
    __magic_name__   :			Dict       		=       TEXT_TO_IMAGE_IMAGE_PARAMS
    __magic_name__   :			List[Any]       		=       False





    def 					_UpperCAmelCase		(     self				:	List[str]							):
          torch.manual_seed(0							)
          A__	:      Union[str, Any]      =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__	:      Any      =DDIMScheduler(
              beta_start=0.00085					,				beta_end=0.012					,				beta_schedule="scaled_linear"					,				clip_sample=lowerCamelCase_					,				set_alpha_to_one=lowerCamelCase_					,				)
          torch.manual_seed(0							)
          A__	:      Union[str, 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							)
          A__	:      Union[str, Any]      =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					,				)
          A__	:      int      =CLIPTextModel(lowerCamelCase_							)
          A__	:      Any      =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

          A__	:      Optional[int]      ={
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components





    def 					_UpperCAmelCase		(     self				:	Any					,				UpperCamelCase__				:	Union[str, Any]					,				UpperCamelCase__				:	Optional[int]=0							):
          if str(lowerCamelCase_							).startswith("mps"							):
                A__	:      Optional[int]      =torch.manual_seed(lowerCamelCase_							)
          else:
                A__	:      Optional[Any]      =torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
          A__	:      List[Any]      ={
              "prompt": ".",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 1.0,
              "sag_scale": 1.0,
              "output_type": "numpy",
          }
          return inputs





    def 					_UpperCAmelCase		(     self				:	Dict							):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)






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




    '''simple docstring'''






    def 					_UpperCAmelCase		(     self				:	Any							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()





    def 					_UpperCAmelCase		(     self				:	Dict							):
          A__	:      str      =StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"							)
          A__	:      Dict      =sag_pipe.to(lowerCamelCase_							)
          sag_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

          A__	:      str      ="."
          A__	:      Optional[Any]      =torch.manual_seed(0							)
          A__	:      Optional[Any]      =sag_pipe(
              [prompt]					,				generator=lowerCamelCase_					,				guidance_scale=7.5					,				sag_scale=1.0					,				num_inference_steps=20					,				output_type="np"							)

          A__	:      int      =output.images

          A__	:      Dict      =image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          A__	:      Any      =np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]							)

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





    def 					_UpperCAmelCase		(     self				:	Tuple							):
          A__	:      Union[str, Any]      =StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
          A__	:      Tuple      =sag_pipe.to(lowerCamelCase_							)
          sag_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

          A__	:      Any      ="."
          A__	:      List[str]      =torch.manual_seed(0							)
          A__	:      Union[str, Any]      =sag_pipe(
              [prompt]					,				generator=lowerCamelCase_					,				guidance_scale=7.5					,				sag_scale=1.0					,				num_inference_steps=20					,				output_type="np"							)

          A__	:      Optional[Any]      =output.images

          A__	:      List[Any]      =image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          A__	:      Dict      =np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]							)

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





    def 					_UpperCAmelCase		(     self				:	List[Any]							):
          A__	:      Any      =StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
          A__	:      List[Any]      =sag_pipe.to(lowerCamelCase_							)
          sag_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

          A__	:      Union[str, Any]      ="."
          A__	:      List[str]      =torch.manual_seed(0							)
          A__	:      Optional[int]      =sag_pipe(
              [prompt]					,				width=768					,				height=512					,				generator=lowerCamelCase_					,				guidance_scale=7.5					,				sag_scale=1.0					,				num_inference_steps=20					,				output_type="np"					,				)

          A__	:      List[str]      =output.images

          assert image.shape == (1, 512, 768, 3)









'''simple docstring'''






# Copyright 2021 The HuggingFace 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.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCAmelCase       							=				[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def    _snake_case  (	A					,       A=None					,       A=None					,       A=None			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      True
							while ask_again:
														lowerCAmelCase__			  =      input(A			)
														try:
																					if default is not None and len(A			) == 0:
																												return default
																					return convert_value(A			) if convert_value is not None else result
														except Exception:
																					if error_message is not None:
																												print(A			)


def    _snake_case  (	A					,       A=[]					,       A=None					,       A=0			)		->       List[Any]:
							lowerCAmelCase__			  =      BulletMenu(A					,       A			)
							lowerCAmelCase__			  =      menu.run(default_choice=A			)
							return convert_value(A			) if convert_value is not None else result


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]			)


def    _snake_case  (	A			)		->       str:
							lowerCAmelCase__			  =      int(A			)
							return DynamoBackend(DYNAMO_BACKENDS[value]			).value


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]			)


def    _snake_case  (	A			)		->       List[str]:
							return {"yes": True, "no": False}[value.lower()]






class 						a__							(				argparse.RawDescriptionHelpFormatter    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      super()._format_usage(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      usage.replace('''<command> [<args>] '''   ,       ''''''  )
														return usage