




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 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= StableDiffusionModelEditingPipeline
					__UpperCamelCase     	= TEXT_TO_IMAGE_PARAMS
					__UpperCamelCase     	= TEXT_TO_IMAGE_BATCH_PARAMS
					__UpperCamelCase     	= TEXT_TO_IMAGE_IMAGE_PARAMS
					__UpperCamelCase     	= TEXT_TO_IMAGE_IMAGE_PARAMS




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									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()
									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=1000,     )
									__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     _lowercase						(   self:		List[str],     _lowercase:		Tuple,     _lowercase:		Union[str, Any]=0):


									'''simple docstring'''


									__lowerCAmelCase					=  torch.manual_seed(_lowercase)
									__lowerCAmelCase					=  {
									    """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     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									__lowerCAmelCase					=  sd_pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									__lowerCAmelCase					=  np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889])

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




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									__lowerCAmelCase					=  """french fries"""
									__lowerCAmelCase					=  sd_pipe(**_lowercase,     negative_prompt=_lowercase)
									__lowerCAmelCase					=  output.images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1]

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

									__lowerCAmelCase					=  np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913])

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




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  EulerAncestralDiscreteScheduler(
									    beta_start=0.00_085,     beta_end=0.012,     beta_schedule="""scaled_linear""")
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									__lowerCAmelCase					=  sd_pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1]

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

									__lowerCAmelCase					=  np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027])

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




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowerCAmelCase					=  self.get_dummy_components()
									__lowerCAmelCase					=  PNDMScheduler()
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline(**_lowercase)
									__lowerCAmelCase					=  sd_pipe.to(_lowercase)
									sd_pipe.set_progress_bar_config(disable=_lowercase)

									__lowerCAmelCase					=  self.get_dummy_inputs(_lowercase)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(_lowercase):
													__lowerCAmelCase					=  sd_pipe(**_lowercase).images




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									super().test_inference_batch_single_identical(expected_max_diff=5e-3)







					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)





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




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




					def     _lowercase						(   self:		str,     _lowercase:		Tuple=0):


									'''simple docstring'''


									__lowerCAmelCase					=  torch.manual_seed(_lowercase)
									__lowerCAmelCase					=  {
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  """CompVis/stable-diffusion-v1-4"""
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline.from_pretrained(_lowercase,     safety_checker=_lowercase)
									pipe.to(_lowercase)
									pipe.set_progress_bar_config(disable=_lowercase)
									pipe.enable_attention_slicing()

									__lowerCAmelCase					=  self.get_inputs()
									__lowerCAmelCase					=  pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1].flatten()

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

									__lowerCAmelCase					=  np.array(
									    [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658])

									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""")

									__lowerCAmelCase					=  pipe(**_lowercase).images
									__lowerCAmelCase					=  image[0, -3:, -3:, -1].flatten()

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

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







					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									__lowerCAmelCase					=  """CompVis/stable-diffusion-v1-4"""
									__lowerCAmelCase					=  DDIMScheduler.from_pretrained(_lowercase,     subfolder="""scheduler""")
									__lowerCAmelCase					=  StableDiffusionModelEditingPipeline.from_pretrained(
									    _lowercase,     scheduler=_lowercase,     safety_checker=_lowercase)
									__lowerCAmelCase					=  pipe.to(_lowercase)
									pipe.set_progress_bar_config(disable=_lowercase)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									__lowerCAmelCase					=  self.get_inputs()
									__lowerCAmelCase					=  pipe(**_lowercase)

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













"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Tuple      				=			{"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Tuple      				=			["""DetrFeatureExtractor"""]
    UpperCamelCase_						:					int      				=			["""DetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Dict      				=			[
        """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DetrForObjectDetection""",
        """DetrForSegmentation""",
        """DetrModel""",
        """DetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					List[str]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


