
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        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					tempfile.mkdtemp()

          snake_case__    =					BlipImageProcessor()
          snake_case__    =					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"			)
          snake_case__    =					BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"			)

          snake_case__    =					InstructBlipProcessor(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)

          processor.save_pretrained(self.tmpdirname			)
      def A_      (      self		,						**lowerCamelCase			):
          return AutoProcessor.from_pretrained(self.tmpdirname		,						**lowerCamelCase			).tokenizer
      def A_      (      self		,						**lowerCamelCase			):
          return AutoProcessor.from_pretrained(self.tmpdirname		,						**lowerCamelCase			).image_processor
      def A_      (      self		,						**lowerCamelCase			):
          return AutoProcessor.from_pretrained(self.tmpdirname		,						**lowerCamelCase			).qformer_tokenizer
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					[np.random.randint(2_55		,						size=(3, 30, 4_00)		,						dtype=np.uinta			)]

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

          return image_inputs
      def A_      (      self			):
          snake_case__    =					InstructBlipProcessor(
              tokenizer=self.get_tokenizer()		,						image_processor=self.get_image_processor()		,						qformer_tokenizer=self.get_qformer_tokenizer()		,						)
          processor.save_pretrained(self.tmpdirname			)

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

          snake_case__    =					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 A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()
          snake_case__    =					self.get_qformer_tokenizer()

          snake_case__    =					InstructBlipProcessor(
              tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase		,						qformer_tokenizer=lowerCamelCase			)

          snake_case__    =					self.prepare_image_inputs()

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					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			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()
          snake_case__    =					self.get_qformer_tokenizer()

          snake_case__    =					InstructBlipProcessor(
              tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase		,						qformer_tokenizer=lowerCamelCase			)

          snake_case__    =					"lower newer"

          snake_case__    =					processor(text=lowerCamelCase			)

          snake_case__    =					tokenizer(lowerCamelCase		,						return_token_type_ids=lowerCamelCase			)
          snake_case__    =					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 A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()
          snake_case__    =					self.get_qformer_tokenizer()

          snake_case__    =					InstructBlipProcessor(
              tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase		,						qformer_tokenizer=lowerCamelCase			)

          snake_case__    =					"lower newer"
          snake_case__    =					self.prepare_image_inputs()

          snake_case__    =					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 A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()
          snake_case__    =					self.get_qformer_tokenizer()

          snake_case__    =					InstructBlipProcessor(
              tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase		,						qformer_tokenizer=lowerCamelCase			)

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

          snake_case__    =					processor.batch_decode(lowerCamelCase			)
          snake_case__    =					tokenizer.batch_decode(lowerCamelCase			)

          self.assertListEqual(lowerCamelCase		,						lowerCamelCase			)



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()
          snake_case__    =					self.get_qformer_tokenizer()

          snake_case__    =					InstructBlipProcessor(
              tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase		,						qformer_tokenizer=lowerCamelCase			)

          snake_case__    =					"lower newer"
          snake_case__    =					self.prepare_image_inputs()

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

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





import os
from datetime import datetime as dt

from github import Github


__magic_name__  							=	[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Github(os.environ["GITHUB_TOKEN"]				)
    snake_case__    =					g.get_repo("huggingface/accelerate"				)
    snake_case__    =					repo.get_issues(state="open"				)

    for issue in open_issues:
        snake_case__    =					sorted([comment for comment in issue.get_comments()]     ,		key=lambda __lowerCAmelCase				: i.created_at     ,		reverse=__lowerCAmelCase				)
        snake_case__    =					comments[0] if len(__lowerCAmelCase				) > 0 else None
        snake_case__    =					dt.utcnow()
        snake_case__    =					(current_time - issue.updated_at).days
        snake_case__    =					(current_time - issue.created_at).days
        if (
            last_comment is not None
            and last_comment.user.login == "github-actions[bot]"
            and days_since_updated > 7
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Close issue since it has been 7 days of inactivity since bot mention.
            issue.edit(state="closed"				)
        elif (
            days_since_updated > 23
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Add stale comment
            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/accelerate/blob/main/CONTRIBUTING.md) "
                "are likely to be ignored."				)


if __name__ == "__main__":
     main()



