


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class   __lowercase				:





      def __init__(		self						:					Any					,       __lowerCamelCase						:					Optional[Any]					,       __lowerCamelCase						:					Any=3					,       __lowerCamelCase						:					int=3_2					,       __lowerCamelCase						:					List[str]=3					,       __lowerCamelCase						:					Dict=1_0					,       __lowerCamelCase						:					List[Any]=[1_0, 2_0, 3_0, 4_0]					,       __lowerCamelCase						:					int=[1, 1, 2, 1]					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					Optional[Any]=True					,       __lowerCamelCase						:					int="relu"					,       __lowerCamelCase						:					Dict=3					,       __lowerCamelCase						:					List[str]=None					,       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     parent
         UpperCAmelCase							=     batch_size
         UpperCAmelCase							=     image_size
         UpperCAmelCase							=     num_channels
         UpperCAmelCase							=     embeddings_size
         UpperCAmelCase							=     hidden_sizes
         UpperCAmelCase							=     depths
         UpperCAmelCase							=     is_training
         UpperCAmelCase							=     use_labels
         UpperCAmelCase							=     hidden_act
         UpperCAmelCase							=     num_labels
         UpperCAmelCase							=     scope
         UpperCAmelCase							=     len(__lowerCamelCase       )





      def        _lowercase				(		self						:					List[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         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.num_labels       )

         UpperCAmelCase							=     self.get_config()
         return config, pixel_values, labels





      def        _lowercase				(		self						:					Any       )    ->   Any:






         """simple docstring"""







         return RegNetConfig(
             num_channels=self.num_channels					,       embeddings_size=self.embeddings_size					,       hidden_sizes=self.hidden_sizes					,       depths=self.depths					,       hidden_act=self.hidden_act					,       num_labels=self.num_labels					,       )





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					Dict					,       __lowerCamelCase						:					Dict					,       __lowerCamelCase						:					List[str]       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     TFRegNetModel(config=__lowerCamelCase       )
         UpperCAmelCase							=     model(__lowerCamelCase					,       training=__lowerCamelCase       )
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape					,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,       )





      def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					Optional[Any]					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					Any       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     self.num_labels
         UpperCAmelCase							=     TFRegNetForImageClassification(__lowerCamelCase       )
         UpperCAmelCase							=     model(__lowerCamelCase					,       labels=__lowerCamelCase					,       training=__lowerCamelCase       )
         self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)       )





      def        _lowercase				(		self						:					List[Any]       )    ->   List[str]:






         """simple docstring"""







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






@require_tf
class   __lowercase				(  __snake_case ,					__snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
      UpperCamelCase								=       (
          {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
          if is_tf_available()
          else {}
      )

      UpperCamelCase								=       False
      UpperCamelCase								=       False
      UpperCamelCase								=       False
      UpperCamelCase								=       False
      UpperCamelCase								=       False





      def        _lowercase				(		self						:					Dict       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     TFRegNetModelTester(self       )
         UpperCAmelCase							=     ConfigTester(self					,       config_class=__lowerCamelCase					,       has_text_modality=__lowerCamelCase       )





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         return





      @unittest.skip(reason="""RegNet does not use inputs_embeds"""       )
      def        _lowercase				(		self						:					List[str]       )    ->   Tuple:






         """simple docstring"""







         pass





      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) == 0					,       reason="""TF does not support backprop for grouped convolutions on CPU."""					,       )
      @slow
      def        _lowercase				(		self						:					str       )    ->   List[Any]:






         """simple docstring"""







         super().test_keras_fit()





      @unittest.skip(reason="""RegNet does not support input and output embeddings"""       )
      def        _lowercase				(		self						:					List[str]       )    ->   Tuple:






         """simple docstring"""







         pass





      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase				,  UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase							=     model_class(__lowerCamelCase       )
            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]					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					List[Any]       ):
            UpperCAmelCase							=     model_class(__lowerCamelCase       )
            UpperCAmelCase							=     model(**self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase       )					,       training=__lowerCamelCase       )

            UpperCAmelCase							=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase							=     self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase       )					,       expected_num_stages + 1       )

            # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]       )					,       [self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,       )

         UpperCAmelCase				,  UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase							=     ["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               UpperCAmelCase							=     layer_type
               UpperCAmelCase							=     True
               check_hidden_states_output(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase							=     True

               check_hidden_states_output(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase				,  UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs_for_common()

         def check_equivalence(__lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					Dict					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					List[str]={}       ):
            UpperCAmelCase							=     model(__lowerCamelCase					,       return_dict=__lowerCamelCase					,       **__lowerCamelCase       )
            UpperCAmelCase							=     model(__lowerCamelCase					,       return_dict=__lowerCamelCase					,       **__lowerCamelCase       ).to_tuple()

            def recursive_check(__lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					Dict       ):
               if isinstance(__lowerCamelCase					,       (List, Tuple)       ):
                  for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase					,       __lowerCamelCase       ):
                     recursive_check(__lowerCamelCase					,       __lowerCamelCase       )
               elif tuple_object is None:
                  return
               else:
                  self.assertTrue(
                      all(tf.equal(__lowerCamelCase					,       __lowerCamelCase       )       )					,       msg=(
                          """Tuple and dict output are not equal. Difference:"""
                          F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}"""
                      )					,       )

               recursive_check(__lowerCamelCase					,       __lowerCamelCase       )

         for model_class in self.all_model_classes:
            UpperCAmelCase							=     model_class(__lowerCamelCase       )

            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase       )
            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase       )
            check_equivalence(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase					,       return_labels=__lowerCamelCase       )
            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase					,       return_labels=__lowerCamelCase       )
            check_equivalence(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase       )
            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase       )
            check_equivalence(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       {"""output_hidden_states""": True}       )

            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase					,       return_labels=__lowerCamelCase       )
            UpperCAmelCase							=     self._prepare_for_class(__lowerCamelCase					,       __lowerCamelCase					,       return_labels=__lowerCamelCase       )
            check_equivalence(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       {"""output_hidden_states""": True}       )





      def        _lowercase				(		self						:					str       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase       )





      @slow
      def        _lowercase				(		self						:					List[Any]       )    ->   str:






         """simple docstring"""







         for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase							=     TFRegNetModel.from_pretrained(__lowerCamelCase       )
            self.assertIsNotNone(__lowerCamelCase       )

def   _UpperCamelCase				(						)						->Union[str, Any]:
   UpperCAmelCase							=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image






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





      @cached_property
      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         return (
             AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
             if is_vision_available()
             else None
         )





      @slow
      def        _lowercase				(		self						:					Optional[Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

         UpperCAmelCase							=     self.default_image_processor
         UpperCAmelCase							=     prepare_img()
         UpperCAmelCase							=     image_processor(images=__lowerCamelCase					,       return_tensors="""tf"""       )

         # forward pass
         UpperCAmelCase							=     model(**__lowerCamelCase					,       training=__lowerCamelCase       )

         # verify the logits
         UpperCAmelCase							=     tf.TensorShape((1, 1_0_0_0)       )
         self.assertEqual(outputs.logits.shape					,       __lowerCamelCase       )

         UpperCAmelCase							=     tf.constant([-0.4_180, -1.5_051, -3.4_836]       )

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







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import OwlViTImageProcessor, OwlViTProcessor






@require_vision
class   __lowercase				(  unittest.TestCase  ):





      def        _lowercase				(		self						:					List[Any]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase							=     ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
         # fmt: on
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )
         UpperCAmelCase							=     ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
         UpperCAmelCase							=     {"""unk_token""": """<unk>"""}

         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""vocab_file"""]       )
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""merges_file"""]       )
         with open(self.vocab_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write(json.dumps(__lowerCamelCase       ) + """\n"""       )
         with open(self.merges_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write("""\n""".join(__lowerCamelCase       )       )

         UpperCAmelCase							=     {
             """do_resize""": True,
             """size""": 2_0,
             """do_center_crop""": True,
             """crop_size""": 1_8,
             """do_normalize""": True,
             """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
             """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
         }
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					List[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







         return CLIPTokenizer.from_pretrained(self.tmpdirname					,       pad_token="""!"""					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					List[str]       )    ->   str:






         """simple docstring"""







         return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,       pad_token="""!"""					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Union[str, Any]					,       **__lowerCamelCase						:					Optional[Any]       )    ->   Optional[int]:






         """simple docstring"""







         return OwlViTImageProcessor.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Any       )    ->   List[str]:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					List[Any]       )    ->   Optional[Any]:






         """simple docstring"""







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

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

         return image_inputs





      def        _lowercase				(		self						:					int       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_rust_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor_slow.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(self.tmpdirname					,       use_fast=__lowerCamelCase       )

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor_fast.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()					,       tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()					,       tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()					,       tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer					,       __lowerCamelCase       )
         self.assertIsInstance(processor_fast.tokenizer					,       __lowerCamelCase       )

         self.assertEqual(processor_slow.image_processor.to_json_string()					,       image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()					,       image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor					,       __lowerCamelCase       )
         self.assertIsInstance(processor_fast.image_processor					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					str       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     OwlViTProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         UpperCAmelCase							=     self.get_tokenizer(bos_token="""(BOS)"""					,       eos_token="""(EOS)"""       )
         UpperCAmelCase							=     self.get_image_processor(do_normalize=__lowerCamelCase       )

         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(
             self.tmpdirname					,       bos_token="""(BOS)"""					,       eos_token="""(EOS)"""					,       do_normalize=__lowerCamelCase       )

         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        _lowercase				(		self						:					Any       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase							=     processor(images=__lowerCamelCase					,       return_tensors="""np"""       )

         for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum()					,       input_processor[key].sum()					,       delta=1e-2       )





      def        _lowercase				(		self						:					Optional[int]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """lower newer"""

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       return_tensors="""np"""       )

         UpperCAmelCase							=     tokenizer(__lowerCamelCase					,       return_tensors="""np"""       )

         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key][0].tolist()					,       encoded_processor[key][0].tolist()       )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

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

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

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

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





      def        _lowercase				(		self						:					Optional[int]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     """google/owlvit-base-patch32"""
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(__lowerCamelCase       )

         UpperCAmelCase							=     ["""cat""", """nasa badge"""]
         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     1_6
         self.assertListEqual(list(inputs.keys()       )					,       ["""input_ids""", """attention_mask"""]       )
         self.assertEqual(inputs["""input_ids"""].shape					,       (2, seq_length)       )

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





      def        _lowercase				(		self						:					Any       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     """google/owlvit-base-patch32"""
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(__lowerCamelCase       )

         UpperCAmelCase							=     [["""cat""", """nasa badge"""], ["""person"""]]
         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     1_6
         UpperCAmelCase							=     len(__lowerCamelCase       )
         UpperCAmelCase							=     max([len(__lowerCamelCase       ) for texts in input_texts]       )

         self.assertListEqual(list(inputs.keys()       )					,       ["""input_ids""", """attention_mask"""]       )
         self.assertEqual(inputs["""input_ids"""].shape					,       (batch_size * num_max_text_queries, seq_length)       )

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





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     """google/owlvit-base-patch32"""
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(__lowerCamelCase       )

         UpperCAmelCase							=     ["""cat""", """nasa badge"""]
         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     1_6
         UpperCAmelCase							=     inputs["""input_ids"""]
         UpperCAmelCase							=     [
             [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         ]

         self.assertListEqual(list(inputs.keys()       )					,       ["""input_ids""", """attention_mask"""]       )
         self.assertEqual(inputs["""input_ids"""].shape					,       (2, seq_length)       )
         self.assertListEqual(list(input_ids[0]       )					,       predicted_ids[0]       )
         self.assertListEqual(list(input_ids[1]       )					,       predicted_ids[1]       )





      def        _lowercase				(		self						:					List[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(images=__lowerCamelCase					,       query_images=__lowerCamelCase       )

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

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





      def        _lowercase				(		self						:					Tuple       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

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

         UpperCAmelCase							=     processor.batch_decode(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.batch_decode(__lowerCamelCase       )

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )



