

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor





@require_vision
class  __lowerCAmelCase     (					unittest.TestCase							):




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =tempfile.mkdtemp()

        _lowercase	       =[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            '的',
            '价',
            '格',
            '是',
            '15',
            '便',
            'alex',
            '##andra',
            '，',
            '。',
            '-',
            't',
            'shirt',
        ]
        _lowercase	       =os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES['vocab_file']					)
        with open(self.vocab_file							,   'w'							,   encoding='utf-8'					) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)

        _lowercase	       ={
            'do_resize': True,
            'size': {'height': 224, 'width': 224},
            'do_center_crop': True,
            'crop_size': {'height': 18, 'width': 18},
            'do_normalize': True,
            'image_mean': [0.48145466, 0.4578275, 0.40821073],
            'image_std': [0.26862954, 0.26130258, 0.27577711],
            'do_convert_rgb': True,
        }
        _lowercase	       =os.path.join(self.tmpdirname							,   lowerCAmelCase					)
        with open(self.image_processor_file							,   'w'							,   encoding='utf-8'					) as fp:
              json.dump(lowerCAmelCase							,   lowerCAmelCase					)




  def   A__     (				self							,   **lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        return BertTokenizer.from_pretrained(self.tmpdirname							,   **lowerCAmelCase					)




  def   A__     (				self							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        return BertTokenizerFast.from_pretrained(self.tmpdirname							,   **lowerCAmelCase					)




  def   A__     (				self							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname							,   **lowerCAmelCase					)




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname					)




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =[np.random.randint(255							,   size=(3, 30, 400)							,   dtype=np.uinta					)]

        _lowercase	       =[Image.fromarray(np.moveaxis(lowerCAmelCase							,   0							,   -1					)					) for x in image_inputs]

        return image_inputs




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.get_tokenizer()
        _lowercase	       =self.get_rust_tokenizer()
        _lowercase	       =self.get_image_processor()

        _lowercase	       =ChineseCLIPProcessor(tokenizer=lowerCAmelCase							,   image_processor=lowerCAmelCase					)
        processor_slow.save_pretrained(self.tmpdirname					)
        _lowercase	       =ChineseCLIPProcessor.from_pretrained(self.tmpdirname							,   use_fast=lowerCAmelCase					)

        _lowercase	       =ChineseCLIPProcessor(tokenizer=lowerCAmelCase							,   image_processor=lowerCAmelCase					)
        processor_fast.save_pretrained(self.tmpdirname					)
        _lowercase	       =ChineseCLIPProcessor.from_pretrained(self.tmpdirname					)

        self.assertEqual(processor_slow.tokenizer.get_vocab()							,   tokenizer_slow.get_vocab()					)
        self.assertEqual(processor_fast.tokenizer.get_vocab()							,   tokenizer_fast.get_vocab()					)
        self.assertEqual(tokenizer_slow.get_vocab()							,   tokenizer_fast.get_vocab()					)
        self.assertIsInstance(processor_slow.tokenizer							,   lowerCAmelCase					)
        self.assertIsInstance(processor_fast.tokenizer							,   lowerCAmelCase					)

        self.assertEqual(processor_slow.image_processor.to_json_string()							,   image_processor.to_json_string()					)
        self.assertEqual(processor_fast.image_processor.to_json_string()							,   image_processor.to_json_string()					)
        self.assertIsInstance(processor_slow.image_processor							,   lowerCAmelCase					)
        self.assertIsInstance(processor_fast.image_processor							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =ChineseCLIPProcessor(tokenizer=self.get_tokenizer()							,   image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        _lowercase	       =self.get_tokenizer(cls_token='(CLS)'							,   sep_token='(SEP)'					)
        _lowercase	       =self.get_image_processor(do_normalize=lowerCAmelCase					)

        _lowercase	       =ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname							,   cls_token='(CLS)'							,   sep_token='(SEP)'							,   do_normalize=lowerCAmelCase					)

        self.assertEqual(processor.tokenizer.get_vocab()							,   tokenizer_add_kwargs.get_vocab()					)
        self.assertIsInstance(processor.tokenizer							,   lowerCAmelCase					)

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




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =self.get_image_processor()
        _lowercase	       =self.get_tokenizer()

        _lowercase	       =ChineseCLIPProcessor(tokenizer=lowerCAmelCase							,   image_processor=lowerCAmelCase					)

        _lowercase	       =self.prepare_image_inputs()

        _lowercase	       =image_processor(lowerCAmelCase							,   return_tensors='np'					)
        _lowercase	       =processor(images=lowerCAmelCase							,   return_tensors='np'					)

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




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =self.get_image_processor()
        _lowercase	       =self.get_tokenizer()

        _lowercase	       =ChineseCLIPProcessor(tokenizer=lowerCAmelCase							,   image_processor=lowerCAmelCase					)

        _lowercase	       ='Alexandra，T-shirt的价格是15便士。'

        _lowercase	       =processor(text=lowerCAmelCase					)

        _lowercase	       =tokenizer(lowerCAmelCase					)

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]							,   encoded_processor[key]					)




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.get_image_processor()
        _lowercase	       =self.get_tokenizer()

        _lowercase	       =ChineseCLIPProcessor(tokenizer=lowerCAmelCase							,   image_processor=lowerCAmelCase					)

        _lowercase	       ='Alexandra，T-shirt的价格是15便士。'
        _lowercase	       =self.prepare_image_inputs()

        _lowercase	       =processor(text=lowerCAmelCase							,   images=lowerCAmelCase					)

        self.assertListEqual(list(inputs.keys()					)							,   ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']					)

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




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.get_image_processor()
        _lowercase	       =self.get_tokenizer()

        _lowercase	       =ChineseCLIPProcessor(tokenizer=lowerCAmelCase							,   image_processor=lowerCAmelCase					)

        _lowercase	       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _lowercase	       =processor.batch_decode(lowerCAmelCase					)
        _lowercase	       =tokenizer.batch_decode(lowerCAmelCase					)

        self.assertListEqual(lowerCAmelCase							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.get_image_processor()
        _lowercase	       =self.get_tokenizer()

        _lowercase	       =ChineseCLIPProcessor(tokenizer=lowerCAmelCase							,   image_processor=lowerCAmelCase					)

        _lowercase	       ='Alexandra，T-shirt的价格是15便士。'
        _lowercase	       =self.prepare_image_inputs()

        _lowercase	       =processor(text=lowerCAmelCase							,   images=lowerCAmelCase					)

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




import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase	      = [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''enhancement''',
    '''new pipeline/model''',
    '''new scheduler''',
    '''wip''',
]






def 							UpperCAmelCase_		(							):
	lowercase       =    Github(os.environ['GITHUB_TOKEN']							)
	lowercase       =    g.get_repo('huggingface/diffusers'							)
	lowercase       =    repo.get_issues(state='open'							)

	for issue in open_issues:
		lowercase       =    sorted(issue.get_comments() ,							key=lambda __SCREAMING_SNAKE_CASE							: i.created_at ,							reverse=__SCREAMING_SNAKE_CASE							)
		lowercase       =    comments[0] if len(__SCREAMING_SNAKE_CASE							) > 0 else None
		if (
		    last_comment is not None
		    and last_comment.user.login == "github-actions[bot]"
		    and (dt.utcnow() - issue.updated_at).days > 7
		    and (dt.utcnow() - issue.created_at).days >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Closes the issue after 7 days of inactivity since the Stalebot notification.
			issue.edit(state='closed'							)
		elif (
		    "stale" in issue.get_labels()
		    and last_comment is not None
		    and last_comment.user.login != "github-actions[bot]"
		):
			# Opens the issue if someone other than Stalebot commented.
			issue.edit(state='open'							)
			issue.remove_from_labels('stale'							)
		elif (
		    (dt.utcnow() - issue.updated_at).days > 23
		    and (dt.utcnow() - issue.created_at).days >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Post a Stalebot notification after 23 days of inactivity.
			issue.create_comment(
			    'This issue has been automatically marked as stale because it has not had '
			    'recent activity. If you think this still needs to be addressed '
			    'please comment on this thread.\n\nPlease note that issues that do not follow the '
			    '[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '
			    'are likely to be ignored.'							)
			issue.add_to_labels('stale'							)


if __name__ == "__main__":
	main()

