



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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast



@require_vision
class 							lowerCAmelCase_						(    unittest.TestCase				):
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    List[str]		      =      tempfile.mkdtemp()

          UpperCamelCase       :    str		      =      BlipImageProcessor()
          UpperCamelCase       :    str		      =      BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'			)

          UpperCamelCase       :    Dict		      =      BlipProcessor(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          processor.save_pretrained(self.tmpdirname			)
       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						Dict:
          return AutoProcessor.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_			).tokenizer
       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						str:
          return AutoProcessor.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_			).image_processor
       def    snake_case_			(     self			)	->						Any:
          shutil.rmtree(self.tmpdirname			)
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    Union[str, Any]		      =      [np.random.randint(255,     size=(3, 30, 400),     dtype=np.uinta			)]

          UpperCamelCase       :    Union[str, Any]		      =      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_,     0,     -1			)			) for x in image_inputs]

          return image_inputs
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Optional[Any]		      =      BlipProcessor(tokenizer=self.get_tokenizer(),     image_processor=self.get_image_processor()			)
          processor.save_pretrained(self.tmpdirname			)

          UpperCamelCase       :    Dict		      =      self.get_tokenizer(bos_token='(BOS)',     eos_token='(EOS)'			)
          UpperCamelCase       :    Any		      =      self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_,     padding_value=1.0			)

          UpperCamelCase       :    Optional[int]		      =      BlipProcessor.from_pretrained(
              self.tmpdirname,     bos_token='(BOS)',     eos_token='(EOS)',     do_normalize=SCREAMING_SNAKE_CASE_,     padding_value=1.0			)

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

          self.assertEqual(processor.image_processor.to_json_string(),     image_processor_add_kwargs.to_json_string()			)
          self.assertIsInstance(processor.image_processor,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    List[Any]		      =      self.get_image_processor()
          UpperCamelCase       :    List[str]		      =      self.get_tokenizer()

          UpperCamelCase       :    Tuple		      =      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase       :    List[str]		      =      image_processor(SCREAMING_SNAKE_CASE_,     return_tensors='np'			)
          UpperCamelCase       :    int		      =      processor(images=SCREAMING_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    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    List[Any]		      =      self.get_image_processor()
          UpperCamelCase       :    List[str]		      =      self.get_tokenizer()

          UpperCamelCase       :    List[str]		      =      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Tuple		      =      'lower newer'

          UpperCamelCase       :    Union[str, Any]		      =      processor(text=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Tuple		      =      tokenizer(SCREAMING_SNAKE_CASE_,     return_token_type_ids=SCREAMING_SNAKE_CASE_			)

          for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key],     encoded_processor[key]			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Any		      =      self.get_image_processor()
          UpperCamelCase       :    str		      =      self.get_tokenizer()

          UpperCamelCase       :    Dict		      =      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Dict		      =      'lower newer'
          UpperCamelCase       :    List[Any]		      =      self.prepare_image_inputs()

          UpperCamelCase       :    List[Any]		      =      processor(text=SCREAMING_SNAKE_CASE_,     images=SCREAMING_SNAKE_CASE_			)

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

          # test if it raises when no input is passed
          with pytest.raises(SCREAMING_SNAKE_CASE_			):
             processor()
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    Union[str, Any]		      =      self.get_image_processor()
          UpperCamelCase       :    str		      =      self.get_tokenizer()

          UpperCamelCase       :    List[Any]		      =      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase       :    List[str]		      =      processor.batch_decode(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

          self.assertListEqual(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    List[str]		      =      self.get_image_processor()
          UpperCamelCase       :    List[Any]		      =      self.get_tokenizer()

          UpperCamelCase       :    List[str]		      =      BlipProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Any		      =      'lower newer'
          UpperCamelCase       :    str		      =      self.prepare_image_inputs()

          UpperCamelCase       :    List[Any]		      =      processor(text=SCREAMING_SNAKE_CASE_,     images=SCREAMING_SNAKE_CASE_			)

          # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
          self.assertListEqual(list(inputs.keys()			),     ['pixel_values', 'input_ids', 'attention_mask']			)




'''simple docstring'''


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import (
					    ImageTextPipelineOutput,
					    UniDiffuserPipeline,
					)
else:
					from .modeling_text_decoder import UniDiffuserTextDecoder
					from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
					from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
