



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       lowercase_	(				self			:  Any     ):
      a						: Union[str, Any]					       =					tempfile.mkdtemp()

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

      a						: Dict					       =					InstructBlipProcessor(__snake_case						,      __snake_case						,      __snake_case     )

      processor.save_pretrained(self.tmpdirname     )







    def       lowercase_	(				self			:  Optional[int]						,      **__snake_case			:  str     ):
      return AutoProcessor.from_pretrained(self.tmpdirname						,      **__snake_case     ).tokenizer







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







    def       lowercase_	(				self			:  int						,      **__snake_case			:  Dict     ):
      return AutoProcessor.from_pretrained(self.tmpdirname						,      **__snake_case     ).qformer_tokenizer







    def       lowercase_	(				self			:  Union[str, Any]     ):
      shutil.rmtree(self.tmpdirname     )







    def       lowercase_	(				self			:  str     ):
      a						: str					       =					[np.random.randint(2_55						,      size=(3, 30, 4_00)						,      dtype=np.uinta     )]

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

      return image_inputs







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Tuple					       =					InstructBlipProcessor(
          tokenizer=self.get_tokenizer()						,      image_processor=self.get_image_processor()						,      qformer_tokenizer=self.get_qformer_tokenizer()						,      )
      processor.save_pretrained(self.tmpdirname     )

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

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

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

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







    def       lowercase_	(				self			:  Union[str, Any]     ):
      a						: List[str]					       =					self.get_image_processor()
      a						: str					       =					self.get_tokenizer()
      a						: Optional[int]					       =					self.get_qformer_tokenizer()

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

      a						: Any					       =					self.prepare_image_inputs()

      a						: Any					       =					image_processor(__snake_case						,      return_tensors='np'     )
      a						: int					       =					processor(images=__snake_case						,      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       lowercase_	(				self			:  Union[str, Any]     ):
      a						: int					       =					self.get_image_processor()
      a						: Dict					       =					self.get_tokenizer()
      a						: str					       =					self.get_qformer_tokenizer()

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

      a						: Union[str, Any]					       =					'lower newer'

      a						: Any					       =					processor(text=__snake_case     )

      a						: int					       =					tokenizer(__snake_case						,      return_token_type_ids=__snake_case     )
      a						: int					       =					qformer_tokenizer(__snake_case						,      return_token_type_ids=__snake_case     )

      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       lowercase_	(				self			:  Tuple     ):
      a						: Tuple					       =					self.get_image_processor()
      a						: Tuple					       =					self.get_tokenizer()
      a						: Any					       =					self.get_qformer_tokenizer()

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

      a						: Tuple					       =					'lower newer'
      a						: Optional[Any]					       =					self.prepare_image_inputs()

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

      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(__snake_case     ):
        processor()







    def       lowercase_	(				self			:  List[str]     ):
      a						: str					       =					self.get_image_processor()
      a						: int					       =					self.get_tokenizer()
      a						: List[Any]					       =					self.get_qformer_tokenizer()

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

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

      a						: List[str]					       =					processor.batch_decode(__snake_case     )
      a						: Optional[int]					       =					tokenizer.batch_decode(__snake_case     )

      self.assertListEqual(__snake_case						,      __snake_case     )







    def       lowercase_	(				self			:  List[str]     ):
      a						: List[Any]					       =					self.get_image_processor()
      a						: str					       =					self.get_tokenizer()
      a						: List[str]					       =					self.get_qformer_tokenizer()

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

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

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

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




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     lowerCAmelCase: Any							=						argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     lowerCAmelCase: Optional[int]							=						parser.parse_args()

     lowerCAmelCase: List[Any]							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     lowerCAmelCase: Optional[Any]							=						CLIPImageProcessor()
     lowerCAmelCase: Tuple							=						CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     lowerCAmelCase: List[str]							=						UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)