





import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		str				=	StableDiffusionDiffEditPipeline
    lowerCamelCase__    :		List[Any]				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
    lowerCamelCase__    :		str				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
    lowerCamelCase__    :		str				=	frozenset(
        []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowerCamelCase__    :		Union[str, Any]				=	frozenset([]    )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        torch.manual_seed(0     )
        SCREAMING_SNAKE_CASE__											=			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   ,				attention_head_dim=(2, 4)   ,				use_linear_projection=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			DDIMScheduler(
            beta_start=0.00085   ,				beta_end=0.012   ,				beta_schedule='''scaled_linear'''   ,				clip_sample=A_   ,				set_alpha_to_one=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			DDIMInverseScheduler(
            beta_start=0.00085   ,				beta_end=0.012   ,				beta_schedule='''scaled_linear'''   ,				clip_sample=A_   ,				set_alpha_to_zero=A_   ,				)
        torch.manual_seed(0     )
        SCREAMING_SNAKE_CASE__											=			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   ,				sample_size=1_28   ,				)
        torch.manual_seed(0     )
        SCREAMING_SNAKE_CASE__											=			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   ,				hidden_act='''gelu'''   ,				projection_dim=5_12   ,				)
        SCREAMING_SNAKE_CASE__											=			CLIPTextModel(A_     )
        SCREAMING_SNAKE_CASE__											=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

        SCREAMING_SNAKE_CASE__											=			{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''inverse_scheduler''': inverse_scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }

        return components



    def  lowercase_					(       self   ,				A_   ,				A_=0     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 16, 16)   ,				rng=random.Random(A_     )     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 2, 4, 16, 16)   ,				rng=random.Random(A_     )     ).to(A_     )
        if str(A_     ).startswith('''mps'''     ):
            SCREAMING_SNAKE_CASE__											=			torch.manual_seed(A_     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.Generator(device=A_     ).manual_seed(A_     )
        SCREAMING_SNAKE_CASE__											=			{
            '''prompt''': '''a dog and a newt''',
            '''mask_image''': mask,
            '''image_latents''': latents,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs



    def  lowercase_					(       self   ,				A_   ,				A_=0     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(A_     )     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			image.cpu().permute(0   ,				2   ,				3   ,				1     )[0]
        SCREAMING_SNAKE_CASE__											=			Image.fromarray(np.uinta(A_     )     ).convert('''RGB'''     )
        if str(A_     ).startswith('''mps'''     ):
            SCREAMING_SNAKE_CASE__											=			torch.manual_seed(A_     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.Generator(device=A_     ).manual_seed(A_     )
        SCREAMING_SNAKE_CASE__											=			{
            '''image''': image,
            '''source_prompt''': '''a cat and a frog''',
            '''target_prompt''': '''a dog and a newt''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''num_maps_per_mask''': 2,
            '''mask_encode_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return inputs



    def  lowercase_					(       self   ,				A_   ,				A_=0     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(A_     )     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			image.cpu().permute(0   ,				2   ,				3   ,				1     )[0]
        SCREAMING_SNAKE_CASE__											=			Image.fromarray(np.uinta(A_     )     ).convert('''RGB'''     )
        if str(A_     ).startswith('''mps'''     ):
            SCREAMING_SNAKE_CASE__											=			torch.manual_seed(A_     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.Generator(device=A_     ).manual_seed(A_     )
        SCREAMING_SNAKE_CASE__											=			{
            '''image''': image,
            '''prompt''': '''a cat and a frog''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''inpaint_strength''': 1.0,
            '''guidance_scale''': 6.0,
            '''decode_latents''': True,
            '''output_type''': '''numpy''',
        }
        return inputs



    def  lowercase_					(       self     ):





        '''simple docstring'''

        if not hasattr(self.pipeline_class   ,				'''_optional_components'''     ):
            return

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_components()
        SCREAMING_SNAKE_CASE__											=			self.pipeline_class(**A_     )
        pipe.to(A_     )
        pipe.set_progress_bar_config(disable=A_     )

        # set all optional components to None and update pipeline config accordingly
        for optional_component in pipe._optional_components:
            setattr(A_   ,				A_   ,				A_     )
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}     )

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs(A_     )
        SCREAMING_SNAKE_CASE__											=			pipe(**A_     )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(A_     )
            SCREAMING_SNAKE_CASE__											=			self.pipeline_class.from_pretrained(A_     )
            pipe_loaded.to(A_     )
            pipe_loaded.set_progress_bar_config(disable=A_     )

        for optional_component in pipe._optional_components:
            self.assertTrue(
                getattr(A_   ,				A_     ) is None   ,				f'''`{optional_component}` did not stay set to None after loading.'''   ,				)

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs(A_     )
        SCREAMING_SNAKE_CASE__											=			pipe_loaded(**A_     )[0]

        SCREAMING_SNAKE_CASE__											=			np.abs(output - output_loaded     ).max()
        self.assertLess(A_   ,				1E-4     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''cpu'''

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_components()
        SCREAMING_SNAKE_CASE__											=			self.pipeline_class(**A_     )
        pipe.to(A_     )
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_mask_inputs(A_     )
        SCREAMING_SNAKE_CASE__											=			pipe.generate_mask(**A_     )
        SCREAMING_SNAKE_CASE__											=			mask[0, -3:, -3:]

        self.assertEqual(mask.shape   ,				(1, 16, 16)     )
        SCREAMING_SNAKE_CASE__											=			np.array([0] * 9     )
        SCREAMING_SNAKE_CASE__											=			np.abs(mask_slice.flatten() - expected_slice     ).max()
        self.assertLessEqual(A_   ,				1E-3     )
        self.assertEqual(mask[0, -3, -4]   ,				0     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''cpu'''

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_components()
        SCREAMING_SNAKE_CASE__											=			self.pipeline_class(**A_     )
        pipe.to(A_     )
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inversion_inputs(A_     )
        SCREAMING_SNAKE_CASE__											=			pipe.invert(**A_     ).images
        SCREAMING_SNAKE_CASE__											=			image[0, -1, -3:, -3:]

        self.assertEqual(image.shape   ,				(2, 32, 32, 3)     )
        SCREAMING_SNAKE_CASE__											=			np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,				)
        SCREAMING_SNAKE_CASE__											=			np.abs(image_slice.flatten() - expected_slice     ).max()
        self.assertLessEqual(A_   ,				1E-3     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

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




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''cpu'''

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_components()

        SCREAMING_SNAKE_CASE__											=			{'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
        SCREAMING_SNAKE_CASE__											=			DPMSolverMultistepScheduler(**A_     )
        SCREAMING_SNAKE_CASE__											=			DPMSolverMultistepInverseScheduler(**A_     )

        SCREAMING_SNAKE_CASE__											=			self.pipeline_class(**A_     )
        pipe.to(A_     )
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inversion_inputs(A_     )
        SCREAMING_SNAKE_CASE__											=			pipe.invert(**A_     ).images
        SCREAMING_SNAKE_CASE__											=			image[0, -1, -3:, -3:]

        self.assertEqual(image.shape   ,				(2, 32, 32, 3)     )
        SCREAMING_SNAKE_CASE__											=			np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,				)
        SCREAMING_SNAKE_CASE__											=			np.abs(image_slice.flatten() - expected_slice     ).max()
        self.assertLessEqual(A_   ,				1E-3     )





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


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    @classmethod
    def  lowercase_					(       cls     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''     )

        SCREAMING_SNAKE_CASE__											=			raw_image.convert('''RGB'''     ).resize((7_68, 7_68)     )

        SCREAMING_SNAKE_CASE__											=			raw_image



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch.manual_seed(0     )

        SCREAMING_SNAKE_CASE__											=			StableDiffusionDiffEditPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2-1'''   ,				safety_checker=A_   ,				torch_dtype=torch.floataa     )
        SCREAMING_SNAKE_CASE__											=			DDIMScheduler.from_config(pipe.scheduler.config     )
        SCREAMING_SNAKE_CASE__											=			DDIMInverseScheduler.from_config(pipe.scheduler.config     )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			'''a bowl of fruit'''
        SCREAMING_SNAKE_CASE__											=			'''a bowl of pears'''

        SCREAMING_SNAKE_CASE__											=			pipe.generate_mask(
            image=self.raw_image   ,				source_prompt=A_   ,				target_prompt=A_   ,				generator=A_   ,				)

        SCREAMING_SNAKE_CASE__											=			pipe.invert(
            prompt=A_   ,				image=self.raw_image   ,				inpaint_strength=0.7   ,				generator=A_     ).latents

        SCREAMING_SNAKE_CASE__											=			pipe(
            prompt=A_   ,				mask_image=A_   ,				image_latents=A_   ,				generator=A_   ,				negative_prompt=A_   ,				inpaint_strength=0.7   ,				output_type='''numpy'''   ,				).images[0]

        SCREAMING_SNAKE_CASE__											=			(
            np.array(
                load_image(
                    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                    '''/diffedit/pears.png'''     ).resize((7_68, 7_68)     )     )
            / 2_55
        )
        assert np.abs((expected_image - image).max()     ) < 5E-1




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch.manual_seed(0     )

        SCREAMING_SNAKE_CASE__											=			StableDiffusionDiffEditPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2-1'''   ,				safety_checker=A_   ,				torch_dtype=torch.floataa     )
        SCREAMING_SNAKE_CASE__											=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
        SCREAMING_SNAKE_CASE__											=			DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config     )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			'''a bowl of fruit'''
        SCREAMING_SNAKE_CASE__											=			'''a bowl of pears'''

        SCREAMING_SNAKE_CASE__											=			pipe.generate_mask(
            image=self.raw_image   ,				source_prompt=A_   ,				target_prompt=A_   ,				generator=A_   ,				)

        SCREAMING_SNAKE_CASE__											=			pipe.invert(
            prompt=A_   ,				image=self.raw_image   ,				inpaint_strength=0.7   ,				generator=A_   ,				num_inference_steps=25   ,				).latents

        SCREAMING_SNAKE_CASE__											=			pipe(
            prompt=A_   ,				mask_image=A_   ,				image_latents=A_   ,				generator=A_   ,				negative_prompt=A_   ,				inpaint_strength=0.7   ,				num_inference_steps=25   ,				output_type='''numpy'''   ,				).images[0]

        SCREAMING_SNAKE_CASE__											=			(
            np.array(
                load_image(
                    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                    '''/diffedit/pears.png'''     ).resize((7_68, 7_68)     )     )
            / 2_55
        )
        assert np.abs((expected_image - image).max()     ) < 5E-1







from typing import TYPE_CHECKING

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


_A					:				Any				=			{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Optional[Any]				=			["""XGLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Any				=			["""XGLMTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				str				=			[
      """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XGLMForCausalLM""",
      """XGLMModel""",
      """XGLMPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Tuple				=			[
      """FlaxXGLMForCausalLM""",
      """FlaxXGLMModel""",
      """FlaxXGLMPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				List[Any]				=			[
      """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXGLMForCausalLM""",
      """TFXGLMModel""",
      """TFXGLMPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm import XGLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm_fast import XGLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
        TFXGLMPreTrainedModel,
    )


else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
