


"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							# This defines a "chinese character" as anything in the CJK Unicode block:
							#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
							#
							# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
							# despite its name. The modern Korean Hangul alphabet is a different block,
							# as is Japanese Hiragana and Katakana. Those alphabets are used to write
							# space-separated words, so they are not treated specially and handled
							# like the all of the other languages.
							if (
							    (cp >= 0X4e00 and cp <= 0X9fff)
							    or (cp >= 0X3400 and cp <= 0X4dbf)  #
							    or (cp >= 0X20000 and cp <= 0X2a6df)  #
							    or (cp >= 0X2a700 and cp <= 0X2b73f)  #
							    or (cp >= 0X2b740 and cp <= 0X2b81f)  #
							    or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
							    or (cp >= 0Xf900 and cp <= 0Xfaff)
							    or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
							):  #
														return True

							return False







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				List[Any]:
							# word like '180' or '身高' or '神'
							for char in word:
														snake_case_           =		ord(_SCREAMING_SNAKE_CASE				)
														if not _is_chinese_char(_SCREAMING_SNAKE_CASE				):
																					return 0
							return 1







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Any:
							snake_case_           =		set()

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							if not chinese_word_set:
														return bert_tokens
							snake_case_           =		max([len(_SCREAMING_SNAKE_CASE				) for w in chinese_word_set]				)

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		[]

							for i in range(0				, len(_SCREAMING_SNAKE_CASE				)				, 100				):
														snake_case_           =		ltp_tokenizer.seg(lines[i : i + 100]				)[0]
														snake_case_           =		[get_chinese_word(_SCREAMING_SNAKE_CASE				) for r in res]
														ltp_res.extend(_SCREAMING_SNAKE_CASE				)
							assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		[]
							for i in range(0				, len(_SCREAMING_SNAKE_CASE				)				, 100				):
														snake_case_           =		bert_tokenizer(lines[i : i + 100]				, add_special_tokens=_SCREAMING_SNAKE_CASE				, truncation=_SCREAMING_SNAKE_CASE				, max_length=512				)
														bert_res.extend(res["""input_ids"""]				)
							assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)

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

							assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)

							return ref_ids







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
							# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
							with open(args.file_name				, """r"""				, encoding="""utf-8"""				) as f:
														snake_case_           =		f.readlines()
							snake_case_           =		[line.strip() for line in data if len(_SCREAMING_SNAKE_CASE				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
							snake_case_           =		LTP(args.ltp				)  # faster in GPU device
							snake_case_           =		BertTokenizer.from_pretrained(args.bert				)

							snake_case_           =		prepare_ref(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Dict		=    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')

				__SCREAMING_SNAKE_CASE							:    Optional[int]		=    parser.parse_args()
				main(args)







"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Dict							      =    KandinskyVaaControlnetPipeline
							__lowercase:		str							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		List[str]							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		Union[str, Any]							      =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							__lowercase:		Tuple							      =    False



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														return 32



							@property
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Union[str, Any]:



														"""simple docstring"""



														return 32



							@property
							def 		lowerCAmelCase       (				self							:      int       )					->List[str]:



														"""simple docstring"""



														return self.time_input_dim



							@property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Union[str, Any]:



														"""simple docstring"""



														return self.time_input_dim * 4



							@property
							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														return 100



							@property
							def 		lowerCAmelCase       (				self							:      str       )					->List[Any]:



														"""simple docstring"""



														torch.manual_seed(0       )

														snake_case_           =		{
														    """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,
														}

														snake_case_           =		UNetaDConditionModel(**UpperCAmelCase_       )
														return model



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->Optional[Any]:



														"""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 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



														torch.manual_seed(0       )
														snake_case_           =		VQModel(**self.dummy_movq_kwargs       )
														return model



							def 		lowerCAmelCase       (				self							:      Dict       )					->str:



														"""simple docstring"""



														snake_case_           =		self.dummy_unet
														snake_case_           =		self.dummy_movq

														snake_case_           =		DDIMScheduler(
														    num_train_timesteps=1_000		,       beta_schedule="""linear"""		,       beta_start=0.00_085		,       beta_end=0.012		,       clip_sample=UpperCAmelCase_		,       set_alpha_to_one=UpperCAmelCase_		,       steps_offset=1		,       prediction_type="""epsilon"""		,       thresholding=UpperCAmelCase_		,       )

														snake_case_           =		{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}
														return components



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]=0       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(UpperCAmelCase_       )       ).to(UpperCAmelCase_       )
														snake_case_           =		floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(seed + 1       )       ).to(
														    UpperCAmelCase_       )

														# create hint
														snake_case_           =		floats_tensor((1, 3, 64, 64)		,       rng=random.Random(UpperCAmelCase_       )       ).to(UpperCAmelCase_       )

														if str(UpperCAmelCase_       ).startswith("""mps"""       ):
																					snake_case_           =		torch.manual_seed(UpperCAmelCase_       )
														else:
																					snake_case_           =		torch.Generator(device=UpperCAmelCase_       ).manual_seed(UpperCAmelCase_       )
														snake_case_           =		{
														    """image_embeds""": image_embeds,
														    """negative_image_embeds""": negative_image_embeds,
														    """hint""": hint,
														    """generator""": generator,
														    """height""": 64,
														    """width""": 64,
														    """guidance_scale""": 4.0,
														    """num_inference_steps""": 2,
														    """output_type""": """np""",
														}
														return inputs



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		"""cpu"""

														snake_case_           =		self.get_dummy_components()

														snake_case_           =		self.pipeline_class(**UpperCAmelCase_       )
														snake_case_           =		pipe.to(UpperCAmelCase_       )

														pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

														snake_case_           =		pipe(**self.get_dummy_inputs(UpperCAmelCase_       )       )
														snake_case_           =		output.images

														snake_case_           =		pipe(
														    **self.get_dummy_inputs(UpperCAmelCase_       )		,       return_dict=UpperCAmelCase_		,       )[0]

														snake_case_           =		image[0, -3:, -3:, -1]
														snake_case_           =		image_from_tuple[0, -3:, -3:, -1]

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

														snake_case_           =		np.array(
														    [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]       )

														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      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Union[str, Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""       )

														snake_case_           =		load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/hint_image_cat.png"""       )
														snake_case_           =		torch.from_numpy(np.array(UpperCAmelCase_       )       ).float() / 255.0
														snake_case_           =		hint.permute(2		,       0		,       1       ).unsqueeze(0       )

														snake_case_           =		KandinskyVaaPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-prior"""		,       torch_dtype=torch.floataa       )
														pipe_prior.to(UpperCAmelCase_       )

														snake_case_           =		KandinskyVaaControlnetPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-controlnet-depth"""		,       torch_dtype=torch.floataa       )
														snake_case_           =		pipeline.to(UpperCAmelCase_       )
														pipeline.set_progress_bar_config(disable=UpperCAmelCase_       )

														snake_case_           =		"""A robot, 4k photo"""

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_       ,						snake_case_           =		pipe_prior(
														    UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=5		,       negative_prompt=""""""		,       ).to_tuple()

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_           =		pipeline(
														    image_embeds=UpperCAmelCase_		,       negative_image_embeds=UpperCAmelCase_		,       hint=UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=100		,       output_type="""np"""		,       )

														snake_case_           =		output.images[0]

														assert image.shape == (512, 512, 3)

														assert_mean_pixel_difference(UpperCAmelCase_		,       UpperCAmelCase_       )



