import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



@skip_mps
class 						snake_case_ (	__A						,__A						,__A						,unittest.TestCase ):
   __A			:	List[Any]        =       StableDiffusionModelEditingPipeline
   __A			:	List[str]        =       TEXT_TO_IMAGE_PARAMS
   __A			:	Optional[int]        =       TEXT_TO_IMAGE_BATCH_PARAMS
   __A			:	Dict        =       TEXT_TO_IMAGE_IMAGE_PARAMS
   __A			:	Dict        =       TEXT_TO_IMAGE_IMAGE_PARAMS






   def    __UpperCamelCase  (   self       :  int						)      ->		str:
       torch.manual_seed(0						)
       lowercase__				:     List[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			,		)
       lowercase__				:     int					      =       DDIMScheduler()
       torch.manual_seed(0						)
       lowercase__				:     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						)
       lowercase__				:     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=10_00			,		)
       lowercase__				:     int					      =       CLIPTextModel(lowercase_						)
       lowercase__				:     Optional[Any]					      =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

       lowercase__				:     Union[str, Any]					      =       {
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  Optional[int]			,		lowercase_       :  List[Any]=0						)      ->		Optional[Any]:
       lowercase__				:     Tuple					      =       torch.manual_seed(lowercase_						)
       lowercase__				:     str					      =       {
           "prompt": "A field of roses",
           "generator": generator,
           # Setting height and width to None to prevent OOMs on CPU.
           "height": None,
           "width": None,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
       }
       return inputs






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Any:
       lowercase__				:     Optional[int]					      =       "cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__				:     List[str]					      =       self.get_dummy_components()
       lowercase__				:     Union[str, Any]					      =       StableDiffusionModelEditingPipeline(**lowercase_						)
       lowercase__				:     Optional[Any]					      =       sd_pipe.to(lowercase_						)
       sd_pipe.set_progress_bar_config(disable=lowercase_						)

       lowercase__				:     str					      =       self.get_dummy_inputs(lowercase_						)
       lowercase__				:     Tuple					      =       sd_pipe(**lowercase_						).images
       lowercase__				:     Dict					      =       image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       lowercase__				:     Optional[Any]					      =       np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2






   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Dict:
       lowercase__				:     Optional[Any]					      =       "cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__				:     Union[str, Any]					      =       self.get_dummy_components()
       lowercase__				:     Optional[int]					      =       StableDiffusionModelEditingPipeline(**lowercase_						)
       lowercase__				:     List[str]					      =       sd_pipe.to(lowercase_						)
       sd_pipe.set_progress_bar_config(disable=lowercase_						)

       lowercase__				:     List[Any]					      =       self.get_dummy_inputs(lowercase_						)
       lowercase__				:     Optional[Any]					      =       "french fries"
       lowercase__				:     Any					      =       sd_pipe(**lowercase_			,		negative_prompt=lowercase_						)
       lowercase__				:     List[Any]					      =       output.images
       lowercase__				:     Any					      =       image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       lowercase__				:     int					      =       np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2






   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       lowercase__				:     Optional[int]					      =       "cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__				:     str					      =       self.get_dummy_components()
       lowercase__				:     Tuple					      =       EulerAncestralDiscreteScheduler(
           beta_start=0.0_00_85			,		beta_end=0.0_12			,		beta_schedule="scaled_linear"						)
       lowercase__				:     Union[str, Any]					      =       StableDiffusionModelEditingPipeline(**lowercase_						)
       lowercase__				:     List[Any]					      =       sd_pipe.to(lowercase_						)
       sd_pipe.set_progress_bar_config(disable=lowercase_						)

       lowercase__				:     Tuple					      =       self.get_dummy_inputs(lowercase_						)
       lowercase__				:     Union[str, Any]					      =       sd_pipe(**lowercase_						).images
       lowercase__				:     int					      =       image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       lowercase__				:     List[str]					      =       np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		List[str]:
       lowercase__				:     Any					      =       "cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__				:     int					      =       self.get_dummy_components()
       lowercase__				:     Any					      =       PNDMScheduler()
       lowercase__				:     Tuple					      =       StableDiffusionModelEditingPipeline(**lowercase_						)
       lowercase__				:     Tuple					      =       sd_pipe.to(lowercase_						)
       sd_pipe.set_progress_bar_config(disable=lowercase_						)

       lowercase__				:     List[Any]					      =       self.get_dummy_inputs(lowercase_						)
       # the pipeline does not expect pndm so test if it raises error.
       with self.assertRaises(lowercase_						):
           lowercase__				:     Optional[int]					      =       sd_pipe(**lowercase_						).images






   def    __UpperCamelCase  (   self       :  Dict						)      ->		int:
       super().test_inference_batch_single_identical(expected_max_diff=5E-3						)







   def    __UpperCamelCase  (   self       :  Any						)      ->		Union[str, Any]:
       super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)



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






   def    __UpperCamelCase  (   self       :  Any						)      ->		Dict:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  List[str]=0						)      ->		List[Any]:
       lowercase__				:     Optional[int]					      =       torch.manual_seed(lowercase_						)
       lowercase__				:     List[Any]					      =       {
           "prompt": "A field of roses",
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		int:
       lowercase__				:     Tuple					      =       "CompVis/stable-diffusion-v1-4"
       lowercase__				:     str					      =       StableDiffusionModelEditingPipeline.from_pretrained(lowercase_			,		safety_checker=lowercase_						)
       pipe.to(lowercase_						)
       pipe.set_progress_bar_config(disable=lowercase_						)
       pipe.enable_attention_slicing()

       lowercase__				:     Any					      =       self.get_inputs()
       lowercase__				:     Dict					      =       pipe(**lowercase_						).images
       lowercase__				:     List[Any]					      =       image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 5_12, 5_12, 3)

       lowercase__				:     Any					      =       np.array(
           [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

       assert np.abs(expected_slice - image_slice						).max() < 1E-2

       # make sure image changes after editing
       pipe.edit_model("A pack of roses"			,		"A pack of blue roses"						)

       lowercase__				:     str					      =       pipe(**lowercase_						).images
       lowercase__				:     Dict					      =       image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 5_12, 5_12, 3)

       assert np.abs(expected_slice - image_slice						).max() > 1E-1







   def    __UpperCamelCase  (   self       :  Tuple						)      ->		int:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowercase__				:     Optional[Any]					      =       "CompVis/stable-diffusion-v1-4"
       lowercase__				:     Any					      =       DDIMScheduler.from_pretrained(lowercase_			,		subfolder="scheduler"						)
       lowercase__				:     Union[str, Any]					      =       StableDiffusionModelEditingPipeline.from_pretrained(
           lowercase_			,		scheduler=lowercase_			,		safety_checker=lowercase_						)
       lowercase__				:     Optional[Any]					      =       pipe.to(lowercase_						)
       pipe.set_progress_bar_config(disable=lowercase_						)
       pipe.enable_attention_slicing(1						)
       pipe.enable_sequential_cpu_offload()

       lowercase__				:     Tuple					      =       self.get_inputs()
       lowercase__				:     str					      =       pipe(**lowercase_						)

       lowercase__				:     int					      =       torch.cuda.max_memory_allocated()
       # make sure that less than 4.4 GB is allocated
       assert mem_bytes < 4.4 * 10**9




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

      UpperCamelCase													=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



