






from datetime import datetime as dt
import os

from github import Github


A__			:    Any         =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    Github(os.environ['''GITHUB_TOKEN''']       )
				lowercase__     				=    g.get_repo('''huggingface/transformers'''       )
				lowercase__     				=    repo.get_issues(state='''open'''       )

				for issue in open_issues:
								lowercase__     				=    sorted([comment for comment in issue.get_comments()]	,     key=lambda lowerCamelCase_       : i.created_at	,     reverse=lowerCamelCase_       )
								lowercase__     				=    comments[0] if len(lowerCamelCase_       ) > 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()       )
								):
												# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
												issue.edit(state='''closed'''       )
								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()       )
								):
												# print(f"Would add stale comment to {issue.number}")
												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/transformers/blob/main/CONTRIBUTING.md) '''
												    '''are likely to be ignored.'''       )


if __name__ == "__main__":
							main()












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"""




				lowercase__       							= ViTImageProcessor if is_vision_available() else None




				@property
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    (3, 32, 128)
								lowercase__     				=    tempfile.mkdtemp()

								# fmt: off
								lowercase__     				=    ['''[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__     				=    dict(zip(lowerCamelCase,						range(len(lowerCamelCase						)						)						)						)

								lowercase__     				=    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(lowerCamelCase						) + '''\n'''						)

								lowercase__     				=    {
								    '''do_normalize''': False,
								    '''do_resize''': True,
								    '''image_processor_type''': '''ViTImageProcessor''',
								    '''resample''': 3,
								    '''size''': {'''height''': 32, '''width''': 128},
								}
								lowercase__     				=    os.path.join(self.tmpdirname,						lowerCamelCase						)
								with open(self.image_processor_file,						'''w''',						encoding='''utf-8'''						) as fp:
												json.dump(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					int,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								return MgpstrTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					str,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								return ViTImageProcessor.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								shutil.rmtree(self.tmpdirname						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    np.random.randint(255,						size=(3, 30, 400),						dtype=np.uinta						)

								lowercase__     				=    Image.fromarray(np.moveaxis(lowerCamelCase,						0,						-1						)						)

								return image_input




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)
								lowercase__     				=    MgpstrProcessor.from_pretrained(self.tmpdirname,						use_fast=lowerCamelCase						)

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

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)

								lowercase__     				=    self.get_tokenizer(bos_token='''(BOS)''',						eos_token='''(EOS)'''						)
								lowercase__     				=    self.get_image_processor(do_normalize=lowerCamelCase,						padding_value=1.0						)

								lowercase__     				=    MgpstrProcessor.from_pretrained(
								    self.tmpdirname,						bos_token='''(BOS)''',						eos_token='''(EOS)''',						do_normalize=lowerCamelCase,						padding_value=1.0						)

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

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(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_image_proc.keys():
												self.assertAlmostEqual(input_image_proc[key].sum(),						input_processor[key].sum(),						delta=1E-2						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''

								lowercase__     				=    processor(text=lowerCamelCase						)

								lowercase__     				=    tokenizer(lowerCamelCase						)
								for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key],						encoded_processor[key]						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

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

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




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

								lowercase__     				=    processor.char_decode(lowerCamelCase						)
								lowercase__     				=    tokenizer.batch_decode(lowerCamelCase						)
								lowercase__     				=    [seq.replace(''' ''',						''''''						) for seq in decoded_tok]

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    None
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    torch.randn(1,						27,						38						)
								lowercase__     				=    torch.randn(1,						27,						50_257						)
								lowercase__     				=    torch.randn(1,						27,						30_522						)

								lowercase__     				=    processor.batch_decode([char_input, bpe_input, wp_input]						)

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




