






"""simple docstring"""





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )


@require_vision
class      _lowercase (     unittest.TestCase						):


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    int  			=tempfile.mkdtemp()

            __snake_case      :    Tuple  			=BlipImageProcessor()
            __snake_case      :    str  			=GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''						)
            __snake_case      :    Optional[int]  			=BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''						)

            __snake_case      :    Optional[int]  			=InstructBlipProcessor(a							,   a							,   a						)

            processor.save_pretrained(self.tmpdirname						)


      def _UpperCamelCase						(    self				:	Dict							,   **a				:	Tuple						):





            """simple docstring"""







            return AutoProcessor.from_pretrained(self.tmpdirname							,   **a						).tokenizer


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   **a				:	Dict						):





            """simple docstring"""







            return AutoProcessor.from_pretrained(self.tmpdirname							,   **a						).image_processor


      def _UpperCamelCase						(    self				:	List[Any]							,   **a				:	int						):





            """simple docstring"""







            return AutoProcessor.from_pretrained(self.tmpdirname							,   **a						).qformer_tokenizer


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            shutil.rmtree(self.tmpdirname						)


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    str  			=[np.random.randint(2_5_5							,   size=(3, 3_0, 4_0_0)							,   dtype=np.uinta						)]

            __snake_case      :    Tuple  			=[Image.fromarray(np.moveaxis(a							,   0							,   -1						)						) for x in image_inputs]

            return image_inputs


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Tuple  			=InstructBlipProcessor(
                tokenizer=self.get_tokenizer()							,   image_processor=self.get_image_processor()							,   qformer_tokenizer=self.get_qformer_tokenizer()							,   )
            processor.save_pretrained(self.tmpdirname						)

            __snake_case      :    Union[str, Any]  			=self.get_tokenizer(bos_token='''(BOS)'''							,   eos_token='''(EOS)'''						)
            __snake_case      :    Tuple  			=self.get_image_processor(do_normalize=a							,   padding_value=1.0						)

            __snake_case      :    List[Any]  			=InstructBlipProcessor.from_pretrained(
                self.tmpdirname							,   bos_token='''(BOS)'''							,   eos_token='''(EOS)'''							,   do_normalize=a							,   padding_value=1.0						)

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

            self.assertEqual(processor.image_processor.to_json_string()							,   image_processor_add_kwargs.to_json_string()						)
            self.assertIsInstance(processor.image_processor							,   a						)
            self.assertIsInstance(processor.qformer_tokenizer							,   a						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    str  			=self.get_image_processor()
            __snake_case      :    Tuple  			=self.get_tokenizer()
            __snake_case      :    Dict  			=self.get_qformer_tokenizer()

            __snake_case      :    Optional[int]  			=InstructBlipProcessor(
                tokenizer=a							,   image_processor=a							,   qformer_tokenizer=a						)

            __snake_case      :    Optional[Any]  			=self.prepare_image_inputs()

            __snake_case      :    List[str]  			=image_processor(a							,   return_tensors='''np'''						)
            __snake_case      :    List[Any]  			=processor(images=a							,   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 _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Any  			=self.get_image_processor()
            __snake_case      :    List[Any]  			=self.get_tokenizer()
            __snake_case      :    List[str]  			=self.get_qformer_tokenizer()

            __snake_case      :    Any  			=InstructBlipProcessor(
                tokenizer=a							,   image_processor=a							,   qformer_tokenizer=a						)

            __snake_case      :    Optional[int]  			='''lower newer'''

            __snake_case      :    List[str]  			=processor(text=a						)

            __snake_case      :    List[Any]  			=tokenizer(a							,   return_token_type_ids=a						)
            __snake_case      :    Dict  			=qformer_tokenizer(a							,   return_token_type_ids=a						)

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

            for key in encoded_tokens_qformer.keys():
                  self.assertListEqual(encoded_tokens_qformer[key]							,   encoded_processor['''qformer_''' + key]						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    int  			=self.get_image_processor()
            __snake_case      :    Any  			=self.get_tokenizer()
            __snake_case      :    Tuple  			=self.get_qformer_tokenizer()

            __snake_case      :    Optional[int]  			=InstructBlipProcessor(
                tokenizer=a							,   image_processor=a							,   qformer_tokenizer=a						)

            __snake_case      :    Dict  			='''lower newer'''
            __snake_case      :    Tuple  			=self.prepare_image_inputs()

            __snake_case      :    int  			=processor(text=a							,   images=a						)

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

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


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.get_image_processor()
            __snake_case      :    Dict  			=self.get_tokenizer()
            __snake_case      :    List[str]  			=self.get_qformer_tokenizer()

            __snake_case      :    List[Any]  			=InstructBlipProcessor(
                tokenizer=a							,   image_processor=a							,   qformer_tokenizer=a						)

            __snake_case      :    List[str]  			=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __snake_case      :    Tuple  			=processor.batch_decode(a						)
            __snake_case      :    Tuple  			=tokenizer.batch_decode(a						)

            self.assertListEqual(a							,   a						)







      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







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

            __snake_case      :    Optional[Any]  			=InstructBlipProcessor(
                tokenizer=a							,   image_processor=a							,   qformer_tokenizer=a						)

            __snake_case      :    List[str]  			='''lower newer'''
            __snake_case      :    Any  			=self.prepare_image_inputs()

            __snake_case      :    List[Any]  			=processor(text=a							,   images=a						)

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










"""simple docstring"""





import os
from datetime import datetime as dt

from github import Github


UpperCamelCase_	:				List[str]										= [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def     __lowercase					(    )					->					Optional[Any]:
      __snake_case      :    List[Any]  			=Github(os.environ['''GITHUB_TOKEN'''] )
      __snake_case      :    int  			=g.get_repo('''huggingface/diffusers''' )
      __snake_case      :    Any  			=repo.get_issues(state='''open''' )

      for issue in open_issues:
            __snake_case      :    Dict  			=sorted(issue.get_comments()       ,		key=lambda a : i.created_at       ,		reverse=a )
            __snake_case      :    Dict  			=comments[0] if len(a ) > 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()


