


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   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     int										=    tempfile.mkdtemp()

            a_    :     Union[str, Any]										=    BlipImageProcessor()
            a_    :     Any										=    GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""    )
            a_    :     Union[str, Any]										=    BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )

            a_    :     List[Any]										=    InstructBlipProcessor(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

            processor.save_pretrained(self.tmpdirname    )

       def 						UpperCAmelCase(   self      :     str      ,							**lowerCamelCase_      :     Optional[Any]    ):
            return AutoProcessor.from_pretrained(self.tmpdirname      ,							**lowerCamelCase_    ).tokenizer

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							**lowerCamelCase_      :     List[str]    ):
            return AutoProcessor.from_pretrained(self.tmpdirname      ,							**lowerCamelCase_    ).image_processor

       def 						UpperCAmelCase(   self      :     Dict      ,							**lowerCamelCase_      :     List[str]    ):
            return AutoProcessor.from_pretrained(self.tmpdirname      ,							**lowerCamelCase_    ).qformer_tokenizer

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            shutil.rmtree(self.tmpdirname    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     List[str]										=    [np.random.randint(2_5_5      ,							size=(3, 3_0, 4_0_0)      ,							dtype=np.uinta    )]

            a_    :     List[str]										=    [Image.fromarray(np.moveaxis(lowerCamelCase_      ,							0      ,							-1    )    ) for x in image_inputs]

            return image_inputs

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     int										=    InstructBlipProcessor(
                tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()      ,							qformer_tokenizer=self.get_qformer_tokenizer()      ,							)
            processor.save_pretrained(self.tmpdirname    )

            a_    :     str										=    self.get_tokenizer(bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""    )
            a_    :     Optional[Any]										=    self.get_image_processor(do_normalize=lowerCamelCase_      ,							padding_value=1.0    )

            a_    :     Union[str, Any]										=    InstructBlipProcessor.from_pretrained(
                self.tmpdirname      ,							bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""      ,							do_normalize=lowerCamelCase_      ,							padding_value=1.0    )

            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_    )
            self.assertIsInstance(processor.qformer_tokenizer      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    self.get_image_processor()
            a_    :     Optional[int]										=    self.get_tokenizer()
            a_    :     Optional[int]										=    self.get_qformer_tokenizer()

            a_    :     Optional[int]										=    InstructBlipProcessor(
                tokenizer=lowerCamelCase_      ,							image_processor=lowerCamelCase_      ,							qformer_tokenizer=lowerCamelCase_    )

            a_    :     str										=    self.prepare_image_inputs()

            a_    :     List[str]										=    image_processor(lowerCamelCase_      ,							return_tensors="""np"""    )
            a_    :     Tuple										=    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 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     List[str]										=    self.get_image_processor()
            a_    :     List[str]										=    self.get_tokenizer()
            a_    :     List[str]										=    self.get_qformer_tokenizer()

            a_    :     List[str]										=    InstructBlipProcessor(
                tokenizer=lowerCamelCase_      ,							image_processor=lowerCamelCase_      ,							qformer_tokenizer=lowerCamelCase_    )

            a_    :     Any										=    """lower newer"""

            a_    :     Optional[int]										=    processor(text=lowerCamelCase_    )

            a_    :     List[str]										=    tokenizer(lowerCamelCase_      ,							return_token_type_ids=lowerCamelCase_    )
            a_    :     str										=    qformer_tokenizer(lowerCamelCase_      ,							return_token_type_ids=lowerCamelCase_    )

            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      :     Any    ):
            a_    :     Union[str, Any]										=    self.get_image_processor()
            a_    :     Union[str, Any]										=    self.get_tokenizer()
            a_    :     List[Any]										=    self.get_qformer_tokenizer()

            a_    :     List[Any]										=    InstructBlipProcessor(
                tokenizer=lowerCamelCase_      ,							image_processor=lowerCamelCase_      ,							qformer_tokenizer=lowerCamelCase_    )

            a_    :     Optional[int]										=    """lower newer"""
            a_    :     Optional[Any]										=    self.prepare_image_inputs()

            a_    :     Tuple										=    processor(text=lowerCamelCase_      ,							images=lowerCamelCase_    )

            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(lowerCamelCase_    ):
                 processor()

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

            a_    :     Dict										=    InstructBlipProcessor(
                tokenizer=lowerCamelCase_      ,							image_processor=lowerCamelCase_      ,							qformer_tokenizer=lowerCamelCase_    )

            a_    :     Any										=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            a_    :     Optional[Any]										=    processor.batch_decode(lowerCamelCase_    )
            a_    :     Union[str, Any]										=    tokenizer.batch_decode(lowerCamelCase_    )

            self.assertListEqual(lowerCamelCase_      ,							lowerCamelCase_    )

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

            a_    :     Union[str, Any]										=    InstructBlipProcessor(
                tokenizer=lowerCamelCase_      ,							image_processor=lowerCamelCase_      ,							qformer_tokenizer=lowerCamelCase_    )

            a_    :     Tuple										=    """lower newer"""
            a_    :     Union[str, Any]										=    self.prepare_image_inputs()

            a_    :     Tuple										=    processor(text=lowerCamelCase_      ,							images=lowerCamelCase_    )

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







from datetime import datetime as dt
import os

from github import Github


__lowerCamelCase        =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]



def  _a			(						):
     a_    :     List[str]										=    Github(os.environ["""GITHUB_TOKEN"""] )
     a_    :     str										=    g.get_repo("""huggingface/transformers""" )
     a_    :     List[str]										=    repo.get_issues(state="""open""" )

     for issue in open_issues:
          a_    :     Optional[Any]										=    sorted([comment for comment in issue.get_comments()]  ,  key=lambda __UpperCamelCase : i.created_at  ,  reverse=__UpperCamelCase )
          a_    :     List[Any]										=    comments[0] if len(__UpperCamelCase ) > 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 >= 3_0
              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 > 2_3
              and (dt.utcnow() - issue.created_at).days >= 3_0
              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()



