


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class _lowerCamelCase(		_a,					unittest.TestCase  ):
       lowercase_					:      Any       					=     KandinskyVaaControlnetImgaImgPipeline
       lowercase_					:      Tuple       					=     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
       lowercase_					:      Optional[int]       					=     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
       lowercase_					:      str       					=     [
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_					:      List[str]       					=     False
       @property
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           return 32
       @property
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           return 32
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return self.time_input_dim
       @property
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           return self.time_input_dim * 4
       @property
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           return 1_00
       @property
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           torch.manual_seed(0)

           _lowercase			:				Tuple 						=						{
               'in_channels': 8,
               # Out channels is double in channels because predicts mean and variance
               'out_channels': 8,
               'addition_embed_type': 'image_hint',
               'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
               'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
               'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
               'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
               'layers_per_block': 1,
               'encoder_hid_dim': self.text_embedder_hidden_size,
               'encoder_hid_dim_type': 'image_proj',
               'cross_attention_dim': self.cross_attention_dim,
               'attention_head_dim': 4,
               'resnet_time_scale_shift': 'scale_shift',
               'class_embed_type': None,
           }

           _lowercase			:				Tuple 						=						UNetaDConditionModel(**lowerCamelCase)
           return model
       @property
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           return {
               "block_out_channels": [32, 32, 64, 64],
               "down_block_types": [
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "AttnDownEncoderBlock2D",
               ],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
               "vq_embed_dim": 4,
           }
       @property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase			:				List[Any] 						=						VQModel(**self.dummy_movq_kwargs)
           return model
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.dummy_unet
           _lowercase			:				List[Any] 						=						self.dummy_movq

           _lowercase			:				Union[str, Any] 						=						{
               'num_train_timesteps': 10_00,
               'beta_schedule': 'linear',
               'beta_start': 0.0_0_0_8_5,
               'beta_end': 0.0_1_2,
               'clip_sample': False,
               'set_alpha_to_one': False,
               'steps_offset': 0,
               'prediction_type': 'epsilon',
               'thresholding': False,
           }

           _lowercase			:				Any 						=						DDIMScheduler(**lowerCamelCase)

           _lowercase			:				Optional[Any] 						=						{
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=0)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Dict 						=						floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase			:				Any 						=						floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(
               lowerCamelCase)
           # create init_image
           _lowercase			:				Tuple 						=						floats_tensor((1, 3, 64, 64), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase			:				str 						=						image.cpu().permute(0, 2, 3, 1)[0]
           _lowercase			:				Optional[int] 						=						Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB').resize((2_56, 2_56))
           # create hint
           _lowercase			:				int 						=						floats_tensor((1, 3, 64, 64), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)

           if str(lowerCamelCase).startswith('mps'):
               _lowercase			:				str 						=						torch.manual_seed(lowerCamelCase)
           else:
               _lowercase			:				Any 						=						torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase			:				Any 						=						{
               'image': init_image,
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'hint': hint,
               'generator': generator,
               'height': 64,
               'width': 64,
               'num_inference_steps': 10,
               'guidance_scale': 7.0,
               'strength': 0.2,
               'output_type': 'np',
           }
           return inputs




       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Dict 						=						'cpu'

           _lowercase			:				Optional[Any] 						=						self.get_dummy_components()

           _lowercase			:				Any 						=						self.pipeline_class(**lowerCamelCase)
           _lowercase			:				str 						=						pipe.to(lowerCamelCase)

           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Any 						=						pipe(**self.get_dummy_inputs(lowerCamelCase))
           _lowercase			:				List[str] 						=						output.images

           _lowercase			:				List[Any] 						=						pipe(
               **self.get_dummy_inputs(lowerCamelCase), return_dict=lowerCamelCase, )[0]

           _lowercase			:				Tuple 						=						image[0, -3:, -3:, -1]
           _lowercase			:				Optional[Any] 						=						image_from_tuple[0, -3:, -3:, -1]

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

           _lowercase			:				int 						=						np.array(
               [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6])
           assert (
               np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class _lowerCamelCase(		unittest.TestCase  ):
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

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




       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy')

           _lowercase			:				Optional[Any] 						=						load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
           _lowercase			:				Dict 						=						init_image.resize((5_12, 5_12))

           _lowercase			:				Union[str, Any] 						=						load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinskyv22/hint_image_cat.png')
           _lowercase			:				List[str] 						=						torch.from_numpy(np.array(lowerCamelCase)).float() / 2_5_5.0
           _lowercase			:				Union[str, Any] 						=						hint.permute(2, 0, 1).unsqueeze(0)

           _lowercase			:				int 						=						'A robot, 4k photo'

           _lowercase			:				List[Any] 						=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-2-prior', torch_dtype=torch.floataa)
           pipe_prior.to(lowerCamelCase)

           _lowercase			:				Dict 						=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-2-controlnet-depth', torch_dtype=torch.floataa)
           _lowercase			:				Union[str, Any] 						=						pipeline.to(lowerCamelCase)

           pipeline.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Tuple 						=						torch.Generator(device='cpu').manual_seed(0)

           _lowercase						,							_lowercase			:				str 						=						pipe_prior(
               lowerCamelCase, image=lowerCamelCase, strength=0.8_5, generator=lowerCamelCase, negative_prompt='', ).to_tuple()

           _lowercase			:				Union[str, Any] 						=						pipeline(
               image=lowerCamelCase, image_embeds=lowerCamelCase, negative_image_embeds=lowerCamelCase, hint=lowerCamelCase, generator=lowerCamelCase, num_inference_steps=1_00, height=5_12, width=5_12, strength=0.5, output_type='np', )

           _lowercase			:				Optional[Any] 						=						output.images[0]

           assert image.shape == (5_12, 5_12, 3)

           assert_mean_pixel_difference(lowerCamelCase, lowerCamelCase)




"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[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  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





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

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    ltp_tokenizer.pipeline(lines[i : i + 1_00],	tasks=['''cws''']      ).cws
										_UpperCamelCase									=    [get_chinese_word(__snake_case      ) for r in res]
										ltp_res.extend(__snake_case      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    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      )

					_UpperCamelCase									=    []
					for input_ids, chinese_word in zip(__snake_case,	__snake_case      ):
										_UpperCamelCase									=    []
										for id in input_ids:
															_UpperCamelCase									=    bert_tokenizer._convert_id_to_token(__snake_case      )
															input_tokens.append(__snake_case      )
										_UpperCamelCase									=    add_sub_symbol(__snake_case,	__snake_case      )
										_UpperCamelCase									=    []
										# 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] == "##":
																				_UpperCamelCase									=    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  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





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

					_UpperCamelCase									=    prepare_ref(__snake_case,	__snake_case,	__snake_case      )

					with open(args.save_path,	'''w''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    [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""",
				    required=False,
				    type=str,
				    default="""./resources/chinese-demo.txt""",
				    help="""file need process, same as training data in lm""",
				)
				parser.add_argument(
				    """--ltp""",
				    required=False,
				    type=str,
				    default="""./resources/ltp""",
				    help="""resources for LTP tokenizer, usually a path""",
				)
				parser.add_argument(
				    """--bert""",
				    required=False,
				    type=str,
				    default="""./resources/robert""",
				    help="""resources for Bert tokenizer""",
				)
				parser.add_argument(
				    """--save_path""",
				    required=False,
				    type=str,
				    default="""./resources/ref.txt""",
				    help="""path to save res""",
				)

				_a								=						parser.parse_args()
				main(args)






