


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				KandinskyImgaImgPipeline
				_snake_case						       =				['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
				_snake_case						       =				[
				    '''prompt''',
				    '''negative_prompt''',
				    '''image_embeds''',
				    '''negative_image_embeds''',
				    '''image''',
				]
				_snake_case						       =				[
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''strength''',
				    '''guidance_scale''',
				    '''negative_prompt''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				_snake_case						       =				False

				@property
				def 	a__		(     self							)    ->  Optional[int]:
					return 3_2

				@property
				def 	a__		(     self							)    ->  List[str]:
					return 3_2

				@property
				def 	a__		(     self							)    ->  Dict:
					return self.time_input_dim

				@property
				def 	a__		(     self							)    ->  Optional[Any]:
					return self.time_input_dim * 4

				@property
				def 	a__		(     self							)    ->  Dict:
					return 1_0_0

				@property
				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		Union[str, Any]   					=		XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"							)
					return tokenizer

				@property
				def 	a__		(     self							)    ->  List[Any]:
					torch.manual_seed(0							)
					lowercase						:		Optional[int]   					=		MCLIPConfig(
					    numDims=self.cross_attention_dim							,      transformerDimensions=self.text_embedder_hidden_size							,      hidden_size=self.text_embedder_hidden_size							,      intermediate_size=3_7							,      num_attention_heads=4							,      num_hidden_layers=5							,      vocab_size=1_0_0_5							,      )

					lowercase						:		Optional[int]   					=		MultilingualCLIP(a_							)
					lowercase						:		Optional[Any]   					=		text_encoder.eval()

					return text_encoder

				@property
				def 	a__		(     self							)    ->  Optional[Any]:
					torch.manual_seed(0							)

					lowercase						:		str   					=		{
					    "in_channels": 4,
					    # Out channels is double in channels because predicts mean and variance
					    "out_channels": 8,
					    "addition_embed_type": "text_image",
					    "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": "text_image_proj",
					    "cross_attention_dim": self.cross_attention_dim,
					    "attention_head_dim": 4,
					    "resnet_time_scale_shift": "scale_shift",
					    "class_embed_type": None,
					}

					lowercase						:		str   					=		UNetaDConditionModel(**a_							)
					return model

				@property
				def 	a__		(     self							)    ->  List[str]:
					return {
					    "block_out_channels": [3_2, 6_4],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 1_2,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}

				@property
				def 	a__		(     self							)    ->  Optional[Any]:
					torch.manual_seed(0							)
					lowercase						:		int   					=		VQModel(**self.dummy_movq_kwargs							)
					return model

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		Tuple   					=		self.dummy_text_encoder
					lowercase						:		List[str]   					=		self.dummy_tokenizer
					lowercase						:		List[str]   					=		self.dummy_unet
					lowercase						:		Optional[int]   					=		self.dummy_movq

					lowercase						:		Optional[Any]   					=		{
					    "num_train_timesteps": 1_0_0_0,
					    "beta_schedule": "linear",
					    "beta_start": 0.0_00_85,
					    "beta_end": 0.0_12,
					    "clip_sample": False,
					    "set_alpha_to_one": False,
					    "steps_offset": 0,
					    "prediction_type": "epsilon",
					    "thresholding": False,
					}

					lowercase						:		int   					=		DDIMScheduler(**a_							)

					lowercase						:		List[str]   					=		{
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "movq": movq,
					}

					return components

				def 	a__		(     self							,      a_							,      a_=0							)    ->  int:
					lowercase						:		Optional[int]   					=		floats_tensor((1, self.cross_attention_dim)							,      rng=random.Random(a_							)							).to(a_							)
					lowercase						:		int   					=		floats_tensor((1, self.cross_attention_dim)							,      rng=random.Random(seed + 1							)							).to(a_							)
					# create init_image
					lowercase						:		Tuple   					=		floats_tensor((1, 3, 6_4, 6_4)							,      rng=random.Random(a_							)							).to(a_							)
					lowercase						:		List[str]   					=		image.cpu().permute(0							,      2							,      3							,      1							)[0]
					lowercase						:		Any   					=		Image.fromarray(np.uinta(a_							)							).convert("RGB"							).resize((2_5_6, 2_5_6)							)

					if str(a_							).startswith("mps"							):
						lowercase						:		int   					=		torch.manual_seed(a_							)
					else:
						lowercase						:		List[str]   					=		torch.Generator(device=a_							).manual_seed(a_							)
					lowercase						:		Any   					=		{
					    "prompt": "horse",
					    "image": init_image,
					    "image_embeds": image_embeds,
					    "negative_image_embeds": negative_image_embeds,
					    "generator": generator,
					    "height": 6_4,
					    "width": 6_4,
					    "num_inference_steps": 1_0,
					    "guidance_scale": 7.0,
					    "strength": 0.2,
					    "output_type": "np",
					}
					return inputs


				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		Union[str, Any]   					=		"cpu"

					lowercase						:		List[Any]   					=		self.get_dummy_components()

					lowercase						:		List[Any]   					=		self.pipeline_class(**a_							)
					lowercase						:		List[Any]   					=		pipe.to(a_							)

					pipe.set_progress_bar_config(disable=a_							)

					lowercase						:		List[str]   					=		pipe(**self.get_dummy_inputs(a_							)							)
					lowercase						:		str   					=		output.images

					lowercase						:		Any   					=		pipe(
					    **self.get_dummy_inputs(a_							)							,      return_dict=a_							,      )[0]

					lowercase						:		Dict   					=		image[0, -3:, -3:, -1]
					lowercase						:		Optional[Any]   					=		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 6_4, 6_4, 3)

					lowercase						:		str   					=		np.array(
					    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]							)
					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        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def 	a__		(     self							)    ->  Dict:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Any   					=		load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/kandinsky/kandinsky_img2img_frog.npy"							)

					lowercase						:		List[str]   					=		load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"							)
					lowercase						:		Dict   					=		"A red cartoon frog, 4k"

					lowercase						:		Any   					=		KandinskyPriorPipeline.from_pretrained(
					    "kandinsky-community/kandinsky-2-1-prior"							,      torch_dtype=torch.floataa							)
					pipe_prior.to(a_							)

					lowercase						:		List[Any]   					=		KandinskyImgaImgPipeline.from_pretrained(
					    "kandinsky-community/kandinsky-2-1"							,      torch_dtype=torch.floataa							)
					lowercase						:		int   					=		pipeline.to(a_							)

					pipeline.set_progress_bar_config(disable=a_							)

					lowercase						:		Optional[Any]   					=		torch.Generator(device="cpu"							).manual_seed(0							)
					lowercase					,		lowercase						:		str   					=		pipe_prior(
					    a_							,      generator=a_							,      num_inference_steps=5							,      negative_prompt=""							,      ).to_tuple()

					lowercase						:		List[str]   					=		pipeline(
					    a_							,      image=a_							,      image_embeds=a_							,      negative_image_embeds=a_							,      generator=a_							,      num_inference_steps=1_0_0							,      height=7_6_8							,      width=7_6_8							,      strength=0.2							,      output_type="np"							,      )

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

					assert image.shape == (7_6_8, 7_6_8, 3)

					assert_mean_pixel_difference(a_							,      a_							)






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

from ltp import LTP

from transformers import BertTokenizer




def 	_A  (       A							)  ->				int:
	# 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 >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
	    or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
	    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
	    or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
	    or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
	    or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
	    or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
	    or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
	):  #
		return True

	return False




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




def 	_A  (       A							)  ->				Dict:
	lowercase						:		Any   					=		set()

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




def 	_A  (       A							,A							)  ->				Tuple:
	if not chinese_word_set:
		return bert_tokens
	lowercase						:		Optional[Any]   					=		max([len(A							) for w in chinese_word_set]							)

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




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	lowercase						:		List[Any]   					=		[]

	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		ltp_tokenizer.seg(lines[i : i + 1_0_0]							)[0]
		lowercase						:		Dict   					=		[get_chinese_word(A							) for r in res]
		ltp_res.extend(A							)
	assert len(A							) == len(A							)

	lowercase						:		List[str]   					=		[]
	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		bert_tokenizer(lines[i : i + 1_0_0]							,add_special_tokens=A							,truncation=A							,max_length=5_1_2							)
		bert_res.extend(res["input_ids"]							)
	assert len(A							) == len(A							)

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

	assert len(A							) == len(A							)

	return ref_ids




def 	_A  (       A							)  ->				Union[str, Any]:
	# 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:
		lowercase						:		List[str]   					=		f.readlines()
	lowercase						:		Tuple   					=		[line.strip() for line in data if len(A							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	lowercase						:		Any   					=		LTP(args.ltp							)  # faster in GPU device
	lowercase						:		int   					=		BertTokenizer.from_pretrained(args.bert							)

	lowercase						:		List[str]   					=		prepare_ref(A							,A							,A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : List[Any]             =				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""")

					lowerCAmelCase   : Union[str, Any]             =				parser.parse_args()
					main(args)


