



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     _A       (	__UpperCAmelCase      ,__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    str											=						StableDiffusionDiffEditPipeline
       UpperCamelCase__  :    List[Any]											=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
       UpperCamelCase__  :    List[Any]											=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
       UpperCamelCase__  :    List[str]											=						frozenset(
           []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCamelCase__  :    Any											=						frozenset([]    )



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           torch.manual_seed(0)
           __a						       =				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=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				DDIMScheduler(
               beta_start=0.0_00_85     ,		beta_end=0.0_12     ,		beta_schedule='''scaled_linear'''     ,		clip_sample=__SCREAMING_SNAKE_CASE     ,		set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				DDIMInverseScheduler(
               beta_start=0.0_00_85     ,		beta_end=0.0_12     ,		beta_schedule='''scaled_linear'''     ,		clip_sample=__SCREAMING_SNAKE_CASE     ,		set_alpha_to_zero=__SCREAMING_SNAKE_CASE     ,		)
           torch.manual_seed(0)
           __a						       =				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=128     ,		)
           torch.manual_seed(0)
           __a						       =				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=1_000     ,		hidden_act='''gelu'''     ,		projection_dim=512     ,		)
           __a						       =				CLIPTextModel(__SCREAMING_SNAKE_CASE)
           __a						       =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

           __a						       =				{
               '''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 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0):


           '''simple docstring'''

           __a						       =				floats_tensor((1, 16, 16)     ,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a						       =				floats_tensor((1, 2, 4, 16, 16)     ,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a						       =				torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a						       =				{
               '''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 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0):


           '''simple docstring'''

           __a						       =				floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a						       =				image.cpu().permute(0     ,		2     ,		3     ,		1)[0]
           __a						       =				Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''')
           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a						       =				torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a						       =				{
               '''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 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0):


           '''simple docstring'''

           __a						       =				floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a						       =				image.cpu().permute(0     ,		2     ,		3     ,		1)[0]
           __a						       =				Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''')
           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a						       =				torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a						       =				{
               '''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 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

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

           __a						       =				self.get_dummy_components()
           __a						       =				self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

           __a						       =				self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
           __a						       =				pipe(**__SCREAMING_SNAKE_CASE)[0]

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

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

           __a						       =				self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
           __a						       =				pipe_loaded(**__SCREAMING_SNAKE_CASE)[0]

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



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				'''cpu'''

           __a						       =				self.get_dummy_components()
           __a						       =				self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

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



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				'''cpu'''

           __a						       =				self.get_dummy_components()
           __a						       =				self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

           self.assertEqual(image.shape     ,		(2, 32, 32, 3))
           __a						       =				np.array(
               [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]     ,		)
           __a						       =				np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,		1E-3)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

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





       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				'''cpu'''

           __a						       =				self.get_dummy_components()

           __a						       =				{'''beta_start''': 0.0_00_85, '''beta_end''': 0.0_12, '''beta_schedule''': '''scaled_linear'''}
           __a						       =				DPMSolverMultistepScheduler(**__SCREAMING_SNAKE_CASE)
           __a						       =				DPMSolverMultistepInverseScheduler(**__SCREAMING_SNAKE_CASE)

           __a						       =				self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

           self.assertEqual(image.shape     ,		(2, 32, 32, 3))
           __a						       =				np.array(
               [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]     ,		)
           __a						       =				np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,		1E-3)


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



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

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



       @classmethod
       def 		_lowerCamelCase			(		cls							:     str):


           '''simple docstring'''

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

           __a						       =				raw_image.convert('''RGB''').resize((768, 768))

           __a						       =				raw_image



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				torch.manual_seed(0)

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

           __a						       =				'''a bowl of fruit'''
           __a						       =				'''a bowl of pears'''

           __a						       =				pipe.generate_mask(
               image=self.raw_image     ,		source_prompt=__SCREAMING_SNAKE_CASE     ,		target_prompt=__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE     ,		)

           __a						       =				pipe.invert(
               prompt=__SCREAMING_SNAKE_CASE     ,		image=self.raw_image     ,		inpaint_strength=0.7     ,		generator=__SCREAMING_SNAKE_CASE).latents

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

           __a						       =				(
               np.array(
                   load_image(
                       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                       '''/diffedit/pears.png''').resize((768, 768)))
               / 255
           )
           assert np.abs((expected_image - image).max()) < 5E-1





       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				torch.manual_seed(0)

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

           __a						       =				'''a bowl of fruit'''
           __a						       =				'''a bowl of pears'''

           __a						       =				pipe.generate_mask(
               image=self.raw_image     ,		source_prompt=__SCREAMING_SNAKE_CASE     ,		target_prompt=__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE     ,		)

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

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

           __a						       =				(
               np.array(
                   load_image(
                       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                       '''/diffedit/pears.png''').resize((768, 768)))
               / 255
           )
           assert np.abs((expected_image - image).max()) < 5E-1










import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				LxmertConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    __a						       =				LxmertForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    torch.save(model.state_dict()   , _UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:List[Any]      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __snake_case						:Optional[Any]      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





