


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
					import torch


if is_vision_available():
					from PIL import Image

					from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				ViTImageProcessor if is_vision_available() else None

				@property
				def 	a__		(     self							)    ->  str:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Dict   					=		(3, 3_2, 1_2_8)
					lowercase						:		Optional[Any]   					=		tempfile.mkdtemp()

					# fmt: off
					lowercase						:		Optional[Any]   					=		["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
					# fmt: on
					lowercase						:		str   					=		dict(zip(a_							,      range(len(a_							)							)							)							)

					lowercase						:		str   					=		os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES["vocab_file"]							)
					with open(self.vocab_file							,      "w"							,      encoding="utf-8"							) as fp:
						fp.write(json.dumps(a_							) + "\n"							)

					lowercase						:		Optional[int]   					=		{
					    "do_normalize": False,
					    "do_resize": True,
					    "image_processor_type": "ViTImageProcessor",
					    "resample": 3,
					    "size": {"height": 3_2, "width": 1_2_8},
					}
					lowercase						:		Optional[int]   					=		os.path.join(self.tmpdirname							,      a_							)
					with open(self.image_processor_file							,      "w"							,      encoding="utf-8"							) as fp:
						json.dump(a_							,      a_							)

				def 	a__		(     self							,      **a_							)    ->  str:
					return MgpstrTokenizer.from_pretrained(self.tmpdirname							,      **a_							)

				def 	a__		(     self							,      **a_							)    ->  Dict:
					return ViTImageProcessor.from_pretrained(self.tmpdirname							,      **a_							)

				def 	a__		(     self							)    ->  int:
					shutil.rmtree(self.tmpdirname							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		str   					=		np.random.randint(2_5_5							,      size=(3, 3_0, 4_0_0)							,      dtype=np.uinta							)

					lowercase						:		List[str]   					=		Image.fromarray(np.moveaxis(a_							,      0							,      -1							)							)

					return image_input

				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		int   					=		self.get_tokenizer()
					lowercase						:		int   					=		self.get_image_processor()

					lowercase						:		Dict   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)
					processor.save_pretrained(self.tmpdirname							)
					lowercase						:		List[Any]   					=		MgpstrProcessor.from_pretrained(self.tmpdirname							,      use_fast=a_							)

					self.assertEqual(processor.char_tokenizer.get_vocab()							,      tokenizer.get_vocab()							)
					self.assertIsInstance(processor.char_tokenizer							,      a_							)

					self.assertEqual(processor.image_processor.to_json_string()							,      image_processor.to_json_string()							)
					self.assertIsInstance(processor.image_processor							,      a_							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Any   					=		self.get_tokenizer()
					lowercase						:		int   					=		self.get_image_processor()

					lowercase						:		List[Any]   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)
					processor.save_pretrained(self.tmpdirname							)

					lowercase						:		Optional[Any]   					=		self.get_tokenizer(bos_token="(BOS)"							,      eos_token="(EOS)"							)
					lowercase						:		Optional[int]   					=		self.get_image_processor(do_normalize=a_							,      padding_value=1.0							)

					lowercase						:		List[Any]   					=		MgpstrProcessor.from_pretrained(
					    self.tmpdirname							,      bos_token="(BOS)"							,      eos_token="(EOS)"							,      do_normalize=a_							,      padding_value=1.0							)

					self.assertEqual(processor.char_tokenizer.get_vocab()							,      tokenizer_add_kwargs.get_vocab()							)
					self.assertIsInstance(processor.char_tokenizer							,      a_							)

					self.assertEqual(processor.image_processor.to_json_string()							,      image_processor_add_kwargs.to_json_string()							)
					self.assertIsInstance(processor.image_processor							,      a_							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		self.get_image_processor()
					lowercase						:		Optional[Any]   					=		self.get_tokenizer()

					lowercase						:		Tuple   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)

					lowercase						:		Tuple   					=		self.prepare_image_inputs()

					lowercase						:		str   					=		image_processor(a_							,      return_tensors="np"							)
					lowercase						:		str   					=		processor(images=a_							,      return_tensors="np"							)

					for key in input_image_proc.keys():
						self.assertAlmostEqual(input_image_proc[key].sum()							,      input_processor[key].sum()							,      delta=1e-2							)

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Optional[Any]   					=		self.get_image_processor()
					lowercase						:		Dict   					=		self.get_tokenizer()

					lowercase						:		Tuple   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)

					lowercase						:		str   					=		"test"

					lowercase						:		List[str]   					=		processor(text=a_							)

					lowercase						:		Tuple   					=		tokenizer(a_							)
					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]							,      encoded_processor[key]							)

				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		List[str]   					=		self.get_image_processor()
					lowercase						:		Any   					=		self.get_tokenizer()

					lowercase						:		Optional[Any]   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)

					lowercase						:		Tuple   					=		"test"
					lowercase						:		int   					=		self.prepare_image_inputs()

					lowercase						:		Optional[Any]   					=		processor(text=a_							,      images=a_							)

					self.assertListEqual(list(inputs.keys()							)							,      ["pixel_values", "labels"]							)

					# test if it raises when no input is passed
					with pytest.raises(a_							):
						processor()

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Any   					=		self.get_image_processor()
					lowercase						:		Dict   					=		self.get_tokenizer()

					lowercase						:		str   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)

					lowercase						:		Union[str, Any]   					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					lowercase						:		str   					=		processor.char_decode(a_							)
					lowercase						:		Dict   					=		tokenizer.batch_decode(a_							)
					lowercase						:		str   					=		[seq.replace(" "							,      ""							) for seq in decoded_tok]

					self.assertListEqual(a_							,      a_							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		Any   					=		self.get_image_processor()
					lowercase						:		Dict   					=		self.get_tokenizer()

					lowercase						:		List[str]   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)

					lowercase						:		Any   					=		None
					lowercase						:		List[str]   					=		self.prepare_image_inputs()

					lowercase						:		str   					=		processor(text=a_							,      images=a_							)

					self.assertListEqual(list(inputs.keys()							)							,      processor.model_input_names							)


				def 	a__		(     self							)    ->  str:
					lowercase						:		str   					=		self.get_image_processor()
					lowercase						:		int   					=		self.get_tokenizer()

					lowercase						:		str   					=		MgpstrProcessor(tokenizer=a_							,      image_processor=a_							)

					lowercase						:		Union[str, Any]   					=		torch.randn(1							,      2_7							,      3_8							)
					lowercase						:		Dict   					=		torch.randn(1							,      2_7							,      5_0_2_5_7							)
					lowercase						:		List[str]   					=		torch.randn(1							,      2_7							,      3_0_5_2_2							)

					lowercase						:		Optional[int]   					=		processor.batch_decode([char_input, bpe_input, wp_input]							)

					self.assertListEqual(list(results.keys()							)							,      ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]							)






'''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)


