
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor




class      __A:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=10	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=0.6	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    patch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    is_training
       UpperCamelCase__				=    use_labels
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout_prob
       UpperCamelCase__				=    attention_probs_dropout_prob
       UpperCamelCase__				=    type_sequence_label_size
       UpperCamelCase__				=    initializer_range
       UpperCamelCase__				=    mask_ratio
       UpperCamelCase__				=    scope

       # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
       # (we add 1 for the [CLS] token)
       UpperCamelCase__				=    (image_size // patch_size) ** 2
       UpperCamelCase__				=    int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       UpperCamelCase__				=    None
       if self.use_labels:
            UpperCamelCase__				=    ids_tensor([self.batch_size]	,      self.type_sequence_label_size						)

       UpperCamelCase__				=    self.get_config()

       return config, pixel_values, labels







  def 	UpperCAmelCase_ (self						):
       return ViTMAEConfig(
           image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      decoder_hidden_size=self.hidden_size	,      decoder_num_hidden_layers=self.num_hidden_layers	,      decoder_num_attention_heads=self.num_attention_heads	,      decoder_intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      is_decoder=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      mask_ratio=self.mask_ratio	,      )







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    TFViTMAEModel(config=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      training=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      training=SCREAMING_SNAKE_CASE_						)
       # expected sequence length = num_patches
       UpperCamelCase__				=    (self.image_size // self.patch_size) ** 2
       UpperCamelCase__				=    self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, num_patches, expected_num_channels)						)

       # test greyscale images
       UpperCamelCase__				=    1
       UpperCamelCase__				=    TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      training=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.patch_size**2
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, num_patches, expected_num_channels)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.prepare_config_and_inputs()
       ((UpperCamelCase__)		,	(UpperCamelCase__)		,	(UpperCamelCase__))				=    config_and_inputs
       UpperCamelCase__				=    {"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_tf
class      __A(    __lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__         =      {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    TFViTMAEModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      has_text_modality=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







  def 	UpperCAmelCase_ (self						):
       self.config_tester.run_common_tests()







  @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""						)
  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            self.assertIsInstance(model.get_input_embeddings()	,      (tf.keras.layers.Layer)						)
            UpperCamelCase__				=    model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_	,      tf.keras.layers.Layer						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    inspect.signature(model.call						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCamelCase__				=    [*signature.parameters.keys()]

            UpperCamelCase__				=    ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # make the mask reproducible
       np.random.seed(2						)

       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__				=    int((config.image_size // config.patch_size) ** 2						)
       UpperCamelCase__				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

            UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)

            UpperCamelCase__				=    copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)						)
            UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    outputs_dict[0].numpy()
            UpperCamelCase__				=    outputs_keywords[0].numpy()

            self.assertLess(np.sum(np.abs(output_dict - output_keywords						)						)	,      1E-6						)







  def 	UpperCAmelCase_ (self						):
       # make the mask reproducible
       np.random.seed(2						)

       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__				=    int((config.image_size // config.patch_size) ** 2						)
       UpperCamelCase__				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

       def prepare_numpy_arrays(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    {}
            for k, v in inputs_dict.items():
                 if tf.is_tensor(SCREAMING_SNAKE_CASE_						):
                      UpperCamelCase__				=    v.numpy()
                 else:
                      UpperCamelCase__				=    np.array(SCREAMING_SNAKE_CASE_						)

            return inputs_np_dict

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)

            UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    prepare_numpy_arrays(SCREAMING_SNAKE_CASE_						)

            UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)
            self.assert_outputs_same(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       # make masks reproducible
       np.random.seed(2						)

       UpperCamelCase__				=    int((tf_model.config.image_size // tf_model.config.patch_size) ** 2						)
       UpperCamelCase__				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
       UpperCamelCase__				=    tf.constant(SCREAMING_SNAKE_CASE_						)

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       UpperCamelCase__				=    tf_noise

       super().check_pt_tf_models(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # make mask reproducible
       np.random.seed(2						)

       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__				=    {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__						),)
           for module_member_name in dir(SCREAMING_SNAKE_CASE_						)
           if module_member_name.endswith("""MainLayer"""						)
           # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
           and module_member_name[: -len("""MainLayer"""						)] == model_class.__name__[: -len("""Model"""						)]
           for module_member in (getattr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						),)
           if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(SCREAMING_SNAKE_CASE_	,      """_keras_serializable"""	,      SCREAMING_SNAKE_CASE_						)
       }

       UpperCamelCase__				=    int((config.image_size // config.patch_size) ** 2						)
       UpperCamelCase__				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
       UpperCamelCase__				=    tf.convert_to_tensor(SCREAMING_SNAKE_CASE_						)
       inputs_dict.update({"""noise""": noise}						)

       for main_layer_class in tf_main_layer_classes:
            UpperCamelCase__				=    main_layer_class(SCREAMING_SNAKE_CASE_						)

            UpperCamelCase__				=    {
                name: tf.keras.Input(tensor.shape[1:]	,      dtype=tensor.dtype						) for name, tensor in inputs_dict.items()
            }

            UpperCamelCase__				=    tf.keras.Model(SCREAMING_SNAKE_CASE_	,      outputs=main_layer(SCREAMING_SNAKE_CASE_						)						)
            UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 UpperCamelCase__				=    os.path.join(SCREAMING_SNAKE_CASE_	,      """keras_model.h5"""						)
                 model.save(SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    tf.keras.models.load_model(
                     SCREAMING_SNAKE_CASE_	,      custom_objects={main_layer_class.__name__: main_layer_class}						)
                 assert isinstance(SCREAMING_SNAKE_CASE_	,      tf.keras.Model						)
                 UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
                 self.assert_outputs_same(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       # make mask reproducible
       np.random.seed(2						)

       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__				=    int((config.image_size // config.patch_size) ** 2						)
       UpperCamelCase__				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)

            if model_class.__name__ == "TFViTMAEModel":
                 UpperCamelCase__				=    outputs.last_hidden_state.numpy()
                 UpperCamelCase__				=    0
            else:
                 UpperCamelCase__				=    outputs.logits.numpy()
                 UpperCamelCase__				=    0

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(SCREAMING_SNAKE_CASE_	,      saved_model=SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    model_class.from_pretrained(SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)

                 if model_class.__name__ == "TFViTMAEModel":
                      UpperCamelCase__				=    after_outputs["""last_hidden_state"""].numpy()
                      UpperCamelCase__				=    0
                 else:
                      UpperCamelCase__				=    after_outputs["""logits"""].numpy()
                      UpperCamelCase__				=    0

                 UpperCamelCase__				=    np.amax(np.abs(out_a - out_a						)						)
                 self.assertLessEqual(SCREAMING_SNAKE_CASE_	,      1E-5						)







  def 	UpperCAmelCase_ (self						):
       # make mask reproducible
       np.random.seed(2						)

       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__				=    int((config.image_size // config.patch_size) ** 2						)
       UpperCamelCase__				=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

            UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model_class.from_config(model.get_config()						)
            # make sure it also accepts a normal config
            UpperCamelCase__				=    model_class.from_config(model.config						)
            UpperCamelCase__				=    new_model(SCREAMING_SNAKE_CASE_						)  # Build model
            new_model.set_weights(model.get_weights()						)
            UpperCamelCase__				=    new_model(SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)

            self.assert_outputs_same(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""						)
  def 	UpperCAmelCase_ (self						):
       pass







  @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""						)
  def 	UpperCAmelCase_ (self						):
       pass







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""						)
       self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     return image




@require_tf
@require_vision
class      __A(    unittest.TestCase			):

  """simple docstring"""







  @cached_property
  def 	UpperCAmelCase_ (self						):
       return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""						) if is_vision_available() else None







  @slow
  def 	UpperCAmelCase_ (self						):
       # make random mask reproducible across the PT and TF model
       np.random.seed(2						)

       UpperCamelCase__				=    TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""						)

       UpperCamelCase__				=    self.default_image_processor
       UpperCamelCase__				=    prepare_img()
       UpperCamelCase__				=    image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""tf"""						)

       # prepare a noise vector that will be also used for testing the TF model
       # (this way we can ensure that the PT and TF models operate on the same inputs)
       UpperCamelCase__				=    ViTMAEConfig()
       UpperCamelCase__				=    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
       UpperCamelCase__				=    np.random.uniform(size=(1, num_patches)						)

       # forward pass
       UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_	,      noise=SCREAMING_SNAKE_CASE_						)

       # verify the logits
       UpperCamelCase__				=    tf.convert_to_tensor([1, 1_96, 7_68]						)
       self.assertEqual(outputs.logits.shape	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    tf.convert_to_tensor(
           [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]						)

       tf.debugging.assert_near(outputs.logits[0, :3, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-4						)







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"""]	,      )





