



"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast


@require_vision
class    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= tempfile.mkdtemp()

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

      lowercase_								= BlipaProcessor(lowerCAmelCase_ ,  lowerCAmelCase_)

      processor.save_pretrained(self.tmpdirname)

    def 					_UpperCAmelCase	(				self			:				int ,  **lowerCAmelCase_			:				Dict):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any ,  **lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      shutil.rmtree(self.tmpdirname)

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







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

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

      return image_inputs

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= BlipaProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor())
      processor.save_pretrained(self.tmpdirname)

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

      lowercase_								= BlipaProcessor.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_)

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= self.get_image_processor()
      lowercase_								= self.get_tokenizer()

      lowercase_								= BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_)

      lowercase_								= self.prepare_image_inputs()

      lowercase_								= image_processor(lowerCAmelCase_ ,  return_tensors="""np""")
      lowercase_								= 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			:				str):







      """simple docstring"""







      lowercase_								= self.get_image_processor()
      lowercase_								= self.get_tokenizer()

      lowercase_								= BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_)

      lowercase_								= """lower newer"""

      lowercase_								= processor(text=lowerCAmelCase_)

      lowercase_								= tokenizer(lowerCAmelCase_ ,  return_token_type_ids=lowerCAmelCase_)

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

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= self.get_image_processor()
      lowercase_								= self.get_tokenizer()

      lowercase_								= BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_)

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

      lowercase_								= processor(text=lowerCAmelCase_ ,  images=lowerCAmelCase_)

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

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

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.get_image_processor()
      lowercase_								= self.get_tokenizer()

      lowercase_								= BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_)

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

      lowercase_								= processor.batch_decode(lowerCAmelCase_)
      lowercase_								= tokenizer.batch_decode(lowerCAmelCase_)

      self.assertListEqual(lowerCAmelCase_ ,  lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.get_image_processor()
      lowercase_								= self.get_tokenizer()

      lowercase_								= BlipaProcessor(tokenizer=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_)

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

      lowercase_								= processor(text=lowerCAmelCase_ ,  images=lowerCAmelCase_)

      # 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase						:		Any			= {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Any			= ["BlipImageProcessor"]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Optional[int]			= [
           "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlipModel",
           "BlipPreTrainedModel",
           "BlipForConditionalGeneration",
           "BlipForQuestionAnswering",
           "BlipVisionModel",
           "BlipTextModel",
           "BlipForImageTextRetrieval",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFBlipModel",
           "TFBlipPreTrainedModel",
           "TFBlipForConditionalGeneration",
           "TFBlipForQuestionAnswering",
           "TFBlipVisionModel",
           "TFBlipTextModel",
           "TFBlipForImageTextRetrieval",
       ]

if TYPE_CHECKING:
       from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
       from .processing_blip import BlipProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_blip import BlipImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blip import (
                  BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlipForConditionalGeneration,
                  BlipForImageTextRetrieval,
                  BlipForQuestionAnswering,
                  BlipModel,
                  BlipPreTrainedModel,
                  BlipTextModel,
                  BlipVisionModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blip import (
                  TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFBlipForConditionalGeneration,
                  TFBlipForImageTextRetrieval,
                  TFBlipForQuestionAnswering,
                  TFBlipModel,
                  TFBlipPreTrainedModel,
                  TFBlipTextModel,
                  TFBlipVisionModel,
              )

else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

