



'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 						__UpperCamelCase				(							_lowercase					) ->			str:

	if (
	    (cp >= 0x4E_00 and cp <= 0x9F_FF)
	    or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
	    or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
	    or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
	    or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
	    or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
	    or (cp >= 0xF9_00 and cp <= 0xFA_FF)
	    or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
	):  #
		return True

	return False







def 						__UpperCamelCase				(							_lowercase					) ->			int:

	for char in word:
		_lowercase					:       List[Any]       =		ord(UpperCAmelCase__					)
		if not _is_chinese_char(UpperCAmelCase__					):
			return 0
	return 1







def 						__UpperCamelCase				(							_lowercase					) ->			Union[str, Any]:
	_lowercase					:       Union[str, Any]       =		set()

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







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Optional[int]:

	if not chinese_word_set:
		return bert_tokens
	_lowercase					:       List[Any]       =		max([len(UpperCAmelCase__					) for w in chinese_word_set]					)

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







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			List[str]:
	_lowercase					:       Tuple       =		[]

	for i in range(0,				len(UpperCAmelCase__					),				100					):
		_lowercase					:       int       =		ltp_tokenizer.pipeline(lines[i : i + 100],				tasks=['cws']					).cws
		_lowercase					:       Optional[Any]       =		[get_chinese_word(UpperCAmelCase__					) for r in res]
		ltp_res.extend(UpperCAmelCase__					)
	assert len(UpperCAmelCase__					) == len(UpperCAmelCase__					)

	_lowercase					:       List[str]       =		[]
	for i in range(0,				len(UpperCAmelCase__					),				100					):
		_lowercase					:       List[str]       =		bert_tokenizer(lines[i : i + 100],				add_special_tokens=UpperCAmelCase__,				truncation=UpperCAmelCase__,				max_length=512					)
		bert_res.extend(res['input_ids']					)
	assert len(UpperCAmelCase__					) == len(UpperCAmelCase__					)

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

	assert len(UpperCAmelCase__					) == len(UpperCAmelCase__					)

	return ref_ids







def 						__UpperCamelCase				(							_lowercase					) ->			int:

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

	_lowercase					:       Optional[int]       =		prepare_ref(UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__					)

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


if __name__ == "__main__":
		_A				:    int     						=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				:    List[str]     						=parser.parse_args()
		main(args)





'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_A				:    Optional[int]     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  **UpperCamelCase_				:	List[str]					)  ->       int:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						requires_backends(self     ,  'vision'					)
						self.check_model_type(
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == 'tf'
						    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING					)




					def __call__(  self				:	int     ,  UpperCamelCase_				:	Union[str, List[str], "Image", List["Image"]]     ,  **UpperCamelCase_				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





						return super().__call__(UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  **UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		{}
						if "candidate_labels" in kwargs:
							_lowercase					:       Union[str, Any]       =		kwargs['candidate_labels']
						if "hypothesis_template" in kwargs:
							_lowercase					:       int       =		kwargs['hypothesis_template']

						return preprocess_params, {}, {}




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	str="This is a photo of {}."					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		load_image(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.image_processor(images=[image]     ,  return_tensors=self.framework					)
						_lowercase					:       Optional[Any]       =		candidate_labels
						_lowercase					:       List[Any]       =		[hypothesis_template.format(UpperCamelCase_					) for x in candidate_labels]
						_lowercase					:       Union[str, Any]       =		self.tokenizer(UpperCamelCase_     ,  return_tensors=self.framework     ,  padding=UpperCamelCase_					)
						_lowercase					:       Any       =		[text_inputs]
						return inputs




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		model_inputs.pop('candidate_labels'					)
						_lowercase					:       List[str]       =		model_inputs.pop('text_inputs'					)
						if isinstance(text_inputs[0]     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		text_inputs[0]
						else:
							# Batching case.
							_lowercase					:       List[str]       =		text_inputs[0][0]

						_lowercase					:       Optional[Any]       =		self.model(**UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		{
						    'candidate_labels': candidate_labels,
						    'logits': outputs.logits_per_image,
						}
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		model_outputs.pop('candidate_labels'					)
						_lowercase					:       Optional[int]       =		model_outputs['logits'][0]
						if self.framework == "pt":
							_lowercase					:       List[Any]       =		logits.softmax(dim=-1					).squeeze(-1					)
							_lowercase					:       Tuple       =		probs.tolist()
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       List[Any]       =		[scores]
						elif self.framework == "tf":
							_lowercase					:       Optional[int]       =		stable_softmax(UpperCamelCase_     ,  axis=-1					)
							_lowercase					:       List[Any]       =		probs.numpy().tolist()
						else:
							raise ValueError(F'''Unsupported framework: {self.framework}'''					)

						_lowercase					:       List[Any]       =		[
						    {'score': score, 'label': candidate_label}
						    for score, candidate_label in sorted(zip(UpperCamelCase_     ,  UpperCamelCase_					)     ,  key=lambda UpperCamelCase_					: -x[0]					)
						]
						return result
