
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    size if size is not None else {"""shortest_edge""": 20}
       UpperCamelCase__				=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    do_resize
       UpperCamelCase__				=    size
       UpperCamelCase__				=    do_center_crop
       UpperCamelCase__				=    crop_size







  def 	UpperCAmelCase_ (self						):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }






@require_torch
@require_vision
class      __A(    __snake_case    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      MobileNetVaImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    MobileNetVaImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(lowerCamelCase_	,      """do_resize"""						)						)
       self.assertTrue(hasattr(lowerCamelCase_	,      """size"""						)						)
       self.assertTrue(hasattr(lowerCamelCase_	,      """do_center_crop"""						)						)
       self.assertTrue(hasattr(lowerCamelCase_	,      """crop_size"""						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size	,      {"""shortest_edge""": 20}						)
       self.assertEqual(image_processor.crop_size	,      {"""height""": 18, """width""": 18}						)

       UpperCamelCase__				=    self.image_processing_class.from_dict(self.image_processor_dict	,      size=42	,      crop_size=84						)
       self.assertEqual(image_processor.size	,      {"""shortest_edge""": 42}						)
       self.assertEqual(image_processor.crop_size	,      {"""height""": 84, """width""": 84}						)







  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=lowerCamelCase_						)
       for image in image_inputs:
            self.assertIsInstance(lowerCamelCase_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape	,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )	,      )

       # Test batched
       UpperCamelCase__				=    image_processing(lowerCamelCase_	,      return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape	,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=lowerCamelCase_	,      numpify=lowerCamelCase_						)
       for image in image_inputs:
            self.assertIsInstance(lowerCamelCase_	,      np.ndarray						)

       # Test not batched input
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape	,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )	,      )

       # Test batched
       UpperCamelCase__				=    image_processing(lowerCamelCase_	,      return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape	,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=lowerCamelCase_	,      torchify=lowerCamelCase_						)
       for image in image_inputs:
            self.assertIsInstance(lowerCamelCase_	,      torch.Tensor						)

       # Test not batched input
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape	,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )	,      )

       # Test batched
       UpperCamelCase__				=    image_processing(lowerCamelCase_	,      return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape	,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )	,      )







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			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    tempfile.mkdtemp()

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

       UpperCamelCase__				=    InstructBlipProcessor(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       processor.save_pretrained(self.tmpdirname						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).tokenizer







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).image_processor







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).qformer_tokenizer







  def 	UpperCAmelCase_ (self						):
       shutil.rmtree(self.tmpdirname						)







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

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

       return image_inputs







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
       processor.save_pretrained(self.tmpdirname						)

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

       UpperCamelCase__				=    InstructBlipProcessor.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_						)
       self.assertIsInstance(processor.qformer_tokenizer	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""np"""						)
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_token_type_ids=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    qformer_tokenizer(SCREAMING_SNAKE_CASE_	,      return_token_type_ids=SCREAMING_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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_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(SCREAMING_SNAKE_CASE_						):
            processor()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    processor.batch_decode(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_SNAKE_CASE_						)

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





