




"""simple docstring"""

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     lowercase_	(					a__      ,     a__      ,     unittest.TestCase							):



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

 def       lowerCAmelCase_							(			self	:	Any		):
       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=_UpperCAmelCase   ,							)
       _A        =       DDIMScheduler(
           beta_start=0.0_0085   ,							beta_end=0.012   ,							beta_schedule='scaled_linear'   ,							clip_sample=_UpperCAmelCase   ,							set_alpha_to_one=_UpperCAmelCase   ,							)
       _A        =       DDIMInverseScheduler(
           beta_start=0.0_0085   ,							beta_end=0.012   ,							beta_schedule='scaled_linear'   ,							clip_sample=_UpperCAmelCase   ,							set_alpha_to_zero=_UpperCAmelCase   ,							)
       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-0_5   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=1_000   ,							hidden_act='gelu'   ,							projection_dim=512   ,							)
       _A        =       CLIPTextModel(_UpperCAmelCase		)
       _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	:	Any   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	List[Any]=0		):
       _A        =       floats_tensor((1, 16, 16)   ,							rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
       _A        =       floats_tensor((1, 2, 4, 16, 16)   ,							rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
       if str(_UpperCAmelCase		).startswith('mps'		):
             _A        =       torch.manual_seed(_UpperCAmelCase		)
       else:
             _A        =       torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
       _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	:	List[Any]   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Dict=0		):
       _A        =       floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
       _A        =       image.cpu().permute(0   ,							2   ,							3   ,							1		)[0]
       _A        =       Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert('RGB'		)
       if str(_UpperCAmelCase		).startswith('mps'		):
             _A        =       torch.manual_seed(_UpperCAmelCase		)
       else:
             _A        =       torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
       _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	:	str   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Any=0		):
       _A        =       floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
       _A        =       image.cpu().permute(0   ,							2   ,							3   ,							1		)[0]
       _A        =       Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert('RGB'		)
       if str(_UpperCAmelCase		).startswith('mps'		):
             _A        =       torch.manual_seed(_UpperCAmelCase		)
       else:
             _A        =       torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
       _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	:	List[str]		):
       if not hasattr(self.pipeline_class   ,							'_optional_components'		):
             return

       _A        =       self.get_dummy_components()
       _A        =       self.pipeline_class(**_UpperCAmelCase		)
       pipe.to(_UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

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

       _A        =       self.get_dummy_inputs(_UpperCAmelCase		)
       _A        =       pipe(**_UpperCAmelCase		)[0]

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

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

       _A        =       self.get_dummy_inputs(_UpperCAmelCase		)
       _A        =       pipe_loaded(**_UpperCAmelCase		)[0]

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

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       "cpu"

       _A        =       self.get_dummy_components()
       _A        =       self.pipeline_class(**_UpperCAmelCase		)
       pipe.to(_UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

       _A        =       self.get_dummy_mask_inputs(_UpperCAmelCase		)
       _A        =       pipe.generate_mask(**_UpperCAmelCase		)
       _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(_UpperCAmelCase   ,							1E-3		)
       self.assertEqual(mask[0, -3, -4]   ,							0		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       "cpu"

       _A        =       self.get_dummy_components()
       _A        =       self.pipeline_class(**_UpperCAmelCase		)
       pipe.to(_UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

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

       self.assertEqual(image.shape   ,							(2, 32, 32, 3)		)
       _A        =       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]   ,							)
       _A        =       np.abs(image_slice.flatten() - expected_slice		).max()
       self.assertLessEqual(_UpperCAmelCase   ,							1E-3		)

 def       lowerCAmelCase_							(			self	:	str		):
       super().test_inference_batch_single_identical(expected_max_diff=5E-3		)



 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       "cpu"

       _A        =       self.get_dummy_components()

       _A        =       {"beta_start": 0.0_0085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
       _A        =       DPMSolverMultistepScheduler(**_UpperCAmelCase		)
       _A        =       DPMSolverMultistepInverseScheduler(**_UpperCAmelCase		)

       _A        =       self.pipeline_class(**_UpperCAmelCase		)
       pipe.to(_UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

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

       self.assertEqual(image.shape   ,							(2, 32, 32, 3)		)
       _A        =       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]   ,							)
       _A        =       np.abs(image_slice.flatten() - expected_slice		).max()
       self.assertLessEqual(_UpperCAmelCase   ,							1E-3		)





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



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Tuple		):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 @classmethod
 def       lowerCAmelCase_							(			cls	:	Optional[Any]		):
       _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	:	List[str]		):
       _A        =       torch.manual_seed(0		)

       _A        =       StableDiffusionDiffEditPipeline.from_pretrained(
           'stabilityai/stable-diffusion-2-1'   ,							safety_checker=_UpperCAmelCase   ,							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=_UpperCAmelCase		)

       _A        =       "a bowl of fruit"
       _A        =       "a bowl of pears"

       _A        =       pipe.generate_mask(
           image=self.raw_image   ,							source_prompt=_UpperCAmelCase   ,							target_prompt=_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							)

       _A        =       pipe.invert(
           prompt=_UpperCAmelCase   ,							image=self.raw_image   ,							inpaint_strength=0.7   ,							generator=_UpperCAmelCase		).latents

       _A        =       pipe(
           prompt=_UpperCAmelCase   ,							mask_image=_UpperCAmelCase   ,							image_latents=_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							negative_prompt=_UpperCAmelCase   ,							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	:	Tuple		):
       _A        =       torch.manual_seed(0		)

       _A        =       StableDiffusionDiffEditPipeline.from_pretrained(
           'stabilityai/stable-diffusion-2-1'   ,							safety_checker=_UpperCAmelCase   ,							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=_UpperCAmelCase		)

       _A        =       "a bowl of fruit"
       _A        =       "a bowl of pears"

       _A        =       pipe.generate_mask(
           image=self.raw_image   ,							source_prompt=_UpperCAmelCase   ,							target_prompt=_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							)

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

       _A        =       pipe(
           prompt=_UpperCAmelCase   ,							mask_image=_UpperCAmelCase   ,							image_latents=_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							negative_prompt=_UpperCAmelCase   ,							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







"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 						_snake_case					( _snake_case							: Dict							)   -> Any:






      '''simple docstring'''


      if (
          (cp >= 0X4e00 and cp <= 0X9fff)
          or (cp >= 0X3400 and cp <= 0X4dbf)  #
          or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
          or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
          or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
          or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
          or (cp >= 0Xf900 and cp <= 0Xfaff)
          or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
      ):  #
            return True

      return False






def 						_snake_case					( _snake_case							: str							)   -> Tuple:






      '''simple docstring'''


      for char in word:
            _A        =       ord(_snake_case							)
            if not _is_chinese_char(_snake_case							):
                  return 0
      return 1






def 						_snake_case					( _snake_case							: List[str]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       set()

      for token in tokens:
            _A        =       len(_snake_case							) > 1 and is_chinese(_snake_case							)
            if chinese_word:
                  word_set.add(_snake_case							)
      _A        =       list(_snake_case							)
      return word_list






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: set()							)   -> Optional[Any]:






      '''simple docstring'''


      if not chinese_word_set:
            return bert_tokens
      _A        =       max([len(_snake_case							) for w in chinese_word_set]							)

      _A        =       bert_tokens
      _A       ,   _A        =       0, len(_snake_case							)
      while start < end:
            _A        =       True
            if is_chinese(bert_word[start]							):
                  _A        =       min(end - start    ,  _snake_case							)
                  for i in range(_snake_case    ,  1    ,  -1							):
                        _A        =       ''.join(bert_word[start : start + i]							)
                        if whole_word in chinese_word_set:
                              for j in range(start + 1    ,  start + i							):
                                    _A        =       '##' + bert_word[j]
                              _A        =       start + i
                              _A        =       False
                              break
            if single_word:
                  start += 1
      return bert_word






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: LTP    ,  _snake_case							: BertTokenizer							)   -> str:






      '''simple docstring'''


      _A        =       []

      for i in range(0    ,  len(_snake_case							)    ,  1_00							):
            _A        =       ltp_tokenizer.seg(lines[i : i + 1_00]							)[0]
            _A        =       [get_chinese_word(_snake_case							) for r in res]
            ltp_res.extend(_snake_case							)
      assert len(_snake_case							) == len(_snake_case							)

      _A        =       []
      for i in range(0    ,  len(_snake_case							)    ,  1_00							):
            _A        =       bert_tokenizer(lines[i : i + 1_00]    ,  add_special_tokens=_snake_case    ,  truncation=_snake_case    ,  max_length=5_12							)
            bert_res.extend(res['input_ids']							)
      assert len(_snake_case							) == len(_snake_case							)

      _A        =       []
      for input_ids, chinese_word in zip(_snake_case    ,  _snake_case							):
            _A        =       []
            for id in input_ids:
                  _A        =       bert_tokenizer._convert_id_to_token(_snake_case							)
                  input_tokens.append(_snake_case							)
            _A        =       add_sub_symbol(_snake_case    ,  _snake_case							)
            _A        =       []
            # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
            for i, token in enumerate(_snake_case							):
                  if token[:2] == "##":
                        _A        =       token[2:]
                        # save chinese tokens' pos
                        if len(_snake_case							) == 1 and _is_chinese_char(ord(_snake_case							)							):
                              ref_id.append(_snake_case							)
            ref_ids.append(_snake_case							)

      assert len(_snake_case							) == len(_snake_case							)

      return ref_ids






def 						_snake_case					( _snake_case							: List[str]							)   -> Dict:






      '''simple docstring'''


      with open(args.file_name    ,  'r'    ,  encoding='utf-8'							) as f:
            _A        =       f.readlines()
      _A        =       [line.strip() for line in data if len(_snake_case							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      _A        =       LTP(args.ltp							)  # faster in GPU device
      _A        =       BertTokenizer.from_pretrained(args.bert							)

      _A        =       prepare_ref(_snake_case    ,  _snake_case    ,  _snake_case							)

      with open(args.save_path    ,  'w'    ,  encoding='utf-8'							) as f:
            _A        =       [json.dumps(_snake_case							) + '\n' for ref in ref_ids]
            f.writelines(_snake_case							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser(description='''prepare_chinese_ref''')
       parser.add_argument(
           '''--file_name''',
           type=str,
           default='''./resources/chinese-demo.txt''',
           help='''file need process, same as training data in lm''',
       )
       parser.add_argument(
           '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
       )
       parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
       parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

       a         =      parser.parse_args()
       main(args)

