


'''simple docstring'''





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 				UpperCAmelCase			:


 def __init__(      self							:	Tuple			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Tuple=1_3			,       __lowerCamelCase							:	Any=3_0			,       __lowerCamelCase							:	Dict=2			,       __lowerCamelCase							:	int=3			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	Any=True			,       __lowerCamelCase							:	Tuple=3_2			,       __lowerCamelCase							:	int=2			,       __lowerCamelCase							:	Dict=4			,       __lowerCamelCase							:	str=3_7			,       __lowerCamelCase							:	str="gelu"			,       __lowerCamelCase							:	Union[str, Any]=0.1			,       __lowerCamelCase							:	Union[str, Any]=0.1			,       __lowerCamelCase							:	Any=1_0			,       __lowerCamelCase							:	Optional[Any]=0.02			,       __lowerCamelCase							:	Tuple=3			,       __lowerCamelCase							:	Optional[int]=0.6			,       __lowerCamelCase							:	str=None			,       ):
    UpperCAmelCase__	:Dict      =      parent
    UpperCAmelCase__	:Dict      =      batch_size
    UpperCAmelCase__	:Any      =      image_size
    UpperCAmelCase__	:List[Any]      =      patch_size
    UpperCAmelCase__	:Any      =      num_channels
    UpperCAmelCase__	:int      =      is_training
    UpperCAmelCase__	:str      =      use_labels
    UpperCAmelCase__	:Optional[int]      =      hidden_size
    UpperCAmelCase__	:str      =      num_hidden_layers
    UpperCAmelCase__	:Dict      =      num_attention_heads
    UpperCAmelCase__	:List[str]      =      intermediate_size
    UpperCAmelCase__	:Optional[Any]      =      hidden_act
    UpperCAmelCase__	:Dict      =      hidden_dropout_prob
    UpperCAmelCase__	:Optional[int]      =      attention_probs_dropout_prob
    UpperCAmelCase__	:Tuple      =      type_sequence_label_size
    UpperCAmelCase__	:List[str]      =      initializer_range
    UpperCAmelCase__	:Optional[int]      =      mask_ratio
    UpperCAmelCase__	:List[Any]      =      scope

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:List[str]      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

    UpperCAmelCase__	:List[str]      =      None
    if self.use_labels:
       UpperCAmelCase__	:int      =      ids_tensor([self.batch_size]			,       self.type_sequence_label_size )

    UpperCAmelCase__	:Optional[int]      =      self.get_config()

    return config, pixel_values, labels


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    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=__lowerCamelCase			,       initializer_range=self.initializer_range			,       mask_ratio=self.mask_ratio			,       )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	Any ):
    UpperCAmelCase__	:Dict      =      TFViTMAEModel(config=__lowerCamelCase )
    UpperCAmelCase__	:Any      =      model(__lowerCamelCase			,       training=__lowerCamelCase )
    self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      TFViTMAEForPreTraining(__lowerCamelCase )
    UpperCAmelCase__	:Any      =      model(__lowerCamelCase			,       training=__lowerCamelCase )
    # expected sequence length = num_patches
    UpperCAmelCase__	:Dict      =      (self.image_size // self.patch_size) ** 2
    UpperCAmelCase__	:Any      =      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__	:Union[str, Any]      =      1
    UpperCAmelCase__	:int      =      TFViTMAEForPreTraining(__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
    UpperCAmelCase__	:Tuple      =      model(__lowerCamelCase			,       training=__lowerCamelCase )
    UpperCAmelCase__	:str      =      self.patch_size**2
    self.parent.assertEqual(result.logits.shape			,       (self.batch_size, num_patches, expected_num_channels) )





 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:str      =      self.prepare_config_and_inputs()
    ((UpperCAmelCase__)   ,						(UpperCAmelCase__)   ,						(UpperCAmelCase__))	:List[str]      =      config_and_inputs
    UpperCAmelCase__	:Any      =      {'''pixel_values''': pixel_values}
    return config, inputs_dict




@require_tf
class 				UpperCAmelCase			(			_snake_case						, _snake_case						, unittest.TestCase						):
 UpperCAmelCase				=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
 UpperCAmelCase				=					{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

 UpperCAmelCase				=					False
 UpperCAmelCase				=					False
 UpperCAmelCase				=					False
 UpperCAmelCase				=					False


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Any      =      TFViTMAEModelTester(self )
    UpperCAmelCase__	:Optional[int]      =      ConfigTester(self			,       config_class=__lowerCamelCase			,       has_text_modality=__lowerCamelCase			,       hidden_size=3_7 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    self.config_tester.run_common_tests()


 @unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    pass


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:Any      =      self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCAmelCase__	:Tuple      =      model_class(__lowerCamelCase )
       self.assertIsInstance(model.get_input_embeddings()			,       (tf.keras.layers.Layer) )
       UpperCAmelCase__	:Union[str, Any]      =      model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__lowerCamelCase			,       tf.keras.layers.Layer ) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase__	:Union[str, Any]      =      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:str      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Dict      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    # make the mask reproducible
    np.random.seed(2 )

    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase__	:Any      =      int((config.image_size // config.patch_size) ** 2 )
    UpperCAmelCase__	:Optional[int]      =      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

    for model_class in self.all_model_classes:
       UpperCAmelCase__	:Tuple      =      model_class(__lowerCamelCase )
       UpperCAmelCase__	:List[Any]      =      self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase )

       UpperCAmelCase__	:Optional[Any]      =      model(__lowerCamelCase			,       noise=__lowerCamelCase )

       UpperCAmelCase__	:Dict      =      copy.deepcopy(self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase ) )
       UpperCAmelCase__	:Dict      =      model(**__lowerCamelCase			,       noise=__lowerCamelCase )
       UpperCAmelCase__	:Dict      =      outputs_dict[0].numpy()
       UpperCAmelCase__	:Union[str, Any]      =      outputs_keywords[0].numpy()

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    # make the mask reproducible
    np.random.seed(2 )

    UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase__	:str      =      int((config.image_size // config.patch_size) ** 2 )
    UpperCAmelCase__	:Optional[int]      =      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

    def prepare_numpy_arrays(__lowerCamelCase							:	List[Any] ):
       UpperCAmelCase__	:str      =      {}
       for k, v in inputs_dict.items():
          if tf.is_tensor(__lowerCamelCase ):
             UpperCAmelCase__	:Tuple      =      v.numpy()
          else:
             UpperCAmelCase__	:Union[str, Any]      =      np.array(__lowerCamelCase )

       return inputs_np_dict

    for model_class in self.all_model_classes:
       UpperCAmelCase__	:int      =      model_class(__lowerCamelCase )

       UpperCAmelCase__	:Tuple      =      self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase )
       UpperCAmelCase__	:int      =      prepare_numpy_arrays(__lowerCamelCase )

       UpperCAmelCase__	:Union[str, Any]      =      model(__lowerCamelCase			,       noise=__lowerCamelCase )
       UpperCAmelCase__	:Dict      =      model(**__lowerCamelCase			,       noise=__lowerCamelCase )
       self.assert_outputs_same(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Dict ):
    # make masks reproducible
    np.random.seed(2 )

    UpperCAmelCase__	:Optional[int]      =      int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
    UpperCAmelCase__	:Tuple      =      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
    UpperCAmelCase__	:Union[str, Any]      =      tf.constant(__lowerCamelCase )

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

    super().check_pt_tf_models(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    # make mask reproducible
    np.random.seed(2 )

    UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase__	:List[Any]      =      {
        module_member
        for model_class in self.all_model_classes
        for module in (import_module(model_class.__module__ ),)
        for module_member_name in dir(__lowerCamelCase )
        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(__lowerCamelCase			,       __lowerCamelCase ),)
        if isinstance(__lowerCamelCase			,       __lowerCamelCase )
        and tf.keras.layers.Layer in module_member.__bases__
        and getattr(__lowerCamelCase			,       '''_keras_serializable'''			,       __lowerCamelCase )
    }

    UpperCAmelCase__	:Tuple      =      int((config.image_size // config.patch_size) ** 2 )
    UpperCAmelCase__	:str      =      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
    UpperCAmelCase__	:Tuple      =      tf.convert_to_tensor(__lowerCamelCase )
    inputs_dict.update({'''noise''': noise} )

    for main_layer_class in tf_main_layer_classes:
       UpperCAmelCase__	:Union[str, Any]      =      main_layer_class(__lowerCamelCase )

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

       UpperCAmelCase__	:Dict      =      tf.keras.Model(__lowerCamelCase			,       outputs=main_layer(__lowerCamelCase ) )
       UpperCAmelCase__	:List[Any]      =      model(__lowerCamelCase )

       with tempfile.TemporaryDirectory() as tmpdirname:
          UpperCAmelCase__	:Optional[int]      =      os.path.join(__lowerCamelCase			,       '''keras_model.h5''' )
          model.save(__lowerCamelCase )
          UpperCAmelCase__	:Optional[int]      =      tf.keras.models.load_model(
              __lowerCamelCase			,       custom_objects={main_layer_class.__name__: main_layer_class} )
          assert isinstance(__lowerCamelCase			,       tf.keras.Model )
          UpperCAmelCase__	:Tuple      =      model(__lowerCamelCase )
          self.assert_outputs_same(__lowerCamelCase			,       __lowerCamelCase )


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

    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase__	:Optional[Any]      =      int((config.image_size // config.patch_size) ** 2 )
    UpperCAmelCase__	:List[str]      =      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

    for model_class in self.all_model_classes:
       UpperCAmelCase__	:str      =      model_class(__lowerCamelCase )
       UpperCAmelCase__	:Any      =      self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase )
       UpperCAmelCase__	:Optional[Any]      =      model(__lowerCamelCase			,       noise=__lowerCamelCase )

       if model_class.__name__ == "TFViTMAEModel":
          UpperCAmelCase__	:Union[str, Any]      =      outputs.last_hidden_state.numpy()
          UpperCAmelCase__	:Optional[Any]      =      0
       else:
          UpperCAmelCase__	:Optional[int]      =      outputs.logits.numpy()
          UpperCAmelCase__	:List[Any]      =      0

       with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(__lowerCamelCase			,       saved_model=__lowerCamelCase )
          UpperCAmelCase__	:Optional[int]      =      model_class.from_pretrained(__lowerCamelCase )
          UpperCAmelCase__	:str      =      model(__lowerCamelCase			,       noise=__lowerCamelCase )

          if model_class.__name__ == "TFViTMAEModel":
             UpperCAmelCase__	:Optional[int]      =      after_outputs['''last_hidden_state'''].numpy()
             UpperCAmelCase__	:Any      =      0
          else:
             UpperCAmelCase__	:Optional[int]      =      after_outputs['''logits'''].numpy()
             UpperCAmelCase__	:List[str]      =      0

          UpperCAmelCase__	:str      =      np.amax(np.abs(out_a - out_a ) )
          self.assertLessEqual(__lowerCamelCase			,       1e-5 )


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

    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[int]      =      self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase__	:Optional[Any]      =      int((config.image_size // config.patch_size) ** 2 )
    UpperCAmelCase__	:Dict      =      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

    for model_class in self.all_model_classes:
       UpperCAmelCase__	:Any      =      model_class(__lowerCamelCase )
       UpperCAmelCase__	:Optional[Any]      =      self._prepare_for_class(__lowerCamelCase			,       __lowerCamelCase )

       UpperCAmelCase__	:Optional[Any]      =      model(__lowerCamelCase			,       noise=__lowerCamelCase )
       UpperCAmelCase__	:str      =      model.get_config()
       # make sure that returned config is jsonifiable, which is required by keras
       json.dumps(__lowerCamelCase )
       UpperCAmelCase__	:Optional[Any]      =      model_class.from_config(model.get_config() )
       # make sure it also accepts a normal config
       UpperCAmelCase__	:Optional[int]      =      model_class.from_config(model.config )
       UpperCAmelCase__	:Optional[Any]      =      new_model(__lowerCamelCase )  # Build model
       new_model.set_weights(model.get_weights() )
       UpperCAmelCase__	:List[Any]      =      new_model(__lowerCamelCase			,       noise=__lowerCamelCase )

       self.assert_outputs_same(__lowerCamelCase			,       __lowerCamelCase )


 @unittest.skip(
     reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.''' )
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    pass


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





 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
    self.assertIsNotNone(__lowerCamelCase )


def        a__     (					):
   UpperCAmelCase__	:int      =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
   return image




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


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





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

    UpperCAmelCase__	:Union[str, Any]      =      TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )

    UpperCAmelCase__	:List[Any]      =      self.default_image_processor
    UpperCAmelCase__	:Optional[Any]      =      prepare_img()
    UpperCAmelCase__	:Tuple      =      image_processor(images=__lowerCamelCase			,       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__	:Union[str, Any]      =      ViTMAEConfig()
    UpperCAmelCase__	:Optional[int]      =      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
    UpperCAmelCase__	:Optional[Any]      =      np.random.uniform(size=(1, num_patches) )

    # forward pass
    UpperCAmelCase__	:List[Any]      =      model(**__lowerCamelCase			,       noise=__lowerCamelCase )

    # verify the logits
    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor([1, 1_9_6, 7_6_8] )
    self.assertEqual(outputs.logits.shape			,       __lowerCamelCase )

    UpperCAmelCase__	:Dict      =      tf.convert_to_tensor(
        [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]] )

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







'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"OwlViTImageProcessor"
 UpperCAmelCase				=					("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int]=None			,       __lowerCamelCase							:	Optional[Any]=None			,       **__lowerCamelCase							:	str ):
    UpperCAmelCase__	:int      =      None
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''			,       __lowerCamelCase			,       )
       UpperCAmelCase__	:Union[str, Any]      =      kwargs.pop('''feature_extractor''' )

    UpperCAmelCase__	:List[str]      =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.''' )
    if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.''' )

    super().__init__(__lowerCamelCase			,       __lowerCamelCase )


 def __call__(      self							:	int			,       __lowerCamelCase							:	str=None			,       __lowerCamelCase							:	List[Any]=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	str="max_length"			,       __lowerCamelCase							:	Any="np"			,       **__lowerCamelCase							:	Tuple ):

    if text is None and query_images is None and images is None:
       raise ValueError(
           '''You have to specify at least one text or query image or image. All three cannot be none.''' )

    if text is not None:
       if isinstance(__lowerCamelCase			,       __lowerCamelCase ) or (isinstance(__lowerCamelCase			,       __lowerCamelCase ) and not isinstance(text[0]			,       __lowerCamelCase )):
          UpperCAmelCase__	:Any      =      [self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )]

       elif isinstance(__lowerCamelCase			,       __lowerCamelCase ) and isinstance(text[0]			,       __lowerCamelCase ):
          UpperCAmelCase__	:Tuple      =      []

          # Maximum number of queries across batch
          UpperCAmelCase__	:List[str]      =      max([len(__lowerCamelCase ) for t in text] )

          # Pad all batch samples to max number of text queries
          for t in text:
             if len(__lowerCamelCase ) != max_num_queries:
                UpperCAmelCase__	:str      =      t + [''' '''] * (max_num_queries - len(__lowerCamelCase ))

             UpperCAmelCase__	:Tuple      =      self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )
             encodings.append(__lowerCamelCase )
       else:
          raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )

       if return_tensors == "np":
          UpperCAmelCase__	:List[Any]      =      np.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Any      =      np.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "jax" and is_flax_available():
          import jax.numpy as jnp

          UpperCAmelCase__	:List[Any]      =      jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Union[str, Any]      =      jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "pt" and is_torch_available():
          import torch

          UpperCAmelCase__	:Any      =      torch.cat([encoding['''input_ids'''] for encoding in encodings]			,       dim=0 )
          UpperCAmelCase__	:List[str]      =      torch.cat([encoding['''attention_mask'''] for encoding in encodings]			,       dim=0 )

       elif return_tensors == "tf" and is_tf_available():
          import tensorflow as tf

          UpperCAmelCase__	:Optional[Any]      =      tf.stack([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:int      =      tf.stack([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       else:
          raise ValueError('''Target return tensor type could not be returned''' )

       UpperCAmelCase__	:List[Any]      =      BatchEncoding()
       UpperCAmelCase__	:Union[str, Any]      =      input_ids
       UpperCAmelCase__	:Dict      =      attention_mask

    if query_images is not None:
       UpperCAmelCase__	:Tuple      =      BatchEncoding()
       UpperCAmelCase__	:int      =      self.image_processor(
           __lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase ).pixel_values
       UpperCAmelCase__	:Optional[int]      =      query_pixel_values

    if images is not None:
       UpperCAmelCase__	:str      =      self.image_processor(__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )

    if text is not None and images is not None:
       UpperCAmelCase__	:Dict      =      image_features.pixel_values
       return encoding
    elif query_images is not None and images is not None:
       UpperCAmelCase__	:Tuple      =      image_features.pixel_values
       return encoding
    elif text is not None or query_images is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**__lowerCamelCase )			,       tensor_type=__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	int ):
    return self.image_processor.post_process(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Tuple ):
    return self.image_processor.post_process_object_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       *__lowerCamelCase							:	Tuple			,       **__lowerCamelCase							:	str ):
    return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[Any]			,       **__lowerCamelCase							:	Union[str, Any] ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       *__lowerCamelCase							:	Union[str, Any]			,       **__lowerCamelCase							:	Dict ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor_class





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor



