



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      _UpperCAmelCase      (							A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					StableDiffusionSAGPipeline
     UpperCamelCase__		     =					TEXT_TO_IMAGE_PARAMS
     UpperCamelCase__		     =					TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		     =					TEXT_TO_IMAGE_IMAGE_PARAMS
     UpperCamelCase__		     =					TEXT_TO_IMAGE_IMAGE_PARAMS
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         torch.manual_seed(0)
         A__					=       UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=4			,    out_channels=4			,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,    cross_attention_dim=3_2			,    )
         A__					=       DDIMScheduler(
             beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    beta_schedule='''scaled_linear'''			,    clip_sample=a__			,    set_alpha_to_one=a__			,    )
         torch.manual_seed(0)
         A__					=       AutoencoderKL(
             block_out_channels=[3_2, 6_4]			,    in_channels=3			,    out_channels=3			,    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=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1_0_0_0			,    )
         A__					=       CLIPTextModel(a__)
         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 		snake_case_		(					self			,    a__			,    a__=0):
         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)
         A__					=       {
             '''prompt''': '''.''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 1.0,
             '''sag_scale''': 1.0,
             '''output_type''': '''numpy''',
         }
         return inputs




     def 		snake_case_		(					self):
         super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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




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




     def 		snake_case_		(					self):
         A__					=       StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
         A__					=       sag_pipe.to(a__)
         sag_pipe.set_progress_bar_config(disable=a__)

         A__					=       '''.'''
         A__					=       torch.manual_seed(0)
         A__					=       sag_pipe(
             [prompt]			,    generator=a__			,    guidance_scale=7.5			,    sag_scale=1.0			,    num_inference_steps=2_0			,    output_type='''np''')

         A__					=       output.images

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         A__					=       np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9])

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




     def 		snake_case_		(					self):
         A__					=       StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
         A__					=       sag_pipe.to(a__)
         sag_pipe.set_progress_bar_config(disable=a__)

         A__					=       '''.'''
         A__					=       torch.manual_seed(0)
         A__					=       sag_pipe(
             [prompt]			,    generator=a__			,    guidance_scale=7.5			,    sag_scale=1.0			,    num_inference_steps=2_0			,    output_type='''np''')

         A__					=       output.images

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         A__					=       np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1])

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




     def 		snake_case_		(					self):
         A__					=       StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
         A__					=       sag_pipe.to(a__)
         sag_pipe.set_progress_bar_config(disable=a__)

         A__					=       '''.'''
         A__					=       torch.manual_seed(0)
         A__					=       sag_pipe(
             [prompt]			,    width=7_6_8			,    height=5_1_2			,    generator=a__			,    guidance_scale=7.5			,    sag_scale=1.0			,    num_inference_steps=2_0			,    output_type='''np'''			,    )

         A__					=       output.images

         assert image.shape == (1, 5_1_2, 7_6_8, 3)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_upernet": ["UperNetConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "UperNetForSemanticSegmentation",
       "UperNetPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_upernet import UperNetConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
