



'''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 	a__     (				unittest.TestCase	):
	__magic_name__		:    Tuple	    =     ViTImageProcessor if is_vision_available() else None


	@property
	def 				lowercase__  (self : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= (3, 32, 128)
					SCREAMING_SNAKE_CASE  :						int      					= tempfile.mkdtemp()

					# fmt: off
					SCREAMING_SNAKE_CASE  :						List[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
					SCREAMING_SNAKE_CASE  :						List[Any]      					= dict(zip(__UpperCAmelCase,					range(len(__UpperCAmelCase      )      )      )      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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(__UpperCAmelCase      ) + '''\n'''      )

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


	def 				lowercase__  (self : List[Any],					**__UpperCAmelCase : Tuple      )     ->			Dict:

					"""simple docstring"""

					return MgpstrTokenizer.from_pretrained(self.tmpdirname,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Any,					**__UpperCAmelCase : str      )     ->			Optional[Any]:

					"""simple docstring"""

					return ViTImageProcessor.from_pretrained(self.tmpdirname,					**__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			Optional[Any]:

					"""simple docstring"""

					shutil.rmtree(self.tmpdirname      )


	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= Image.fromarray(np.moveaxis(__UpperCAmelCase,					0,					-1      )      )

					return image_input


	def 				lowercase__  (self : str      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_tokenizer()
					SCREAMING_SNAKE_CASE  :						int      					= self.get_image_processor()

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

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

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


	def 				lowercase__  (self : str      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_tokenizer()
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_image_processor()

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

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_image_processor(do_normalize=__UpperCAmelCase,					padding_value=1.0      )

					SCREAMING_SNAKE_CASE  :						Dict      					= MgpstrProcessor.from_pretrained(
					    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=__UpperCAmelCase,					padding_value=1.0      )

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

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


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						str      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= image_processor(__UpperCAmelCase,					return_tensors='''np'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= processor(images=__UpperCAmelCase,					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 : str      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''test'''

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= processor(text=__UpperCAmelCase      )

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


	def 				lowercase__  (self : str      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= '''test'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= processor(text=__UpperCAmelCase,					images=__UpperCAmelCase      )

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

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


	def 				lowercase__  (self : List[str]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= processor.char_decode(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= tokenizer.batch_decode(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [seq.replace(''' ''',					''''''      ) for seq in decoded_tok]

					self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : Tuple      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						int      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Any      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= None
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE  :						int      					= processor(text=__UpperCAmelCase,					images=__UpperCAmelCase      )

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






	def 				lowercase__  (self : Optional[int]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_image_processor()
					SCREAMING_SNAKE_CASE  :						Any      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Tuple      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.randn(1,					27,					38      )
					SCREAMING_SNAKE_CASE  :						int      					= torch.randn(1,					27,					50257      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.randn(1,					27,					30522      )

					SCREAMING_SNAKE_CASE  :						Dict      					= 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 os
from datetime import datetime as dt

from github import Github


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






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						List[Any]      					= Github(os.environ['''GITHUB_TOKEN''']       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= g.get_repo('''huggingface/diffusers'''       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= repo.get_issues(state='''open'''       )

				for issue in open_issues:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= sorted(issue.get_comments()    ,	key=lambda _SCREAMING_SNAKE_CASE       : i.created_at    ,	reverse=_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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()




