'''simple docstring'''


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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						StableDiffusionModelEditingPipeline
  lowerCamelCase_						:List[str]		       =						TEXT_TO_IMAGE_PARAMS
  lowerCamelCase_						:Any		       =						TEXT_TO_IMAGE_BATCH_PARAMS
  lowerCamelCase_						:Union[str, Any]		       =						TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCamelCase_						:List[str]		       =						TEXT_TO_IMAGE_IMAGE_PARAMS


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(0		)
       UpperCAmelCase_				:  str  = 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 ,						)
       UpperCAmelCase_				:  List[str]  = DDIMScheduler()
       torch.manual_seed(0		)
       UpperCAmelCase_				:  List[str]  = 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		)
       UpperCAmelCase_				:  int  = 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 ,						)
       UpperCAmelCase_				:  int  = CLIPTextModel(snake_case_		)
       UpperCAmelCase_				:  Any  = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

       UpperCAmelCase_				:  str  = {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_=0		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = torch.manual_seed(snake_case_		)
       UpperCAmelCase_				:  int  = {
           '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		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  Optional[int]  = self.get_dummy_components()
       UpperCAmelCase_				:  List[str]  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Union[str, Any]  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  Any  = sd_pipe(**snake_case_		).images
       UpperCAmelCase_				:  Dict  = image[0, -3:, -3:, -1]
       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCAmelCase_				:  str  = 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		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  int  = self.get_dummy_components()
       UpperCAmelCase_				:  Dict  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Tuple  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Any  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  Any  = 'french fries'
       UpperCAmelCase_				:  Tuple  = sd_pipe(**snake_case_ ,						negative_prompt=snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = output.images
       UpperCAmelCase_				:  List[Any]  = image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCAmelCase_				:  List[Any]  = 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		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  Tuple  = self.get_dummy_components()
       UpperCAmelCase_				:  Optional[int]  = EulerAncestralDiscreteScheduler(
           beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear'		)
       UpperCAmelCase_				:  Optional[int]  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Optional[Any]  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = sd_pipe(**snake_case_		).images
       UpperCAmelCase_				:  Optional[Any]  = image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCAmelCase_				:  List[Any]  = 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		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  Optional[int]  = self.get_dummy_components()
       UpperCAmelCase_				:  Optional[Any]  = PNDMScheduler()
       UpperCAmelCase_				:  List[str]  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Tuple  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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







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







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_=0		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = torch.manual_seed(snake_case_		)
       UpperCAmelCase_				:  Any  = {
           'prompt': 'A field of roses',
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = 'CompVis/stable-diffusion-v1-4'
       UpperCAmelCase_				:  Any  = StableDiffusionModelEditingPipeline.from_pretrained(snake_case_ ,						safety_checker=snake_case_		)
       pipe.to(snake_case_		)
       pipe.set_progress_bar_config(disable=snake_case_		)
       pipe.enable_attention_slicing()

       UpperCAmelCase_				:  str  = self.get_inputs()
       UpperCAmelCase_				:  List[str]  = pipe(**snake_case_		).images
       UpperCAmelCase_				:  Any  = image[0, -3:, -3:, -1].flatten()

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

       UpperCAmelCase_				:  str  = 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'		)

       UpperCAmelCase_				:  Union[str, Any]  = pipe(**snake_case_		).images
       UpperCAmelCase_				:  Optional[int]  = image[0, -3:, -3:, -1].flatten()

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

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






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Optional[Any]  = 'CompVis/stable-diffusion-v1-4'
       UpperCAmelCase_				:  str  = DDIMScheduler.from_pretrained(snake_case_ ,						subfolder='scheduler'		)
       UpperCAmelCase_				:  Dict  = StableDiffusionModelEditingPipeline.from_pretrained(
           snake_case_ ,						scheduler=snake_case_ ,						safety_checker=snake_case_		)
       UpperCAmelCase_				:  List[Any]  = pipe.to(snake_case_		)
       pipe.set_progress_bar_config(disable=snake_case_		)
       pipe.enable_attention_slicing(1		)
       pipe.enable_sequential_cpu_offload()

       UpperCAmelCase_				:  Optional[int]  = self.get_inputs()
       UpperCAmelCase_				:  Any  = pipe(**snake_case_		)

       UpperCAmelCase_				:  str  = torch.cuda.max_memory_allocated()
       # make sure that less than 4.4 GB is allocated
       assert mem_bytes < 4.4 * 1_0**9














"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

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


